From 7839d3fab1e43ee25e80b9e66895a9f152e95479 Mon Sep 17 00:00:00 2001
From: xiaoruiliu <xiaoruiyliu@gmail.com>
Date: Thu, 19 Sep 2024 22:55:02 -0700
Subject: [PATCH] fix leaking my file path/dep error + add vis_2 changes

---
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diff --git a/docs/eda/eda.html b/docs/eda/eda.html
index 776b993e..3cbc95c7 100644
--- a/docs/eda/eda.html
+++ b/docs/eda/eda.html
@@ -319,7 +319,7 @@ <h2 id="toc-title">Data Cleaning and EDA</h2>
 </header>
 
 
-<div id="a16504e0" class="cell" data-execution_count="1">
+<div id="105f0468" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -384,7 +384,7 @@ <h3 data-number="5.1.1" class="anchored" data-anchor-id="file-formats"><span cla
 <section id="csv" class="level4" data-number="5.1.1.1">
 <h4 data-number="5.1.1.1" class="anchored" data-anchor-id="csv"><span class="header-section-number">5.1.1.1</span> CSV</h4>
 <p>CSVs, which stand for <strong>Comma-Separated Values</strong>, are a common tabular data format. In the past two <code>pandas</code> lectures, we briefly touched on the idea of file format: the way data is encoded in a file for storage. Specifically, our <code>elections</code> and <code>babynames</code> datasets were stored and loaded as CSVs:</p>
-<div id="d2c30105" class="cell" data-execution_count="2">
+<div id="3995615e" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>pd.read_csv(<span class="st">"data/elections.csv"</span>).head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="2">
 <div>
@@ -455,7 +455,7 @@ <h4 data-number="5.1.1.1" class="anchored" data-anchor-id="csv"><span class="hea
 </div>
 </div>
 <p>To better understand the properties of a CSV, let’s take a look at the first few rows of the raw data file to see what it looks like before being loaded into a <code>DataFrame</code>. We’ll use the <code>repr()</code> function to return the raw string with its special characters:</p>
-<div id="48b95827" class="cell" data-execution_count="3">
+<div id="a3c9e37d" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"data/elections.csv"</span>, <span class="st">"r"</span>) <span class="im">as</span> table:</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    i <span class="op">=</span> <span class="dv">0</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> row <span class="kw">in</span> table:</span>
@@ -476,7 +476,7 @@ <h4 data-number="5.1.1.1" class="anchored" data-anchor-id="csv"><span class="hea
 <h4 data-number="5.1.1.2" class="anchored" data-anchor-id="tsv"><span class="header-section-number">5.1.1.2</span> TSV</h4>
 <p>Another common file type is <strong>TSV (Tab-Separated Values)</strong>. In a TSV, records are still delimited by a newline <code>\n</code>, while fields are delimited by <code>\t</code> tab character.</p>
 <p>Let’s check out the first few rows of the raw TSV file. Again, we’ll use the <code>repr()</code> function so that <code>print</code> shows the special characters.</p>
-<div id="c7191cce" class="cell" data-execution_count="4">
+<div id="0b7fdb5e" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"data/elections.txt"</span>, <span class="st">"r"</span>) <span class="im">as</span> table:</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    i <span class="op">=</span> <span class="dv">0</span></span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> row <span class="kw">in</span> table:</span>
@@ -492,7 +492,7 @@ <h4 data-number="5.1.1.2" class="anchored" data-anchor-id="tsv"><span class="hea
 </div>
 </div>
 <p>TSVs can be loaded into <code>pandas</code> using <code>pd.read_csv</code>. We’ll need to specify the <strong>delimiter</strong> with parameter<code>sep='\t'</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">(documentation)</a>.</p>
-<div id="419b6d1d" class="cell" data-execution_count="5">
+<div id="411f85cb" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>pd.read_csv(<span class="st">"data/elections.txt"</span>, sep<span class="op">=</span><span class="st">'</span><span class="ch">\t</span><span class="st">'</span>).head(<span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="5">
 <div>
@@ -549,7 +549,7 @@ <h4 data-number="5.1.1.2" class="anchored" data-anchor-id="tsv"><span class="hea
 <section id="json" class="level4" data-number="5.1.1.3">
 <h4 data-number="5.1.1.3" class="anchored" data-anchor-id="json"><span class="header-section-number">5.1.1.3</span> JSON</h4>
 <p><strong>JSON (JavaScript Object Notation)</strong> files behave similarly to Python dictionaries. A raw JSON is shown below.</p>
-<div id="93e178e5" class="cell" data-execution_count="6">
+<div id="150b9bb6" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"data/elections.json"</span>, <span class="st">"r"</span>) <span class="im">as</span> table:</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    i <span class="op">=</span> <span class="dv">0</span></span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> row <span class="kw">in</span> table:</span>
@@ -579,7 +579,7 @@ <h4 data-number="5.1.1.3" class="anchored" data-anchor-id="json"><span class="he
 </div>
 </div>
 <p>JSON files can be loaded into <code>pandas</code> using <code>pd.read_json</code>.</p>
-<div id="79c8be4d" class="cell" data-execution_count="7">
+<div id="35b0ba4f" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>pd.read_json(<span class="st">'data/elections.json'</span>).head(<span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <div>
@@ -634,7 +634,7 @@ <h4 data-number="5.1.1.3" class="anchored" data-anchor-id="json"><span class="he
 <section id="eda-with-json-berkeley-covid-19-data" class="level5" data-number="5.1.1.3.1">
 <h5 data-number="5.1.1.3.1" class="anchored" data-anchor-id="eda-with-json-berkeley-covid-19-data"><span class="header-section-number">5.1.1.3.1</span> EDA with JSON: Berkeley COVID-19 Data</h5>
 <p>The City of Berkeley Open Data <a href="https://data.cityofberkeley.info/Health/COVID-19-Confirmed-Cases/xn6j-b766">website</a> has a dataset with COVID-19 Confirmed Cases among Berkeley residents by date. Let’s download the file and save it as a JSON (note the source URL file type is also a JSON). In the interest of reproducible data science, we will download the data programatically. We have defined some helper functions in the <a href="https://ds100.org/fa23/resources/assets/lectures/lec05/lec05-eda.html"><code>ds100_utils.py</code></a> file that we can reuse these helper functions in many different notebooks.</p>
-<div id="f62ba6a7" class="cell" data-execution_count="8">
+<div id="b2ef2591" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> ds100_utils <span class="im">import</span> fetch_and_cache</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>covid_file <span class="op">=</span> fetch_and_cache(</span>
@@ -653,7 +653,7 @@ <h5 data-number="5.1.1.3.1" class="anchored" data-anchor-id="eda-with-json-berke
 <h6 data-number="5.1.1.3.1.1" class="anchored" data-anchor-id="file-size"><span class="header-section-number">5.1.1.3.1.1</span> File Size</h6>
 <p>Let’s start our analysis by getting a rough estimate of the size of the dataset to inform the tools we use to view the data. For relatively small datasets, we can use a text editor or spreadsheet. For larger datasets, more programmatic exploration or distributed computing tools may be more fitting. Here we will use <code>Python</code> tools to probe the file.</p>
 <p>Since there seem to be text files, let’s investigate the number of lines, which often corresponds to the number of records</p>
-<div id="5b9d5e47" class="cell" data-execution_count="9">
+<div id="79de35a9" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(covid_file, <span class="st">"is"</span>, os.path.getsize(covid_file) <span class="op">/</span> <span class="fl">1e6</span>, <span class="st">"MB"</span>)</span>
@@ -671,7 +671,7 @@ <h6 data-number="5.1.1.3.1.2" class="anchored" data-anchor-id="unix-commands"><s
 <p>As part of the EDA workflow, Unix commands can come in very handy. In fact, there’s an entire book called <a href="https://datascienceatthecommandline.com/">“Data Science at the Command Line”</a> that explores this idea in depth! In Jupyter/IPython, you can prefix lines with <code>!</code> to execute arbitrary Unix commands, and within those lines, you can refer to Python variables and expressions with the syntax <code>{expr}</code>.</p>
 <p>Here, we use the <code>ls</code> command to list files, using the <code>-lh</code> flags, which request “long format with information in human-readable form.” We also use the <code>wc</code> command for “word count,” but with the <code>-l</code> flag, which asks for line counts instead of words.</p>
 <p>These two give us the same information as the code above, albeit in a slightly different form:</p>
-<div id="70ba3576" class="cell" data-execution_count="10">
+<div id="9b3aeceb" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>ls <span class="op">-</span>lh {covid_file}</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>wc <span class="op">-</span>l {covid_file}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -683,7 +683,7 @@ <h6 data-number="5.1.1.3.1.2" class="anchored" data-anchor-id="unix-commands"><s
 <section id="file-contents" class="level6" data-number="5.1.1.3.1.3">
 <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><span class="header-section-number">5.1.1.3.1.3</span> File Contents</h6>
 <p>Let’s explore the data format using <code>Python</code>.</p>
-<div id="456da41e" class="cell" data-execution_count="11">
+<div id="e9c7a110" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(covid_file, <span class="st">"r"</span>) <span class="im">as</span> f:</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> i, row <span class="kw">in</span> <span class="bu">enumerate</span>(f):</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>        <span class="bu">print</span>(<span class="bu">repr</span>(row)) <span class="co"># print raw strings</span></span>
@@ -697,7 +697,7 @@ <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><s
 </div>
 </div>
 <p>We can use the <code>head</code> Unix command (which is where <code>pandas</code>’ <code>head</code> method comes from!) to see the first few lines of the file:</p>
-<div id="0feae6b0" class="cell" data-execution_count="12">
+<div id="5c9fd02e" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>head <span class="op">-</span><span class="dv">5</span> {covid_file}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>{
@@ -708,21 +708,21 @@ <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><s
 </div>
 </div>
 <p>In order to load the JSON file into <code>pandas</code>, Let’s first do some EDA with Oython’s <code>json</code> package to understand the particular structure of this JSON file so that we can decide what (if anything) to load into <code>pandas</code>. Python has relatively good support for JSON data since it closely matches the internal python object model. In the following cell we import the entire JSON datafile into a python dictionary using the <code>json</code> package.</p>
-<div id="483396d4" class="cell" data-execution_count="13">
+<div id="93c0c260" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> json</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(covid_file, <span class="st">"rb"</span>) <span class="im">as</span> f:</span>
 <span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>    covid_json <span class="op">=</span> json.load(f)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The <code>covid_json</code> variable is now a dictionary encoding the data in the file:</p>
-<div id="fb9d8c77" class="cell" data-execution_count="14">
+<div id="8fd9ef90" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(covid_json)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
 <pre><code>dict</code></pre>
 </div>
 </div>
 <p>We can examine what keys are in the top level JSON object by listing out the keys.</p>
-<div id="fb0813b1" class="cell" data-execution_count="15">
+<div id="c6d53974" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>covid_json.keys()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="15">
 <pre><code>dict_keys(['meta', 'data'])</code></pre>
@@ -730,14 +730,14 @@ <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><s
 </div>
 <p><strong>Observation</strong>: The JSON dictionary contains a <code>meta</code> key which likely refers to metadata (data about the data). Metadata is often maintained with the data and can be a good source of additional information.</p>
 <p>We can investigate the metadata further by examining the keys associated with the metadata.</p>
-<div id="b8a32afc" class="cell" data-execution_count="16">
+<div id="f4139d5c" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>covid_json[<span class="st">'meta'</span>].keys()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
 <pre><code>dict_keys(['view'])</code></pre>
 </div>
 </div>
 <p>The <code>meta</code> key contains another dictionary called <code>view</code>. This likely refers to metadata about a particular “view” of some underlying database. We will learn more about views when we study SQL later in the class.</p>
-<div id="7b049f9b" class="cell" data-execution_count="17">
+<div id="e45e0a74" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>covid_json[<span class="st">'meta'</span>][<span class="st">'view'</span>].keys()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
 <pre><code>dict_keys(['id', 'name', 'assetType', 'attribution', 'averageRating', 'category', 'createdAt', 'description', 'displayType', 'downloadCount', 'hideFromCatalog', 'hideFromDataJson', 'newBackend', 'numberOfComments', 'oid', 'provenance', 'publicationAppendEnabled', 'publicationDate', 'publicationGroup', 'publicationStage', 'rowsUpdatedAt', 'rowsUpdatedBy', 'tableId', 'totalTimesRated', 'viewCount', 'viewLastModified', 'viewType', 'approvals', 'columns', 'grants', 'metadata', 'owner', 'query', 'rights', 'tableAuthor', 'tags', 'flags'])</code></pre>
@@ -757,7 +757,7 @@ <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><s
     | -&gt; columns
     ...</code></pre>
 <p>There is a key called description in the view sub dictionary. This likely contains a description of the data:</p>
-<div id="acef1425" class="cell" data-execution_count="18">
+<div id="c838f92a" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(covid_json[<span class="st">'meta'</span>][<span class="st">'view'</span>][<span class="st">'description'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Counts of confirmed COVID-19 cases among Berkeley residents by date.</code></pre>
@@ -767,7 +767,7 @@ <h6 data-number="5.1.1.3.1.3" class="anchored" data-anchor-id="file-contents"><s
 <section id="examining-the-data-field-for-records" class="level6" data-number="5.1.1.3.1.4">
 <h6 data-number="5.1.1.3.1.4" class="anchored" data-anchor-id="examining-the-data-field-for-records"><span class="header-section-number">5.1.1.3.1.4</span> Examining the Data Field for Records</h6>
 <p>We can look at a few entries in the <code>data</code> field. This is what we’ll load into <code>pandas</code>.</p>
-<div id="4e1d4bb0" class="cell" data-execution_count="19">
+<div id="8c043794" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">3</span>):</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="ss">f"</span><span class="sc">{</span>i<span class="sc">:03}</span><span class="ss"> | </span><span class="sc">{</span>covid_json[<span class="st">'data'</span>][i]<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -778,7 +778,7 @@ <h6 data-number="5.1.1.3.1.4" class="anchored" data-anchor-id="examining-the-dat
 </div>
 <p>Observations: * These look like equal-length records, so maybe <code>data</code> is a table! * But what do each of values in the record mean? Where can we find column headers?</p>
 <p>For that, we’ll need the <code>columns</code> key in the metadata dictionary. This returns a list:</p>
-<div id="eb015490" class="cell" data-execution_count="20">
+<div id="fcff3a52" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(covid_json[<span class="st">'meta'</span>][<span class="st">'view'</span>][<span class="st">'columns'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="20">
 <pre><code>list</code></pre>
@@ -805,7 +805,7 @@ <h6 data-number="5.1.1.3.1.6" class="anchored" data-anchor-id="loading-covid-dat
 <li><p>Remove columns that have no metadata description. This would be a bad idea in general, but here we remove these columns since the above analysis suggests they are unlikely to contain useful information.</p></li>
 <li><p>Examine the <code>tail</code> of the table.</p></li>
 </ol>
-<div id="2a18a6a6" class="cell" data-execution_count="21">
+<div id="1a5f35c4" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the data from JSON and assign column titles</span></span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>covid <span class="op">=</span> pd.DataFrame(</span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>    covid_json[<span class="st">'data'</span>],</span>
@@ -918,7 +918,7 @@ <h6 data-number="5.1.1.3.1.6" class="anchored" data-anchor-id="loading-covid-dat
 <h3 data-number="5.1.2" class="anchored" data-anchor-id="primary-and-foreign-keys"><span class="header-section-number">5.1.2</span> Primary and Foreign Keys</h3>
 <p>Last time, we introduced <code>.merge</code> as the <code>pandas</code> method for joining multiple <code>DataFrame</code>s together. In our discussion of joins, we touched on the idea of using a “key” to determine what rows should be merged from each table. Let’s take a moment to examine this idea more closely.</p>
 <p>The <strong>primary key</strong> is the column or set of columns in a table that <em>uniquely</em> determine the values of the remaining columns. It can be thought of as the unique identifier for each individual row in the table. For example, a table of Data 100 students might use each student’s Cal ID as the primary key.</p>
-<div id="77f8ac75" class="cell" data-execution_count="22">
+<div id="3acbbd47" class="cell" data-execution_count="22">
 <div class="cell-output cell-output-display" data-execution_count="22">
 <div>
 
@@ -964,7 +964,7 @@ <h3 data-number="5.1.2" class="anchored" data-anchor-id="primary-and-foreign-key
 </div>
 </div>
 <p>The <strong>foreign key</strong> is the column or set of columns in a table that reference primary keys in other tables. Knowing a dataset’s foreign keys can be useful when assigning the <code>left_on</code> and <code>right_on</code> parameters of <code>.merge</code>. In the table of office hour tickets below, <code>"Cal ID"</code> is a foreign key referencing the previous table.</p>
-<div id="c1ffb110" class="cell" data-execution_count="23">
+<div id="02ad9c35" class="cell" data-execution_count="23">
 <div class="cell-output cell-output-display" data-execution_count="23">
 <div>
 
@@ -1057,7 +1057,7 @@ <h3 data-number="5.2.3" class="anchored" data-anchor-id="temporality"><span clas
 <section id="temporality-with-pandas-dt-accessors" class="level4" data-number="5.2.3.1">
 <h4 data-number="5.2.3.1" class="anchored" data-anchor-id="temporality-with-pandas-dt-accessors"><span class="header-section-number">5.2.3.1</span> Temporality with <code>pandas</code>’ <code>dt</code> accessors</h4>
 <p>Let’s briefly look at how we can use <code>pandas</code>’ <code>dt</code> accessors to work with dates/times in a dataset using the dataset you’ll see in Lab 3: the Berkeley PD Calls for Service dataset.</p>
-<div id="a834e200" class="cell" data-execution_count="24">
+<div id="4369e55b" class="cell" data-execution_count="24">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>calls <span class="op">=</span> pd.read_csv(<span class="st">"data/Berkeley_PD_-_Calls_for_Service.csv"</span>)</span>
@@ -1164,11 +1164,11 @@ <h4 data-number="5.2.3.1" class="anchored" data-anchor-id="temporality-with-pand
 <p>Looks like there are three columns with dates/times: <code>EVENTDT</code>, <code>EVENTTM</code>, and <code>InDbDate</code>.</p>
 <p>Most likely, <code>EVENTDT</code> stands for the date when the event took place, <code>EVENTTM</code> stands for the time of day the event took place (in 24-hr format), and <code>InDbDate</code> is the date this call is recorded onto the database.</p>
 <p>If we check the data type of these columns, we will see they are stored as strings. We can convert them to <code>datetime</code> objects using pandas <code>to_datetime</code> function.</p>
-<div id="e5aef10b" class="cell" data-execution_count="25">
+<div id="101bc09b" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>calls[<span class="st">"EVENTDT"</span>] <span class="op">=</span> pd.to_datetime(calls[<span class="st">"EVENTDT"</span>])</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>calls.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94394/874729699.py:1: UserWarning:
+<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19259/874729699.py:1: UserWarning:
 
 Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
 </code></pre>
@@ -1273,7 +1273,7 @@ <h4 data-number="5.2.3.1" class="anchored" data-anchor-id="temporality-with-pand
 </div>
 <p>Now, we can use the <code>dt</code> accessor on this column.</p>
 <p>We can get the month:</p>
-<div id="e256b6e1" class="cell" data-execution_count="26">
+<div id="c0df7213" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>calls[<span class="st">"EVENTDT"</span>].dt.month.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
 <pre><code>0    4
@@ -1285,7 +1285,7 @@ <h4 data-number="5.2.3.1" class="anchored" data-anchor-id="temporality-with-pand
 </div>
 </div>
 <p>Which day of the week the date is on:</p>
-<div id="4191cbf2" class="cell" data-execution_count="27">
+<div id="e1b07fa6" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>calls[<span class="st">"EVENTDT"</span>].dt.dayofweek.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
 <pre><code>0    3
@@ -1297,7 +1297,7 @@ <h4 data-number="5.2.3.1" class="anchored" data-anchor-id="temporality-with-pand
 </div>
 </div>
 <p>Check the mimimum values to see if there are any suspicious-looking, 70s dates:</p>
-<div id="9a927c33" class="cell" data-execution_count="28">
+<div id="f40feb60" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>calls.sort_values(<span class="st">"EVENTDT"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="28">
 <div>
@@ -1444,7 +1444,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="csvs-and-field-names"><
 <p>We can then explore the CSV (which is a text file, and does not contain binary-encoded data) in many ways: 1. Using a text editor like emacs, vim, VSCode, etc. 2. Opening the CSV directly in DataHub (read-only), Excel, Google Sheets, etc. 3. The <code>Python</code> file object 4. <code>pandas</code>, using <code>pd.read_csv()</code></p>
 <p>To try out options 1 and 2, you can view or download the Tuberculosis from the <a href="https://data100.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FDS-100%2Ffa23-student&amp;urlpath=lab%2Ftree%2Ffa23-student%2Flecture%2Flec05%2Flec04-eda.ipynb&amp;branch=main">lecture demo notebook</a> under the <code>data</code> folder in the left hand menu. Notice how the CSV file is a type of <strong>rectangular data (i.e., tabular data) stored as comma-separated values</strong>.</p>
 <p>Next, let’s try out option 3 using the <code>Python</code> file object. We’ll look at the first four lines:</p>
-<div id="e9054833" class="cell" data-execution_count="29">
+<div id="9b6093d0" class="cell" data-execution_count="29">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"data/cdc_tuberculosis.csv"</span>, <span class="st">"r"</span>) <span class="im">as</span> f:</span>
@@ -1469,7 +1469,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="csvs-and-field-names"><
 <p>Whoa, why are there blank lines interspaced between the lines of the CSV?</p>
 <p>You may recall that all line breaks in text files are encoded as the special newline character <code>\n</code>. Python’s <code>print()</code> prints each string (including the newline), and an additional newline on top of that.</p>
 <p>If you’re curious, we can use the <code>repr()</code> function to return the raw string with all special characters:</p>
-<div id="a2098c9d" class="cell" data-execution_count="30">
+<div id="9a584b49" class="cell" data-execution_count="30">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">"data/cdc_tuberculosis.csv"</span>, <span class="st">"r"</span>) <span class="im">as</span> f:</span>
@@ -1488,7 +1488,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="csvs-and-field-names"><
 </div>
 </div>
 <p>Finally, let’s try option 4 and use the tried-and-true Data 100 approach: <code>pandas</code>.</p>
-<div id="b74a5b9b" class="cell" data-execution_count="31">
+<div id="8d0bb92b" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>tb_df <span class="op">=</span> pd.read_csv(<span class="st">"data/cdc_tuberculosis.csv"</span>)</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a>tb_df.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
@@ -1568,7 +1568,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="csvs-and-field-names"><
 <p>You may notice some strange things about this table: what’s up with the “Unnamed” column names and the first row?</p>
 <p>Congratulations — you’re ready to wrangle your data! Because of how things are stored, we’ll need to clean the data a bit to name our columns better.</p>
 <p>A reasonable first step is to identify the row with the right header. The <code>pd.read_csv()</code> function (<a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">documentation</a>) has the convenient <code>header</code> parameter that we can set to use the elements in row 1 as the appropriate columns:</p>
-<div id="78bb24ee" class="cell" data-execution_count="32">
+<div id="8f6df9c8" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>tb_df <span class="op">=</span> pd.read_csv(<span class="st">"data/cdc_tuberculosis.csv"</span>, header<span class="op">=</span><span class="dv">1</span>) <span class="co"># row index</span></span>
 <span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a>tb_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
@@ -1647,7 +1647,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="csvs-and-field-names"><
 </div>
 <p>Wait…but now we can’t differentiate betwen the “Number of TB cases” and “TB incidence” year columns. <code>pandas</code> has tried to make our lives easier by automatically adding “.1” to the latter columns, but this doesn’t help us, as humans, understand the data.</p>
 <p>We can do this manually with <code>df.rename()</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename.html?highlight=rename#pandas.DataFrame.rename">documentation</a>):</p>
-<div id="b9c76baf" class="cell" data-execution_count="33">
+<div id="01cce733" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>rename_dict <span class="op">=</span> {<span class="st">'2019'</span>: <span class="st">'TB cases 2019'</span>,</span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a>               <span class="st">'2020'</span>: <span class="st">'TB cases 2020'</span>,</span>
 <span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>               <span class="st">'2021'</span>: <span class="st">'TB cases 2021'</span>,</span>
@@ -1737,7 +1737,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 <p>Row 0 is what we call a <strong>rollup record</strong>, or summary record. It’s often useful when displaying tables to humans. The <strong>granularity</strong> of record 0 (Totals) vs the rest of the records (States) is different.</p>
 <p>Okay, EDA step two. How was the rollup record aggregated?</p>
 <p>Let’s check if Total TB cases is the sum of all state TB cases. If we sum over all rows, we should get <strong>2x</strong> the total cases in each of our TB cases by year (why do you think this is?).</p>
-<div id="59f62178" class="cell" data-execution_count="34">
+<div id="633f0363" class="cell" data-execution_count="34">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>tb_df.<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">0</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1754,7 +1754,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 </div>
 </div>
 <p>Whoa, what’s going on with the TB cases in 2019, 2020, and 2021? Check out the column types:</p>
-<div id="d0457f5e" class="cell" data-execution_count="35">
+<div id="c173653b" class="cell" data-execution_count="35">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb56"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>tb_df.dtypes</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1772,7 +1772,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 </div>
 <p>Since there are commas in the values for TB cases, the numbers are read as the <code>object</code> datatype, or <strong>storage type</strong> (close to the <code>Python</code> string datatype), so <code>pandas</code> is concatenating strings instead of adding integers (recall that Python can “sum”, or concatenate, strings together: <code>"data" + "100"</code> evaluates to <code>"data100"</code>).</p>
 <p>Fortunately <code>read_csv</code> also has a <code>thousands</code> parameter (<a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">documentation</a>):</p>
-<div id="0f23a572" class="cell" data-execution_count="36">
+<div id="e304af5d" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a><span class="co"># improve readability: chaining method calls with outer parentheses/line breaks</span></span>
 <span id="cb58-2"><a href="#cb58-2" aria-hidden="true" tabindex="-1"></a>tb_df <span class="op">=</span> (</span>
 <span id="cb58-3"><a href="#cb58-3" aria-hidden="true" tabindex="-1"></a>    pd.read_csv(<span class="st">"data/cdc_tuberculosis.csv"</span>, header<span class="op">=</span><span class="dv">1</span>, thousands<span class="op">=</span><span class="st">','</span>)</span>
@@ -1853,7 +1853,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 </div>
 </div>
 </div>
-<div id="df66a6bb" class="cell" data-execution_count="37">
+<div id="155ddd14" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb59"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a>tb_df.<span class="bu">sum</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
 <pre><code>U.S. jurisdiction    TotalAlabamaAlaskaArizonaArkansasCaliforniaCol...
@@ -1868,7 +1868,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 </div>
 <p>The total TB cases look right. Phew!</p>
 <p>Let’s just look at the records with <strong>state-level granularity</strong>:</p>
-<div id="7b4f4ea8" class="cell" data-execution_count="38">
+<div id="e8e0e50a" class="cell" data-execution_count="38">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>state_tb_df <span class="op">=</span> tb_df[<span class="dv">1</span>:]</span>
@@ -1953,7 +1953,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="record-granularity"><sp
 <h3 data-number="5.4.3" class="anchored" data-anchor-id="gather-census-data"><span class="header-section-number">5.4.3</span> Gather Census Data</h3>
 <p>U.S. Census population estimates <a href="https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-total.html">source</a> (2019), <a href="https://www.census.gov/data/tables/time-series/demo/popest/2020s-state-total.html">source</a> (2020-2021).</p>
 <p>Running the below cells cleans the data. There are a few new methods here: * <code>df.convert_dtypes()</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.convert_dtypes.html">documentation</a>) conveniently converts all float dtypes into ints and is out of scope for the class. * <code>df.drop_na()</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html">documentation</a>) will be explained in more detail next time.</p>
-<div id="0ad655ff" class="cell" data-execution_count="39">
+<div id="9e7a324b" class="cell" data-execution_count="39">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb62"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb62-1"><a href="#cb62-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 2010s census data</span></span>
@@ -2077,7 +2077,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="gather-census-data"><sp
 <p>or use <code>iPython</code> magic which will intelligently import code when files change:</p>
 <div class="sourceCode" id="cb64"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext autoreload</span>
 <span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>autoreload <span class="dv">2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div id="ed201034" class="cell" data-execution_count="40">
+<div id="77b3b9e0" class="cell" data-execution_count="40">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a><span class="co"># census 2020s data</span></span>
@@ -2154,7 +2154,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="gather-census-data"><sp
 <section id="joining-data-merging-dataframes" class="level3" data-number="5.4.4">
 <h3 data-number="5.4.4" class="anchored" data-anchor-id="joining-data-merging-dataframes"><span class="header-section-number">5.4.4</span> Joining Data (Merging <code>DataFrame</code>s)</h3>
 <p>Time to <code>merge</code>! Here we use the <code>DataFrame</code> method <code>df1.merge(right=df2, ...)</code> on <code>DataFrame</code> <code>df1</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html">documentation</a>). Contrast this with the function <code>pd.merge(left=df1, right=df2, ...)</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.merge.html?highlight=pandas%20merge#pandas.merge">documentation</a>). Feel free to use either.</p>
-<div id="b5e7d4dd" class="cell" data-execution_count="41">
+<div id="4f9d5439" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb66"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a><span class="co"># merge TB DataFrame with two US census DataFrames</span></span>
 <span id="cb66-2"><a href="#cb66-2" aria-hidden="true" tabindex="-1"></a>tb_census_df <span class="op">=</span> (</span>
 <span id="cb66-3"><a href="#cb66-3" aria-hidden="true" tabindex="-1"></a>    tb_df</span>
@@ -2329,7 +2329,7 @@ <h3 data-number="5.4.4" class="anchored" data-anchor-id="joining-data-merging-da
 </div>
 </div>
 <p>Having all of these columns is a little unwieldy. We could either drop the unneeded columns now, or just merge on smaller census <code>DataFrame</code>s. Let’s do the latter.</p>
-<div id="3d72c739" class="cell" data-execution_count="42">
+<div id="3891a5b2" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a><span class="co"># try merging again, but cleaner this time</span></span>
 <span id="cb67-2"><a href="#cb67-2" aria-hidden="true" tabindex="-1"></a>tb_census_df <span class="op">=</span> (</span>
 <span id="cb67-3"><a href="#cb67-3" aria-hidden="true" tabindex="-1"></a>    tb_df</span>
@@ -2442,7 +2442,7 @@ <h3 data-number="5.4.5" class="anchored" data-anchor-id="reproducing-data-comput
 <p><span class="math display">\[\text{TB incidence} = \frac{\text{TB cases in population}}{\text{groups in population}} = \frac{\text{TB cases in population}}{\text{population}/100000} \]</span></p>
 <p><span class="math display">\[= \frac{\text{TB cases in population}}{\text{population}} \times 100000\]</span></p>
 <p>Let’s try this for 2019:</p>
-<div id="bbdcc842" class="cell" data-execution_count="43">
+<div id="4bfa537b" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb68"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a>tb_census_df[<span class="st">"recompute incidence 2019"</span>] <span class="op">=</span> tb_census_df[<span class="st">"TB cases 2019"</span>]<span class="op">/</span>tb_census_df[<span class="st">"2019"</span>]<span class="op">*</span><span class="dv">100000</span></span>
 <span id="cb68-2"><a href="#cb68-2" aria-hidden="true" tabindex="-1"></a>tb_census_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="43">
@@ -2545,7 +2545,7 @@ <h3 data-number="5.4.5" class="anchored" data-anchor-id="reproducing-data-comput
 </div>
 <p>Awesome!!!</p>
 <p>Let’s use a for-loop and Python format strings to compute TB incidence for all years. Python f-strings are just used for the purposes of this demo, but they’re handy to know when you explore data beyond this course (<a href="https://docs.python.org/3/tutorial/inputoutput.html">documentation</a>).</p>
-<div id="ceb99984" class="cell" data-execution_count="44">
+<div id="89eee523" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb69"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb69-1"><a href="#cb69-1" aria-hidden="true" tabindex="-1"></a><span class="co"># recompute incidence for all years</span></span>
 <span id="cb69-2"><a href="#cb69-2" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> year <span class="kw">in</span> [<span class="dv">2019</span>, <span class="dv">2020</span>, <span class="dv">2021</span>]:</span>
 <span id="cb69-3"><a href="#cb69-3" aria-hidden="true" tabindex="-1"></a>    tb_census_df[<span class="ss">f"recompute incidence </span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>] <span class="op">=</span> tb_census_df[<span class="ss">f"TB cases </span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>]<span class="op">/</span>tb_census_df[<span class="ss">f"</span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>]<span class="op">*</span><span class="dv">100000</span></span>
@@ -2661,7 +2661,7 @@ <h3 data-number="5.4.5" class="anchored" data-anchor-id="reproducing-data-comput
 </div>
 </div>
 <p>These numbers look pretty close!!! There are a few errors in the hundredths place, particularly in 2021. It may be useful to further explore reasons behind this discrepancy.</p>
-<div id="26718e1d" class="cell" data-execution_count="45">
+<div id="e9fb3d2d" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb70"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb70-1"><a href="#cb70-1" aria-hidden="true" tabindex="-1"></a>tb_census_df.describe()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
 <div>
@@ -2822,7 +2822,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 <p>This is TB incidence computed across the entire U.S. population! How do we reproduce this? * We need to reproduce the “Total” TB incidences in our rolled record. * But our current <code>tb_census_df</code> only has 51 entries (50 states plus Washington, D.C.). There is no rolled record. * What happened…?</p>
 <p>Let’s get exploring!</p>
 <p>Before we keep exploring, we’ll set all indexes to more meaningful values, instead of just numbers that pertain to some row at some point. This will make our cleaning slightly easier.</p>
-<div id="f9b89da1" class="cell" data-execution_count="46">
+<div id="8a28ef2f" class="cell" data-execution_count="46">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb71"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb71-1"><a href="#cb71-1" aria-hidden="true" tabindex="-1"></a>tb_df <span class="op">=</span> tb_df.set_index(<span class="st">"U.S. jurisdiction"</span>)</span>
@@ -2905,7 +2905,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 </div>
 </div>
-<div id="c9f4a55d" class="cell" data-execution_count="47">
+<div id="1268ec84" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb72"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb72-1"><a href="#cb72-1" aria-hidden="true" tabindex="-1"></a>census_2010s_df <span class="op">=</span> census_2010s_df.set_index(<span class="st">"Geographic Area"</span>)</span>
 <span id="cb72-2"><a href="#cb72-2" aria-hidden="true" tabindex="-1"></a>census_2010s_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="47">
@@ -3013,7 +3013,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 </div>
 </div>
-<div id="2e882a95" class="cell" data-execution_count="48">
+<div id="c4909bfc" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb73"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb73-1"><a href="#cb73-1" aria-hidden="true" tabindex="-1"></a>census_2020s_df <span class="op">=</span> census_2020s_df.set_index(<span class="st">"Geographic Area"</span>)</span>
 <span id="cb73-2"><a href="#cb73-2" aria-hidden="true" tabindex="-1"></a>census_2020s_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="48">
@@ -3073,7 +3073,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 </div>
 <p>It turns out that our merge above only kept state records, even though our original <code>tb_df</code> had the “Total” rolled record:</p>
-<div id="3f8942e6" class="cell" data-execution_count="49">
+<div id="785e96ac" class="cell" data-execution_count="49">
 <div class="sourceCode cell-code" id="cb74"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb74-1"><a href="#cb74-1" aria-hidden="true" tabindex="-1"></a>tb_df.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="49">
 <div>
@@ -3154,7 +3154,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 <p>Recall that <code>merge</code> by default does an <strong>inner</strong> merge by default, meaning that it only preserves keys that are present in <strong>both</strong> <code>DataFrame</code>s.</p>
 <p>The rolled records in our census <code>DataFrame</code> have different <code>Geographic Area</code> fields, which was the key we merged on:</p>
-<div id="7efba35e" class="cell" data-execution_count="50">
+<div id="e94c3b47" class="cell" data-execution_count="50">
 <div class="sourceCode cell-code" id="cb75"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb75-1"><a href="#cb75-1" aria-hidden="true" tabindex="-1"></a>census_2010s_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="50">
 <div>
@@ -3263,7 +3263,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 <p>The Census <code>DataFrame</code> has several rolled records. The aggregate record we are looking for actually has the Geographic Area named “United States”.</p>
 <p>One straightforward way to get the right merge is to rename the value itself. Because we now have the Geographic Area index, we’ll use <code>df.rename()</code> (<a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rename.html">documentation</a>):</p>
-<div id="798098a9" class="cell" data-execution_count="51">
+<div id="066bc34c" class="cell" data-execution_count="51">
 <div class="sourceCode cell-code" id="cb76"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb76-1"><a href="#cb76-1" aria-hidden="true" tabindex="-1"></a><span class="co"># rename rolled record for 2010s</span></span>
 <span id="cb76-2"><a href="#cb76-2" aria-hidden="true" tabindex="-1"></a>census_2010s_df.rename(index<span class="op">=</span>{<span class="st">'United States'</span>:<span class="st">'Total'</span>}, inplace<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb76-3"><a href="#cb76-3" aria-hidden="true" tabindex="-1"></a>census_2010s_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3372,7 +3372,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 </div>
 </div>
-<div id="53155249" class="cell" data-execution_count="52">
+<div id="c9cd4f9e" class="cell" data-execution_count="52">
 <div class="sourceCode cell-code" id="cb77"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb77-1"><a href="#cb77-1" aria-hidden="true" tabindex="-1"></a><span class="co"># same, but for 2020s rename rolled record</span></span>
 <span id="cb77-2"><a href="#cb77-2" aria-hidden="true" tabindex="-1"></a>census_2020s_df.rename(index<span class="op">=</span>{<span class="st">'United States'</span>:<span class="st">'Total'</span>}, inplace<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb77-3"><a href="#cb77-3" aria-hidden="true" tabindex="-1"></a>census_2020s_df.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3434,7 +3434,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 <p><br></p>
 <p>Next let’s rerun our merge. Note the different chaining, because we are now merging on indexes (<code>df.merge()</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html">documentation</a>).</p>
-<div id="1d249250" class="cell" data-execution_count="53">
+<div id="8b9238dd" class="cell" data-execution_count="53">
 <div class="sourceCode cell-code" id="cb78"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb78-1"><a href="#cb78-1" aria-hidden="true" tabindex="-1"></a>tb_census_df <span class="op">=</span> (</span>
 <span id="cb78-2"><a href="#cb78-2" aria-hidden="true" tabindex="-1"></a>    tb_df</span>
 <span id="cb78-3"><a href="#cb78-3" aria-hidden="true" tabindex="-1"></a>    .merge(right<span class="op">=</span>census_2010s_df[[<span class="st">"2019"</span>]],</span>
@@ -3531,7 +3531,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 </div>
 <p><br></p>
 <p>Finally, let’s recompute our incidences:</p>
-<div id="10b9078b" class="cell" data-execution_count="54">
+<div id="a8cfc67a" class="cell" data-execution_count="54">
 <div class="sourceCode cell-code" id="cb79"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb79-1"><a href="#cb79-1" aria-hidden="true" tabindex="-1"></a><span class="co"># recompute incidence for all years</span></span>
 <span id="cb79-2"><a href="#cb79-2" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> year <span class="kw">in</span> [<span class="dv">2019</span>, <span class="dv">2020</span>, <span class="dv">2021</span>]:</span>
 <span id="cb79-3"><a href="#cb79-3" aria-hidden="true" tabindex="-1"></a>    tb_census_df[<span class="ss">f"recompute incidence </span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>] <span class="op">=</span> tb_census_df[<span class="ss">f"TB cases </span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>]<span class="op">/</span>tb_census_df[<span class="ss">f"</span><span class="sc">{</span>year<span class="sc">}</span><span class="ss">"</span>]<span class="op">*</span><span class="dv">100000</span></span>
@@ -3646,21 +3646,21 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 <p>Reported TB incidence (cases per 100,000 persons) increased <strong>9.4%</strong>, from <strong>2.2</strong> during 2020 to <strong>2.4</strong> during 2021 but was lower than incidence during 2019 (2.7). Increases occurred among both U.S.-born and non–U.S.-born persons.</p>
 </blockquote>
 <p>Recall that percent change from <span class="math inline">\(A\)</span> to <span class="math inline">\(B\)</span> is computed as <span class="math inline">\(\text{percent change} = \frac{B - A}{A} \times 100\)</span>.</p>
-<div id="ad001457" class="cell" data-execution_count="55">
+<div id="146e2ad0" class="cell" data-execution_count="55">
 <div class="sourceCode cell-code" id="cb80"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb80-1"><a href="#cb80-1" aria-hidden="true" tabindex="-1"></a>incidence_2020 <span class="op">=</span> tb_census_df.loc[<span class="st">'Total'</span>, <span class="st">'recompute incidence 2020'</span>]</span>
 <span id="cb80-2"><a href="#cb80-2" aria-hidden="true" tabindex="-1"></a>incidence_2020</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="55">
 <pre><code>2.1637257652759883</code></pre>
 </div>
 </div>
-<div id="3b5c850b" class="cell" data-execution_count="56">
+<div id="39e6e00a" class="cell" data-execution_count="56">
 <div class="sourceCode cell-code" id="cb82"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb82-1"><a href="#cb82-1" aria-hidden="true" tabindex="-1"></a>incidence_2021 <span class="op">=</span> tb_census_df.loc[<span class="st">'Total'</span>, <span class="st">'recompute incidence 2021'</span>]</span>
 <span id="cb82-2"><a href="#cb82-2" aria-hidden="true" tabindex="-1"></a>incidence_2021</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="56">
 <pre><code>2.3672448914298068</code></pre>
 </div>
 </div>
-<div id="a36888eb" class="cell" data-execution_count="57">
+<div id="7e3c3b3f" class="cell" data-execution_count="57">
 <div class="sourceCode cell-code" id="cb84"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb84-1"><a href="#cb84-1" aria-hidden="true" tabindex="-1"></a>difference <span class="op">=</span> (incidence_2021 <span class="op">-</span> incidence_2020)<span class="op">/</span>incidence_2020 <span class="op">*</span> <span class="dv">100</span></span>
 <span id="cb84-2"><a href="#cb84-2" aria-hidden="true" tabindex="-1"></a>difference</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="57">
@@ -3672,7 +3672,7 @@ <h3 data-number="5.4.6" class="anchored" data-anchor-id="bonus-eda-reproducing-t
 <section id="eda-demo-2-mauna-loa-co2-data-a-lesson-in-data-faithfulness" class="level2" data-number="5.5">
 <h2 data-number="5.5" class="anchored" data-anchor-id="eda-demo-2-mauna-loa-co2-data-a-lesson-in-data-faithfulness"><span class="header-section-number">5.5</span> EDA Demo 2: Mauna Loa CO<sub>2</sub> Data – A Lesson in Data Faithfulness</h2>
 <p><a href="https://gml.noaa.gov/ccgg/trends/data.html">Mauna Loa Observatory</a> has been monitoring CO<sub>2</sub> concentrations since 1958.</p>
-<div id="8a69d82a" class="cell" data-execution_count="58">
+<div id="9e263863" class="cell" data-execution_count="58">
 <div class="sourceCode cell-code" id="cb86"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb86-1"><a href="#cb86-1" aria-hidden="true" tabindex="-1"></a>co2_file <span class="op">=</span> <span class="st">"data/co2_mm_mlo.txt"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Let’s do some <strong>EDA</strong>!!</p>
@@ -3697,7 +3697,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="reading-this-file-into-
 <li>The 71st and 72nd lines in the file contain column headings split over two lines.</li>
 </ul>
 <p>We can use&nbsp;<code>read_csv</code>&nbsp;to read the data into a <code>pandas</code> <code>DataFrame</code>, and we provide several arguments to specify that the separators are white space, there is no header (<strong>we will set our own column names</strong>), and to skip the first 72 rows of the file.</p>
-<div id="554e60af" class="cell" data-execution_count="59">
+<div id="e7d87a9d" class="cell" data-execution_count="59">
 <div class="sourceCode cell-code" id="cb88"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb88-1"><a href="#cb88-1" aria-hidden="true" tabindex="-1"></a>co2 <span class="op">=</span> pd.read_csv(</span>
 <span id="cb88-2"><a href="#cb88-2" aria-hidden="true" tabindex="-1"></a>    co2_file, header <span class="op">=</span> <span class="va">None</span>, skiprows <span class="op">=</span> <span class="dv">72</span>,</span>
 <span id="cb88-3"><a href="#cb88-3" aria-hidden="true" tabindex="-1"></a>    sep <span class="op">=</span> <span class="vs">r'\s+'</span>       <span class="co">#delimiter for continuous whitespace (stay tuned for regex next lecture))</span></span>
@@ -3785,7 +3785,7 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="reading-this-file-into-
 <h3 data-number="5.5.2" class="anchored" data-anchor-id="exploring-variable-feature-types"><span class="header-section-number">5.5.2</span> Exploring Variable Feature Types</h3>
 <p>The NOAA <a href="https://gml.noaa.gov/ccgg/trends/">webpage</a> might have some useful tidbits (in this case it doesn’t).</p>
 <p>Using this information, we’ll rerun <code>pd.read_csv</code>, but this time with some <strong>custom column names.</strong></p>
-<div id="e5181de0" class="cell" data-execution_count="60">
+<div id="d8f0d206" class="cell" data-execution_count="60">
 <div class="sourceCode cell-code" id="cb89"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb89-1"><a href="#cb89-1" aria-hidden="true" tabindex="-1"></a>co2 <span class="op">=</span> pd.read_csv(</span>
 <span id="cb89-2"><a href="#cb89-2" aria-hidden="true" tabindex="-1"></a>    co2_file, header <span class="op">=</span> <span class="va">None</span>, skiprows <span class="op">=</span> <span class="dv">72</span>,</span>
 <span id="cb89-3"><a href="#cb89-3" aria-hidden="true" tabindex="-1"></a>    sep <span class="op">=</span> <span class="st">'\s+'</span>, <span class="co">#regex for continuous whitespace (next lecture)</span></span>
@@ -3870,7 +3870,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="exploring-variable-feat
 <section id="visualizing-co2" class="level3" data-number="5.5.3">
 <h3 data-number="5.5.3" class="anchored" data-anchor-id="visualizing-co2"><span class="header-section-number">5.5.3</span> Visualizing CO<sub>2</sub></h3>
 <p>Scientific studies tend to have very clean data, right…? Let’s jump right in and make a time series plot of CO<sub>2</sub> monthly averages.</p>
-<div id="bf7356c4" class="cell" data-execution_count="61">
+<div id="19e0cb40" class="cell" data-execution_count="61">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb90"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb90-1"><a href="#cb90-1" aria-hidden="true" tabindex="-1"></a>sns.lineplot(x<span class="op">=</span><span class="st">'DecDate'</span>, y<span class="op">=</span><span class="st">'Avg'</span>, data<span class="op">=</span>co2)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3885,7 +3885,7 @@ <h3 data-number="5.5.3" class="anchored" data-anchor-id="visualizing-co2"><span
 </div>
 <p>The code above uses the <code>seaborn</code> plotting library (abbreviated <code>sns</code>). We will cover this in the Visualization lecture, but now you don’t need to worry about how it works!</p>
 <p>Yikes! Plotting the data uncovered a problem. The sharp vertical lines suggest that we have some <strong>missing values</strong>. What happened here?</p>
-<div id="99347502" class="cell" data-execution_count="62">
+<div id="2d8dba35" class="cell" data-execution_count="62">
 <div class="sourceCode cell-code" id="cb91"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb91-1"><a href="#cb91-1" aria-hidden="true" tabindex="-1"></a>co2.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="62">
 <div>
@@ -3961,7 +3961,7 @@ <h3 data-number="5.5.3" class="anchored" data-anchor-id="visualizing-co2"><span
 </div>
 </div>
 </div>
-<div id="5e620c30" class="cell" data-execution_count="63">
+<div id="075f9bef" class="cell" data-execution_count="63">
 <div class="sourceCode cell-code" id="cb92"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb92-1"><a href="#cb92-1" aria-hidden="true" tabindex="-1"></a>co2.tail()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="63">
 <div>
@@ -4054,7 +4054,7 @@ <h3 data-number="5.5.4" class="anchored" data-anchor-id="sanity-checks-reasoning
 <li>Data from March 1958 to August 2019.</li>
 <li>We should have $ 12 (2019-1957) - 2 - 4 = 738 $ records.</li>
 </ul>
-<div id="ba130a52" class="cell" data-execution_count="64">
+<div id="c21d6769" class="cell" data-execution_count="64">
 <div class="sourceCode cell-code" id="cb93"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb93-1"><a href="#cb93-1" aria-hidden="true" tabindex="-1"></a>co2.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="64">
 <pre><code>(738, 7)</code></pre>
@@ -4068,7 +4068,7 @@ <h3 data-number="5.5.5" class="anchored" data-anchor-id="understanding-missing-v
 <p><code>Days</code> is a time field, so let’s analyze other time fields to see if there is an explanation for missing values of days of operation.</p>
 <p>Let’s start with <strong>months</strong>, <code>Mo</code>.</p>
 <p>Are we missing any records? The number of months should have 62 or 61 instances (March 1957-August 2019).</p>
-<div id="143af7d2" class="cell" data-execution_count="65">
+<div id="e51ec7fc" class="cell" data-execution_count="65">
 <div class="sourceCode cell-code" id="cb95"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb95-1"><a href="#cb95-1" aria-hidden="true" tabindex="-1"></a>co2[<span class="st">"Mo"</span>].value_counts().sort_index()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="65">
 <pre><code>Mo
@@ -4090,7 +4090,7 @@ <h3 data-number="5.5.5" class="anchored" data-anchor-id="understanding-missing-v
 <p>As expected Jan, Feb, Sep, Oct, Nov, and Dec have 61 occurrences and the rest 62.</p>
 <p><br></p>
 <p>Next let’s explore <strong>days</strong> <code>Days</code> itself, which is the number of days that the measurement equipment worked.</p>
-<div id="a3957ebf" class="cell" data-execution_count="66">
+<div id="8147ff13" class="cell" data-execution_count="66">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb97"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb97-1"><a href="#cb97-1" aria-hidden="true" tabindex="-1"></a>sns.displot(co2[<span class="st">'Days'</span>])<span class="op">;</span></span>
@@ -4108,7 +4108,7 @@ <h3 data-number="5.5.5" class="anchored" data-anchor-id="understanding-missing-v
 <p><br></p>
 <p>Finally, let’s check the last time feature, <strong>year</strong> <code>Yr</code>.</p>
 <p>Let’s check to see if there is any connection between missing-ness and the year of the recording.</p>
-<div id="4eb67654" class="cell" data-execution_count="67">
+<div id="18b38fe2" class="cell" data-execution_count="67">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb98"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb98-1"><a href="#cb98-1" aria-hidden="true" tabindex="-1"></a>sns.scatterplot(x<span class="op">=</span><span class="st">"Yr"</span>, y<span class="op">=</span><span class="st">"Days"</span>, data<span class="op">=</span>co2)<span class="op">;</span></span>
@@ -4137,7 +4137,7 @@ <h3 data-number="5.5.5" class="anchored" data-anchor-id="understanding-missing-v
 <section id="understanding-missing-value-2-avg" class="level3" data-number="5.5.6">
 <h3 data-number="5.5.6" class="anchored" data-anchor-id="understanding-missing-value-2-avg"><span class="header-section-number">5.5.6</span> Understanding Missing Value 2: <code>Avg</code></h3>
 <p>Next, let’s return to the -99.99 values in <code>Avg</code> to analyze the overall quality of the CO<sub>2</sub> measurements. We’ll plot a histogram of the average CO<sub>2</sub> measurements</p>
-<div id="e3c4157e" class="cell" data-execution_count="68">
+<div id="b4d8a69d" class="cell" data-execution_count="68">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb99"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb99-1"><a href="#cb99-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Histograms of average CO2 measurements</span></span>
@@ -4153,7 +4153,7 @@ <h3 data-number="5.5.6" class="anchored" data-anchor-id="understanding-missing-v
 </div>
 <p>The non-missing values are in the 300-400 range (a regular range of CO<sub>2</sub> levels).</p>
 <p>We also see that there are only a few missing <code>Avg</code> values (<strong>&lt;1% of values</strong>). Let’s examine all of them:</p>
-<div id="2f346322" class="cell" data-execution_count="69">
+<div id="2308edad" class="cell" data-execution_count="69">
 <div class="sourceCode cell-code" id="cb100"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb100-1"><a href="#cb100-1" aria-hidden="true" tabindex="-1"></a>co2[co2[<span class="st">"Avg"</span>] <span class="op">&lt;</span> <span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="69">
 <div>
@@ -4260,7 +4260,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 <li>Impute using some strategy</li>
 </ol>
 <p>Remember we want to fix the following plot:</p>
-<div id="04659520" class="cell" data-execution_count="70">
+<div id="9c1a0ed9" class="cell" data-execution_count="70">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb101"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb101-1"><a href="#cb101-1" aria-hidden="true" tabindex="-1"></a>sns.lineplot(x<span class="op">=</span><span class="st">'DecDate'</span>, y<span class="op">=</span><span class="st">'Avg'</span>, data<span class="op">=</span>co2)</span>
@@ -4278,7 +4278,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 <p>Let’s consider a few options: 1. Drop those records 2. Replace -99.99 with NaN 3. Substitute it with a likely value for the average CO<sub>2</sub>?</p>
 <p>What do you think are the pros and cons of each possible action?</p>
 <p>Let’s examine each of these three options.</p>
-<div id="017d212a" class="cell" data-execution_count="71">
+<div id="9d61a94b" class="cell" data-execution_count="71">
 <div class="sourceCode cell-code" id="cb102"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb102-1"><a href="#cb102-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 1. Drop missing values</span></span>
 <span id="cb102-2"><a href="#cb102-2" aria-hidden="true" tabindex="-1"></a>co2_drop <span class="op">=</span> co2[co2[<span class="st">'Avg'</span>] <span class="op">&gt;</span> <span class="dv">0</span>]</span>
 <span id="cb102-3"><a href="#cb102-3" aria-hidden="true" tabindex="-1"></a>co2_drop.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -4356,7 +4356,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 </div>
 </div>
 </div>
-<div id="80447385" class="cell" data-execution_count="72">
+<div id="32426d34" class="cell" data-execution_count="72">
 <div class="sourceCode cell-code" id="cb103"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb103-1"><a href="#cb103-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 2. Replace NaN with -99.99</span></span>
 <span id="cb103-2"><a href="#cb103-2" aria-hidden="true" tabindex="-1"></a>co2_NA <span class="op">=</span> co2.replace(<span class="op">-</span><span class="fl">99.99</span>, np.nan)</span>
 <span id="cb103-3"><a href="#cb103-3" aria-hidden="true" tabindex="-1"></a>co2_NA.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -4442,7 +4442,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 </blockquote>
 <p>The <code>Int</code> feature has values that exactly match those in <code>Avg</code>, except when <code>Avg</code> is -99.99, and then a <strong>reasonable</strong> estimate is used instead.</p>
 <p>So, the third version of our data will use the <code>Int</code> feature instead of <code>Avg</code>.</p>
-<div id="0f996de5" class="cell" data-execution_count="73">
+<div id="9d6fb662" class="cell" data-execution_count="73">
 <div class="sourceCode cell-code" id="cb104"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb104-1"><a href="#cb104-1" aria-hidden="true" tabindex="-1"></a><span class="co"># 3. Use interpolated column which estimates missing Avg values</span></span>
 <span id="cb104-2"><a href="#cb104-2" aria-hidden="true" tabindex="-1"></a>co2_impute <span class="op">=</span> co2.copy()</span>
 <span id="cb104-3"><a href="#cb104-3" aria-hidden="true" tabindex="-1"></a>co2_impute[<span class="st">'Avg'</span>] <span class="op">=</span> co2[<span class="st">'Int'</span>]</span>
@@ -4523,7 +4523,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 </div>
 <p>What’s a <strong>reasonable</strong> estimate?</p>
 <p>To answer this question, let’s zoom in on a short time period, say the measurements in 1958 (where we know we have two missing values).</p>
-<div id="fcca3504" class="cell" data-execution_count="74">
+<div id="9ab3ab76" class="cell" data-execution_count="74">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb105"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb105-1"><a href="#cb105-1" aria-hidden="true" tabindex="-1"></a><span class="co"># results of plotting data in 1958</span></span>
@@ -4566,7 +4566,7 @@ <h3 data-number="5.5.7" class="anchored" data-anchor-id="drop-nan-or-impute-miss
 <li>We are plotting all months in our data as a line plot</li>
 </ul>
 <p>Let’s replot our original figure with option 3:</p>
-<div id="336b04db" class="cell" data-execution_count="75">
+<div id="a33dd9a7" class="cell" data-execution_count="75">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb106"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb106-1"><a href="#cb106-1" aria-hidden="true" tabindex="-1"></a>sns.lineplot(x<span class="op">=</span><span class="st">'DecDate'</span>, y<span class="op">=</span><span class="st">'Avg'</span>, data<span class="op">=</span>co2_impute)</span>
@@ -4598,7 +4598,7 @@ <h3 data-number="5.5.8" class="anchored" data-anchor-id="presenting-the-data-a-d
 <ul>
 <li>You might be happier with a <strong>coarser granularity</strong> of average year data!</li>
 </ul>
-<div id="70592be7" class="cell" data-execution_count="76">
+<div id="4a82e195" class="cell" data-execution_count="76">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb107"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb107-1"><a href="#cb107-1" aria-hidden="true" tabindex="-1"></a>co2_year <span class="op">=</span> co2_impute.groupby(<span class="st">'Yr'</span>).mean()</span>
diff --git a/docs/eda/eda_files/figure-pdf/cell-62-output-1.pdf b/docs/eda/eda_files/figure-pdf/cell-62-output-1.pdf
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diff --git a/docs/pandas_1/pandas_1.html b/docs/pandas_1/pandas_1.html
index a633163a..f80340d3 100644
--- a/docs/pandas_1/pandas_1.html
+++ b/docs/pandas_1/pandas_1.html
@@ -349,7 +349,7 @@ <h2 data-number="2.1" class="anchored" data-anchor-id="tabular-data"><span class
 <section id="series-dataframes-and-indices" class="level2" data-number="2.2">
 <h2 data-number="2.2" class="anchored" data-anchor-id="series-dataframes-and-indices"><span class="header-section-number">2.2</span> <code>Series</code>, <code>DataFrame</code>s, and Indices</h2>
 <p>To begin our work in <code>pandas</code>, we must first import the library into our Python environment. This will allow us to use <code>pandas</code> data structures and methods in our code.</p>
-<div id="22c4620c" class="cell" data-execution_count="1">
+<div id="3012b0b5" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># `pd` is the conventional alias for Pandas, as `np` is for NumPy</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -372,7 +372,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="series"><span class="he
 <li>A sequence of data labels called the <strong>index</strong>.</li>
 </ul>
 <p>In the cell below, we create a <code>Series</code> named <code>s</code>.</p>
-<div id="f8dc9cdc" class="cell" data-execution_count="2">
+<div id="06b4fae5" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>s <span class="op">=</span> pd.Series([<span class="st">"welcome"</span>, <span class="st">"to"</span>, <span class="st">"data 100"</span>])</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>s</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="2">
@@ -382,14 +382,14 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="series"><span class="he
 dtype: object</code></pre>
 </div>
 </div>
-<div id="76ef0a64" class="cell" data-execution_count="3">
+<div id="473956ef" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a> <span class="co"># Accessing data values within the Series</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> s.values</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="3">
 <pre><code>array(['welcome', 'to', 'data 100'], dtype=object)</code></pre>
 </div>
 </div>
-<div id="8d5939d7" class="cell" data-execution_count="4">
+<div id="848c26c4" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a> <span class="co"># Accessing the Index of the Series</span></span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> s.index</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="4">
@@ -397,7 +397,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="series"><span class="he
 </div>
 </div>
 <p>By default, the <code>index</code> of a <code>Series</code> is a sequential list of integers beginning from 0. Optionally, a manually specified list of desired indices can be passed to the <code>index</code> argument.</p>
-<div id="eeed1944" class="cell" data-execution_count="5">
+<div id="3fa68824" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>s <span class="op">=</span> pd.Series([<span class="op">-</span><span class="dv">1</span>, <span class="dv">10</span>, <span class="dv">2</span>], index <span class="op">=</span> [<span class="st">"a"</span>, <span class="st">"b"</span>, <span class="st">"c"</span>])</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>s</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="5">
@@ -407,14 +407,14 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="series"><span class="he
 dtype: int64</code></pre>
 </div>
 </div>
-<div id="8c401a86" class="cell" data-execution_count="6">
+<div id="1092c207" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>s.index</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="6">
 <pre><code>Index(['a', 'b', 'c'], dtype='object')</code></pre>
 </div>
 </div>
 <p>Indices can also be changed after initialization.</p>
-<div id="b383f586" class="cell" data-execution_count="7">
+<div id="8ec71fd2" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>s.index <span class="op">=</span> [<span class="st">"first"</span>, <span class="st">"second"</span>, <span class="st">"third"</span>]</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>s</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
@@ -424,7 +424,7 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="series"><span class="he
 dtype: int64</code></pre>
 </div>
 </div>
-<div id="87f7ab0b" class="cell" data-execution_count="8">
+<div id="e72e68ea" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>s.index</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <pre><code>Index(['first', 'second', 'third'], dtype='object')</code></pre>
@@ -439,7 +439,7 @@ <h4 data-number="2.2.1.1" class="anchored" data-anchor-id="selection-in-series">
 <li>A filtering condition.</li>
 </ol>
 <p>To demonstrate this, let’s define a new Series <code>s</code>.</p>
-<div id="18202c02" class="cell" data-execution_count="9">
+<div id="9e01f161" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>s <span class="op">=</span> pd.Series([<span class="dv">4</span>, <span class="op">-</span><span class="dv">2</span>, <span class="dv">0</span>, <span class="dv">6</span>], index <span class="op">=</span> [<span class="st">"a"</span>, <span class="st">"b"</span>, <span class="st">"c"</span>, <span class="st">"d"</span>])</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>s</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
@@ -452,7 +452,7 @@ <h4 data-number="2.2.1.1" class="anchored" data-anchor-id="selection-in-series">
 </div>
 <section id="a-single-label" class="level5" data-number="2.2.1.1.1">
 <h5 data-number="2.2.1.1.1" class="anchored" data-anchor-id="a-single-label"><span class="header-section-number">2.2.1.1.1</span> A Single Label</h5>
-<div id="e3a7a697" class="cell" data-execution_count="10">
+<div id="d3eaca4d" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># We return the value stored at the index label "a"</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>s[<span class="st">"a"</span>] </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
@@ -462,7 +462,7 @@ <h5 data-number="2.2.1.1.1" class="anchored" data-anchor-id="a-single-label"><sp
 </section>
 <section id="a-list-of-labels" class="level5" data-number="2.2.1.1.2">
 <h5 data-number="2.2.1.1.2" class="anchored" data-anchor-id="a-list-of-labels"><span class="header-section-number">2.2.1.1.2</span> A List of Labels</h5>
-<div id="76f1f06e" class="cell" data-execution_count="11">
+<div id="868c5b95" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># We return a Series of the values stored at the index labels "a" and "c"</span></span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>s[[<span class="st">"a"</span>, <span class="st">"c"</span>]] </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
@@ -476,7 +476,7 @@ <h5 data-number="2.2.1.1.2" class="anchored" data-anchor-id="a-list-of-labels"><
 <h5 data-number="2.2.1.1.3" class="anchored" data-anchor-id="a-filtering-condition"><span class="header-section-number">2.2.1.1.3</span> A Filtering Condition</h5>
 <p>Perhaps the most interesting (and useful) method of selecting data from a <code>Series</code> is by using a filtering condition.</p>
 <p>First, we apply a boolean operation to the <code>Series</code>. This creates <strong>a new <code>Series</code> of boolean values</strong>.</p>
-<div id="5792af02" class="cell" data-execution_count="12">
+<div id="2d1914ca" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter condition: select all elements greater than 0</span></span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>s <span class="op">&gt;</span> <span class="dv">0</span> </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
@@ -488,7 +488,7 @@ <h5 data-number="2.2.1.1.3" class="anchored" data-anchor-id="a-filtering-conditi
 </div>
 </div>
 <p>We then use this boolean condition to index into our original <code>Series</code>. <code>pandas</code> will select only the entries in the original <code>Series</code> that satisfy the condition.</p>
-<div id="1e9b983c" class="cell" data-execution_count="13">
+<div id="e17d3a92" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>s[s <span class="op">&gt;</span> <span class="dv">0</span>] </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
 <pre><code>a    4
@@ -518,7 +518,7 @@ <h4 data-number="2.2.2.1" class="anchored" data-anchor-id="creating-a-dataframe"
 <h5 data-number="2.2.2.1.1" class="anchored" data-anchor-id="from-a-csv-file"><span class="header-section-number">2.2.2.1.1</span> From a CSV file</h5>
 <p>In Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a <code>DataFrame</code> by passing the data path as an argument to the following <code>pandas</code> function. <br>  <code>pd.read_csv("filename.csv")</code></p>
 <p>With our new understanding of <code>pandas</code> in hand, let’s return to the <code>elections</code> dataset from before. Now, we can recognize that it is represented as a <code>pandas</code> <code>DataFrame</code>.</p>
-<div id="b23c81a0" class="cell" data-execution_count="14">
+<div id="7f52913e" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>elections <span class="op">=</span> pd.read_csv(<span class="st">"data/elections.csv"</span>)</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>elections</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
@@ -650,7 +650,7 @@ <h5 data-number="2.2.2.1.1" class="anchored" data-anchor-id="from-a-csv-file"><s
 <h5 data-number="2.2.2.1.2" class="anchored" data-anchor-id="using-a-list-and-column-names"><span class="header-section-number">2.2.2.1.2</span> Using a List and Column Name(s)</h5>
 <p>We’ll now explore creating a <code>DataFrame</code> with data of our own.</p>
 <p>Consider the following examples. The first code cell creates a <code>DataFrame</code> with a single column <code>Numbers</code>.</p>
-<div id="2ca7c45c" class="cell" data-execution_count="15">
+<div id="49c880c5" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>df_list <span class="op">=</span> pd.DataFrame([<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>], columns<span class="op">=</span>[<span class="st">"Numbers"</span>])</span>
 <span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>df_list</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="15">
@@ -684,7 +684,7 @@ <h5 data-number="2.2.2.1.2" class="anchored" data-anchor-id="using-a-list-and-co
 </div>
 </div>
 <p>The second creates a <code>DataFrame</code> with the columns <code>Numbers</code> and <code>Description</code>. Notice how a 2D list of values is required to initialize the second <code>DataFrame</code> — each nested list represents a single row of data.</p>
-<div id="33ad3a17" class="cell" data-execution_count="16">
+<div id="5f5da5d3" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>df_list <span class="op">=</span> pd.DataFrame([[<span class="dv">1</span>, <span class="st">"one"</span>], [<span class="dv">2</span>, <span class="st">"two"</span>]], columns <span class="op">=</span> [<span class="st">"Number"</span>, <span class="st">"Description"</span>])</span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>df_list</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
@@ -721,7 +721,7 @@ <h5 data-number="2.2.2.1.2" class="anchored" data-anchor-id="using-a-list-and-co
 <h5 data-number="2.2.2.1.3" class="anchored" data-anchor-id="from-a-dictionary"><span class="header-section-number">2.2.2.1.3</span> From a Dictionary</h5>
 <p>A third (and more common) way to create a <code>DataFrame</code> is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.</p>
 <p>Below are two ways of implementing this approach. The first is based on specifying the columns of the <code>DataFrame</code>, whereas the second is based on specifying the rows of the <code>DataFrame</code>.</p>
-<div id="441a6590" class="cell" data-execution_count="17">
+<div id="5a3ba20d" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>df_dict <span class="op">=</span> pd.DataFrame({</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">"Fruit"</span>: [<span class="st">"Strawberry"</span>, <span class="st">"Orange"</span>], </span>
 <span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">"Price"</span>: [<span class="fl">5.49</span>, <span class="fl">3.99</span>]</span>
@@ -756,7 +756,7 @@ <h5 data-number="2.2.2.1.3" class="anchored" data-anchor-id="from-a-dictionary">
 </div>
 </div>
 </div>
-<div id="cc098236" class="cell" data-execution_count="18">
+<div id="9030a4ed" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>df_dict <span class="op">=</span> pd.DataFrame(</span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>    [</span>
 <span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>        {<span class="st">"Fruit"</span>:<span class="st">"Strawberry"</span>, <span class="st">"Price"</span>:<span class="fl">5.49</span>}, </span>
@@ -798,14 +798,14 @@ <h5 data-number="2.2.2.1.3" class="anchored" data-anchor-id="from-a-dictionary">
 <h5 data-number="2.2.2.1.4" class="anchored" data-anchor-id="from-a-series"><span class="header-section-number">2.2.2.1.4</span> From a <code>Series</code></h5>
 <p>Earlier, we explained how a <code>Series</code> was synonymous to a column in a <code>DataFrame</code>. It follows, then, that a <code>DataFrame</code> is equivalent to a collection of <code>Series</code>, which all share the same <code>Index</code>.</p>
 <p>In fact, we can initialize a <code>DataFrame</code> by merging two or more <code>Series</code>. Consider the <code>Series</code> <code>s_a</code> and <code>s_b</code>.</p>
-<div id="70fa6f7d" class="cell" data-execution_count="19">
+<div id="6d22fcd8" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Notice how our indices, or row labels, are the same</span></span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>s_a <span class="op">=</span> pd.Series([<span class="st">"a1"</span>, <span class="st">"a2"</span>, <span class="st">"a3"</span>], index <span class="op">=</span> [<span class="st">"r1"</span>, <span class="st">"r2"</span>, <span class="st">"r3"</span>])</span>
 <span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>s_b <span class="op">=</span> pd.Series([<span class="st">"b1"</span>, <span class="st">"b2"</span>, <span class="st">"b3"</span>], index <span class="op">=</span> [<span class="st">"r1"</span>, <span class="st">"r2"</span>, <span class="st">"r3"</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We can turn individual <code>Series</code> into a <code>DataFrame</code> using two common methods (shown below):</p>
-<div id="8c6dcae5" class="cell" data-execution_count="20">
+<div id="e49eebd8" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>pd.DataFrame(s_a)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="20">
 <div>
@@ -837,7 +837,7 @@ <h5 data-number="2.2.2.1.4" class="anchored" data-anchor-id="from-a-series"><spa
 </div>
 </div>
 </div>
-<div id="203f9746" class="cell" data-execution_count="21">
+<div id="9d6a9fda" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>s_b.to_frame()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
 <div>
@@ -870,7 +870,7 @@ <h5 data-number="2.2.2.1.4" class="anchored" data-anchor-id="from-a-series"><spa
 </div>
 </div>
 <p>To merge the two <code>Series</code> and specify their column names, we use the following syntax:</p>
-<div id="1d18fa89" class="cell" data-execution_count="22">
+<div id="eaeb8f1b" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>pd.DataFrame({</span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">"A-column"</span>: s_a, </span>
 <span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">"B-column"</span>: s_b</span>
@@ -915,7 +915,7 @@ <h5 data-number="2.2.2.1.4" class="anchored" data-anchor-id="from-a-series"><spa
 <section id="indices" class="level3" data-number="2.2.3">
 <h3 data-number="2.2.3" class="anchored" data-anchor-id="indices"><span class="header-section-number">2.2.3</span> Indices</h3>
 <p>On a more technical note, an index doesn’t have to be an integer, nor does it have to be unique. For example, we can set the index of the <code>elections</code> <code>DataFrame</code> to be the name of presidential candidates.</p>
-<div id="c76401c1" class="cell" data-execution_count="23">
+<div id="92979293" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Creating a DataFrame from a CSV file and specifying the index column</span></span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>elections <span class="op">=</span> pd.read_csv(<span class="st">"data/elections.csv"</span>, index_col <span class="op">=</span> <span class="st">"Candidate"</span>)</span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>elections</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1039,7 +1039,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="indices"><span class="h
 </div>
 </div>
 <p>We can also select a new column and set it as the index of the <code>DataFrame</code>. For example, we can set the index of the <code>elections</code> <code>DataFrame</code> to represent the candidate’s party.</p>
-<div id="02c8e8af" class="cell" data-execution_count="24">
+<div id="e0ad2c6f" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>elections.reset_index(inplace <span class="op">=</span> <span class="va">True</span>) <span class="co"># Resetting the index so we can set it again</span></span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a><span class="co"># This sets the index to the "Party" column</span></span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>elections.set_index(<span class="st">"Party"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1163,7 +1163,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="indices"><span class="h
 </div>
 </div>
 <p>And, if we’d like, we can revert the index back to the default list of integers.</p>
-<div id="4d5ccf64" class="cell" data-execution_count="25">
+<div id="60fb0bbc" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This resets the index to be the default list of integer</span></span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>elections.reset_index(inplace<span class="op">=</span><span class="va">True</span>) </span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>elections.index</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1186,7 +1186,7 @@ <h3 data-number="2.2.3" class="anchored" data-anchor-id="indices"><span class="h
 <h2 data-number="2.3" class="anchored" data-anchor-id="dataframe-attributes-index-columns-and-shape"><span class="header-section-number">2.3</span> <code>DataFrame</code> Attributes: Index, Columns, and Shape</h2>
 <p>On the other hand, column names in a <code>DataFrame</code> are almost always unique. Looking back to the <code>elections</code> dataset, it wouldn’t make sense to have two columns named <code>"Candidate"</code>. Sometimes, you’ll want to extract these different values, in particular, the list of row and column labels.</p>
 <p>For index/row labels, use <code>DataFrame.index</code>:</p>
-<div id="2ecd797f" class="cell" data-execution_count="26">
+<div id="962c8887" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>elections.set_index(<span class="st">"Party"</span>, inplace <span class="op">=</span> <span class="va">True</span>)</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>elections.index</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
@@ -1201,14 +1201,14 @@ <h2 data-number="2.3" class="anchored" data-anchor-id="dataframe-attributes-inde
 </div>
 </div>
 <p>For column labels, use <code>DataFrame.columns</code>:</p>
-<div id="5f2717be" class="cell" data-execution_count="27">
+<div id="a1c5e75d" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>elections.columns</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
 <pre><code>Index(['index', 'Candidate', 'Year', 'Popular vote', 'Result', '%'], dtype='object')</code></pre>
 </div>
 </div>
 <p>And for the shape of the <code>DataFrame</code>, we can use <code>DataFrame.shape</code> to get the number of rows followed by the number of columns:</p>
-<div id="e7926045" class="cell" data-execution_count="28">
+<div id="e9eb64ff" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>elections.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="28">
 <pre><code>(182, 6)</code></pre>
@@ -1237,13 +1237,13 @@ <h2 data-number="2.4" class="anchored" data-anchor-id="slicing-in-dataframes"><s
 <h3 data-number="2.4.1" class="anchored" data-anchor-id="extracting-data-with-.head-and-.tail"><span class="header-section-number">2.4.1</span> Extracting data with <code>.head</code> and <code>.tail</code></h3>
 <p>The simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the <code>DataFrame</code>.</p>
 <p>To extract the first <code>n</code> rows of a <code>DataFrame</code> <code>df</code>, we use the syntax <code>df.head(n)</code>.</p>
-<div id="b051f819" class="cell" data-execution_count="29">
+<div id="fcfa2a75" class="cell" data-execution_count="29">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>elections <span class="op">=</span> pd.read_csv(<span class="st">"data/elections.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
-<div id="b476ecbd" class="cell" data-execution_count="30">
+<div id="b8d564ab" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract the first 5 rows of the DataFrame</span></span>
 <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a>elections.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
@@ -1315,7 +1315,7 @@ <h3 data-number="2.4.1" class="anchored" data-anchor-id="extracting-data-with-.h
 </div>
 </div>
 <p>Similarly, calling <code>df.tail(n)</code> allows us to extract the last <code>n</code> rows of the <code>DataFrame</code>.</p>
-<div id="ec71a8a7" class="cell" data-execution_count="31">
+<div id="820adff4" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Extract the last 5 rows of the DataFrame</span></span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>elections.tail(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
@@ -1401,14 +1401,14 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 <li>A list.</li>
 </ul>
 <p>For example, to select a single value, we can select the row labeled <code>0</code> and the column labeled <code>Candidate</code> from the <code>elections</code> <code>DataFrame</code>.</p>
-<div id="b25952ab" class="cell" data-execution_count="32">
+<div id="ff8d7f72" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a>elections.loc[<span class="dv">0</span>, <span class="st">'Candidate'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
 <pre><code>'Andrew Jackson'</code></pre>
 </div>
 </div>
 <p>Keep in mind that passing in just one argument as a single value will produce a <code>Series</code>. Below, we’ve extracted a subset of the <code>"Popular vote"</code> column as a <code>Series</code>.</p>
-<div id="6862baa9" class="cell" data-execution_count="33">
+<div id="d1120fa2" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>elections.loc[[<span class="dv">87</span>, <span class="dv">25</span>, <span class="dv">179</span>], <span class="st">"Popular vote"</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="33">
 <pre><code>87     15761254
@@ -1418,7 +1418,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 </div>
 <p>Note that if we pass <code>"Popular vote"</code> as a list, the output will be a <code>DataFrame</code>.</p>
-<div id="beb41c42" class="cell" data-execution_count="34">
+<div id="dc037b2d" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a>elections.loc[[<span class="dv">87</span>, <span class="dv">25</span>, <span class="dv">179</span>], [<span class="st">"Popular vote"</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="34">
 <div>
@@ -1451,7 +1451,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 </div>
 <p>To select <em>multiple</em> rows and columns, we can use Python slice notation. Here, we select the rows from labels <code>0</code> to <code>3</code> and the columns from labels <code>"Year"</code> to <code>"Popular vote"</code>. Notice that unlike Python slicing, <code>.loc</code> is <em>inclusive</em> of the right upper bound.</p>
-<div id="745add2b" class="cell" data-execution_count="35">
+<div id="a9cc6b81" class="cell" data-execution_count="35">
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>elections.loc[<span class="dv">0</span>:<span class="dv">3</span>, <span class="st">'Year'</span>:<span class="st">'Popular vote'</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="35">
 <div>
@@ -1503,7 +1503,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 </div>
 <p>Suppose that instead, we want to extract <em>all</em> column values for the first four rows in the <code>elections</code> <code>DataFrame</code>. The shorthand <code>:</code> is useful for this.</p>
-<div id="29f88c01" class="cell" data-execution_count="36">
+<div id="3e95066a" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>elections.loc[<span class="dv">0</span>:<span class="dv">3</span>, :]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="36">
 <div>
@@ -1565,7 +1565,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 </div>
 <p>We can use the same shorthand to extract all rows.</p>
-<div id="f0b532d7" class="cell" data-execution_count="37">
+<div id="68f65989" class="cell" data-execution_count="37">
 <div class="sourceCode cell-code" id="cb56"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>elections.loc[:, [<span class="st">"Year"</span>, <span class="st">"Candidate"</span>, <span class="st">"Result"</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
 <div>
@@ -1656,7 +1656,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 <p>There are a couple of things we should note. Firstly, unlike conventional Python, <code>pandas</code> allows us to slice string values (in our example, the column labels). Secondly, slicing with <code>.loc</code> is <em>inclusive</em>. Notice how our resulting <code>DataFrame</code> includes every row and column between and including the slice labels we specified.</p>
 <p>Equivalently, we can use a list to obtain multiple rows and columns in our <code>elections</code> <code>DataFrame</code>.</p>
-<div id="8b401c12" class="cell" data-execution_count="38">
+<div id="f7b64c2f" class="cell" data-execution_count="38">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>elections.loc[[<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>], [<span class="st">'Year'</span>, <span class="st">'Candidate'</span>, <span class="st">'Party'</span>, <span class="st">'Popular vote'</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="38">
 <div>
@@ -1708,7 +1708,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 </div>
 </div>
 <p>Lastly, we can interchange list and slicing notation.</p>
-<div id="25d04020" class="cell" data-execution_count="39">
+<div id="4cc94c7b" class="cell" data-execution_count="39">
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a>elections.loc[[<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>], :]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="39">
 <div>
@@ -1774,7 +1774,7 @@ <h3 data-number="2.4.2" class="anchored" data-anchor-id="label-based-extraction-
 <h3 data-number="2.4.3" class="anchored" data-anchor-id="integer-based-extraction-indexing-with-.iloc"><span class="header-section-number">2.4.3</span> Integer-based Extraction: Indexing with <code>.iloc</code></h3>
 <p>Slicing with <code>.iloc</code> works similarly to <code>.loc</code>. However, <code>.iloc</code> uses the <em>index positions</em> of rows and columns rather than the labels (think to yourself: <strong>l</strong>oc uses <strong>l</strong>ables; <strong>i</strong>loc uses <strong>i</strong>ndices). The arguments to the <code>.iloc</code> function also behave similarly — single values, lists, indices, and any combination of these are permitted.</p>
 <p>Let’s begin reproducing our results from above. We’ll begin by selecting the first presidential candidate in our <code>elections</code> <code>DataFrame</code>:</p>
-<div id="012025ad" class="cell" data-execution_count="40">
+<div id="7a0feb62" class="cell" data-execution_count="40">
 <div class="sourceCode cell-code" id="cb59"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a><span class="co"># elections.loc[0, "Candidate"] - Previous approach</span></span>
 <span id="cb59-2"><a href="#cb59-2" aria-hidden="true" tabindex="-1"></a>elections.iloc[<span class="dv">0</span>, <span class="dv">1</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="40">
@@ -1783,7 +1783,7 @@ <h3 data-number="2.4.3" class="anchored" data-anchor-id="integer-based-extractio
 </div>
 <p>Notice how the first argument to both <code>.loc</code> and <code>.iloc</code> are the same. This is because the row with a label of <code>0</code> is conveniently in the <span class="math inline">\(0^{\text{th}}\)</span> (equivalently, the first position) of the <code>elections</code> <code>DataFrame</code>. Generally, this is true of any <code>DataFrame</code> where the row labels are incremented in ascending order from 0.</p>
 <p>And, as before, if we were to pass in only one single value argument, our result would be a <code>Series</code>.</p>
-<div id="5ae9d15f" class="cell" data-execution_count="41">
+<div id="1cb6e9d4" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb61"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a>elections.iloc[[<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>],<span class="dv">1</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="41">
 <pre><code>1    John Quincy Adams
@@ -1793,7 +1793,7 @@ <h3 data-number="2.4.3" class="anchored" data-anchor-id="integer-based-extractio
 </div>
 </div>
 <p>However, when we select the first four rows and columns using <code>.iloc</code>, we notice something.</p>
-<div id="b068cd51" class="cell" data-execution_count="42">
+<div id="506a7b1b" class="cell" data-execution_count="42">
 <div class="sourceCode cell-code" id="cb63"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a><span class="co"># elections.loc[0:3, 'Year':'Popular vote'] - Previous approach</span></span>
 <span id="cb63-2"><a href="#cb63-2" aria-hidden="true" tabindex="-1"></a>elections.iloc[<span class="dv">0</span>:<span class="dv">4</span>, <span class="dv">0</span>:<span class="dv">4</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="42">
@@ -1847,7 +1847,7 @@ <h3 data-number="2.4.3" class="anchored" data-anchor-id="integer-based-extractio
 </div>
 <p>Slicing is no longer inclusive in <code>.iloc</code> — it’s <em>exclusive</em>. In other words, the right end of a slice is not included when using <code>.iloc</code>. This is one of the subtleties of <code>pandas</code> syntax; you will get used to it with practice.</p>
 <p>List behavior works just as expected.</p>
-<div id="82cb4f41" class="cell" data-execution_count="43">
+<div id="f120c24b" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb64"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a><span class="co">#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach</span></span>
 <span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a>elections.iloc[[<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>], [<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="43">
@@ -1900,7 +1900,7 @@ <h3 data-number="2.4.3" class="anchored" data-anchor-id="integer-based-extractio
 </div>
 </div>
 <p>And just like with <code>.loc</code>, we can use a colon with <code>.iloc</code> to extract all rows or columns.</p>
-<div id="953c7744" class="cell" data-execution_count="44">
+<div id="681e4956" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb65"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb65-1"><a href="#cb65-1" aria-hidden="true" tabindex="-1"></a>elections.iloc[:, <span class="dv">0</span>:<span class="dv">3</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="44">
 <div>
@@ -2008,7 +2008,7 @@ <h3 data-number="2.4.4" class="anchored" data-anchor-id="context-dependent-extra
 <section id="a-slice-of-row-numbers" class="level4" data-number="2.4.4.1">
 <h4 data-number="2.4.4.1" class="anchored" data-anchor-id="a-slice-of-row-numbers"><span class="header-section-number">2.4.4.1</span> A slice of row numbers</h4>
 <p>Say we wanted the first four rows of our <code>elections</code> <code>DataFrame</code>.</p>
-<div id="cfdbf6cf" class="cell" data-execution_count="45">
+<div id="ac7fc626" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb66"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a>elections[<span class="dv">0</span>:<span class="dv">4</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
 <div>
@@ -2073,7 +2073,7 @@ <h4 data-number="2.4.4.1" class="anchored" data-anchor-id="a-slice-of-row-number
 <section id="a-list-of-column-labels" class="level4" data-number="2.4.4.2">
 <h4 data-number="2.4.4.2" class="anchored" data-anchor-id="a-list-of-column-labels"><span class="header-section-number">2.4.4.2</span> A list of column labels</h4>
 <p>Suppose we now want the first four columns.</p>
-<div id="42a488e3" class="cell" data-execution_count="46">
+<div id="c588d6fc" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb67"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb67-1"><a href="#cb67-1" aria-hidden="true" tabindex="-1"></a>elections[[<span class="st">"Year"</span>, <span class="st">"Candidate"</span>, <span class="st">"Party"</span>, <span class="st">"Popular vote"</span>]]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="46">
 <div>
@@ -2178,7 +2178,7 @@ <h4 data-number="2.4.4.2" class="anchored" data-anchor-id="a-list-of-column-labe
 <section id="a-single-column-label" class="level4" data-number="2.4.4.3">
 <h4 data-number="2.4.4.3" class="anchored" data-anchor-id="a-single-column-label"><span class="header-section-number">2.4.4.3</span> A single-column label</h4>
 <p>Lastly, <code>[]</code> allows us to extract only the <code>"Candidate"</code> column.</p>
-<div id="59917f2f" class="cell" data-execution_count="47">
+<div id="3bd695cf" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb68"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb68-1"><a href="#cb68-1" aria-hidden="true" tabindex="-1"></a>elections[<span class="st">"Candidate"</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="47">
 <pre><code>0         Andrew Jackson
diff --git a/docs/pandas_2/pandas_2.html b/docs/pandas_2/pandas_2.html
index 678c9dbd..3622541a 100644
--- a/docs/pandas_2/pandas_2.html
+++ b/docs/pandas_2/pandas_2.html
@@ -274,7 +274,7 @@ <h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapt
 <p>Last time, we introduced the <code>pandas</code> library as a toolkit for processing data. We learned the <code>DataFrame</code> and <code>Series</code> data structures, familiarized ourselves with the basic syntax for manipulating tabular data, and began writing our first lines of <code>pandas</code> code.</p>
 <p>In this lecture, we’ll start to dive into some advanced <code>pandas</code> syntax. You may find it helpful to follow along with a notebook of your own as we walk through these new pieces of code.</p>
 <p>We’ll start by loading the <code>babynames</code> dataset.</p>
-<div id="8f1cc58b" class="cell" data-execution_count="1">
+<div id="c74ef04e" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This code pulls census data and loads it into a DataFrame</span></span>
@@ -367,7 +367,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 <p>Conditional selection allows us to select a subset of rows in a <code>DataFrame</code> that satisfy some specified condition.</p>
 <p>To understand how to use conditional selection, we must look at another possible input of the <code>.loc</code> and <code>[]</code> methods – a boolean array, which is simply an array or <code>Series</code> where each element is either <code>True</code> or <code>False</code>. This boolean array must have a length equal to the number of rows in the <code>DataFrame</code>. It will return all rows that correspond to a value of <code>True</code> in the array. We used a very similar technique when performing conditional extraction from a <code>Series</code> in the last lecture.</p>
 <p>To see this in action, let’s select all even-indexed rows in the first 10 rows of our <code>DataFrame</code>.</p>
-<div id="446cd40a" class="cell" data-execution_count="2">
+<div id="b83c746d" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Ask yourself: why is :9 is the correct slice to select the first 10 rows?</span></span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>babynames_first_10_rows <span class="op">=</span> babynames.loc[:<span class="dv">9</span>, :]</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -436,7 +436,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 </div>
 <p>We can perform a similar operation using <code>.loc</code>.</p>
-<div id="2e325b15" class="cell" data-execution_count="3">
+<div id="f7385998" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>babynames_first_10_rows.loc[[<span class="va">True</span>, <span class="va">False</span>, <span class="va">True</span>, <span class="va">False</span>, <span class="va">True</span>, <span class="va">False</span>, <span class="va">True</span>, <span class="va">False</span>, <span class="va">True</span>, <span class="va">False</span>], :]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="3">
 <div>
@@ -502,7 +502,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 <p>These techniques worked well in this example, but you can imagine how tedious it might be to list out <code>True</code> and <code>False</code>for every row in a larger <code>DataFrame</code>. To make things easier, we can instead provide a logical condition as an input to <code>.loc</code> or <code>[]</code> that returns a boolean array with the necessary length.</p>
 <p>For example, to return all names associated with <code>F</code> sex:</p>
-<div id="7e5fe985" class="cell" data-execution_count="4">
+<div id="8b7782ec" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># First, use a logical condition to generate a boolean array</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>logical_operator <span class="op">=</span> (babynames[<span class="st">"Sex"</span>] <span class="op">==</span> <span class="st">"F"</span>)</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -572,7 +572,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 <p>Recall from the previous lecture that <code>.head()</code> will return only the first few rows in the <code>DataFrame</code>. In reality, <code>babynames[logical operator]</code> contains as many rows as there are entries in the original <code>babynames</code> <code>DataFrame</code> with sex <code>"F"</code>.</p>
 <p>Here, <code>logical_operator</code> evaluates to a <code>Series</code> of boolean values with length 407428.</p>
-<div id="f23d3dae" class="cell" data-execution_count="5">
+<div id="c5bbcf79" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"There are a total of </span><span class="sc">{}</span><span class="st"> values in 'logical_operator'"</span>.<span class="bu">format</span>(<span class="bu">len</span>(logical_operator)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -582,7 +582,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 </div>
 <p>Rows starting at row 0 and ending at row 239536 evaluate to <code>True</code> and are thus returned in the <code>DataFrame</code>. Rows from 239537 onwards evaluate to <code>False</code> and are omitted from the output.</p>
-<div id="20c23cc5" class="cell" data-execution_count="6">
+<div id="238d9ba3" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"The 0th item in this 'logical_operator' is: </span><span class="sc">{}</span><span class="st">"</span>.<span class="bu">format</span>(logical_operator.iloc[<span class="dv">0</span>]))</span>
@@ -597,7 +597,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 <p>Passing a <code>Series</code> as an argument to <code>babynames[]</code> has the same effect as using a boolean array. In fact, the <code>[]</code> selection operator can take a boolean <code>Series</code>, array, and list as arguments. These three are used interchangeably throughout the course.</p>
 <p>We can also use <code>.loc</code> to achieve similar results.</p>
-<div id="6e50def8" class="cell" data-execution_count="7">
+<div id="76e3b096" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>babynames.loc[babynames[<span class="st">"Sex"</span>] <span class="op">==</span> <span class="st">"F"</span>].head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <div>
@@ -695,7 +695,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </table>
 <p>When combining multiple conditions with logical operators, we surround each individual condition with a set of parenthesis <code>()</code>. This imposes an order of operations on <code>pandas</code> evaluating your logic and can avoid code erroring.</p>
 <p>For example, if we want to return data on all names with sex <code>"F"</code> born before the year 2000, we can write:</p>
-<div id="ced1a2ca" class="cell" data-execution_count="8">
+<div id="8aed57cc" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>babynames[(babynames[<span class="st">"Sex"</span>] <span class="op">==</span> <span class="st">"F"</span>) <span class="op">&amp;</span> (babynames[<span class="st">"Year"</span>] <span class="op">&lt;</span> <span class="dv">2000</span>)].head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <div>
@@ -760,12 +760,12 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 </div>
 <p>Note that we’re working with <code>Series</code>, so using <code>and</code> in place of <code>&amp;</code>, or <code>or</code> in place <code>|</code> will error.</p>
-<div id="dee54466" class="cell" data-execution_count="9">
+<div id="fe69bb56" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This line of code will raise a ValueError</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="co"># babynames[(babynames["Sex"] == "F") and (babynames["Year"] &lt; 2000)].head()</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>If we want to return data on all names with sex <code>"F"</code> <em>or</em> all born before the year 2000, we can write:</p>
-<div id="d664ec09" class="cell" data-execution_count="10">
+<div id="dae8aafb" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>babynames[(babynames[<span class="st">"Sex"</span>] <span class="op">==</span> <span class="st">"F"</span>) <span class="op">|</span> (babynames[<span class="st">"Year"</span>] <span class="op">&lt;</span> <span class="dv">2000</span>)].head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
 <div>
@@ -830,7 +830,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 </div>
 <p>Boolean array selection is a useful tool, but can lead to overly verbose code for complex conditions. In the example below, our boolean condition is long enough to extend for several lines of code.</p>
-<div id="83e50354" class="cell" data-execution_count="11">
+<div id="9772aef0" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Note: The parentheses surrounding the code make it possible to break the code on to multiple lines for readability</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>(</span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>    babynames[(babynames[<span class="st">"Name"</span>] <span class="op">==</span> <span class="st">"Bella"</span>) <span class="op">|</span> </span>
@@ -902,7 +902,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 <p>Fortunately, <code>pandas</code> provides many alternative methods for constructing boolean filters.</p>
 <p>The <code>.isin</code> function is one such example. This method evaluates if the values in a <code>Series</code> are contained in a different sequence (list, array, or <code>Series</code>) of values. In the cell below, we achieve equivalent results to the <code>DataFrame</code> above with far more concise code.</p>
-<div id="8dfdbf01" class="cell" data-execution_count="12">
+<div id="1509104b" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>names <span class="op">=</span> [<span class="st">"Bella"</span>, <span class="st">"Alex"</span>, <span class="st">"Narges"</span>, <span class="st">"Lisa"</span>]</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Name"</span>].isin(names).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
@@ -914,7 +914,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 Name: Name, dtype: bool</code></pre>
 </div>
 </div>
-<div id="31d4098d" class="cell" data-execution_count="13">
+<div id="55488d21" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>babynames[babynames[<span class="st">"Name"</span>].isin(names)].head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
 <div>
@@ -979,7 +979,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 </div>
 </div>
 <p>The function <code>str.startswith</code> can be used to define a filter based on string values in a <code>Series</code> object. It checks to see if string values in a <code>Series</code> start with a particular character.</p>
-<div id="ed80a0f5" class="cell" data-execution_count="14">
+<div id="20878c65" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Identify whether names begin with the letter "N"</span></span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Name"</span>].<span class="bu">str</span>.startswith(<span class="st">"N"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
@@ -991,7 +991,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 Name: Name, dtype: bool</code></pre>
 </div>
 </div>
-<div id="4fccb577" class="cell" data-execution_count="15">
+<div id="60f5f75b" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Extracting names that begin with the letter "N"</span></span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>babynames[babynames[<span class="st">"Name"</span>].<span class="bu">str</span>.startswith(<span class="st">"N"</span>)].head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="15">
@@ -1061,7 +1061,7 @@ <h2 data-number="3.1" class="anchored" data-anchor-id="conditional-selection"><s
 <h2 data-number="3.2" class="anchored" data-anchor-id="adding-removing-and-modifying-columns"><span class="header-section-number">3.2</span> Adding, Removing, and Modifying Columns</h2>
 <p>In many data science tasks, we may need to change the columns contained in our <code>DataFrame</code> in some way. Fortunately, the syntax to do so is fairly straightforward.</p>
 <p>To add a new column to a <code>DataFrame</code>, we use a syntax similar to that used when accessing an existing column. Specify the name of the new column by writing <code>df["column"]</code>, then assign this to a <code>Series</code> or array containing the values that will populate this column.</p>
-<div id="dfed22dd" class="cell" data-execution_count="16">
+<div id="49aac933" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a Series of the length of each name. </span></span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>babyname_lengths <span class="op">=</span> babynames[<span class="st">"Name"</span>].<span class="bu">str</span>.<span class="bu">len</span>()</span>
 <span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1137,7 +1137,7 @@ <h2 data-number="3.2" class="anchored" data-anchor-id="adding-removing-and-modif
 </div>
 </div>
 <p>If we need to later modify an existing column, we can do so by referencing this column again with the syntax <code>df["column"]</code>, then re-assigning it to a new <code>Series</code> or array of the appropriate length.</p>
-<div id="773a56d3" class="cell" data-execution_count="17">
+<div id="08dfe184" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Modify the “name_lengths” column to be one less than its original value</span></span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"name_lengths"</span>] <span class="op">=</span> babynames[<span class="st">"name_lengths"</span>] <span class="op">-</span> <span class="dv">1</span></span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>babynames.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1210,7 +1210,7 @@ <h2 data-number="3.2" class="anchored" data-anchor-id="adding-removing-and-modif
 </div>
 </div>
 <p>We can rename a column using the <code>.rename()</code> method. It takes in a dictionary that maps old column names to their new ones.</p>
-<div id="06881350" class="cell" data-execution_count="18">
+<div id="b9cfd349" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename “name_lengths” to “Length”</span></span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>babynames <span class="op">=</span> babynames.rename(columns<span class="op">=</span>{<span class="st">"name_lengths"</span>:<span class="st">"Length"</span>})</span>
 <span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>babynames.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1283,7 +1283,7 @@ <h2 data-number="3.2" class="anchored" data-anchor-id="adding-removing-and-modif
 </div>
 </div>
 <p>If we want to remove a column or row of a <code>DataFrame</code>, we can call the <code>.drop</code> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html">(documentation)</a> method. Use the <code>axis</code> parameter to specify whether a column or row should be dropped. Unless otherwise specified, <code>pandas</code> will assume that we are dropping a row by default.</p>
-<div id="1eb1818f" class="cell" data-execution_count="19">
+<div id="c726a720" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop our new "Length" column from the DataFrame</span></span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>babynames <span class="op">=</span> babynames.drop(<span class="st">"Length"</span>, axis<span class="op">=</span><span class="st">"columns"</span>)</span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>babynames.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1351,7 +1351,7 @@ <h2 data-number="3.2" class="anchored" data-anchor-id="adding-removing-and-modif
 </div>
 <p>Notice that we <em>re-assigned</em> <code>babynames</code> to the result of <code>babynames.drop(...)</code>. This is a subtle but important point: <code>pandas</code> table operations <strong>do not occur in-place</strong>. Calling <code>df.drop(...)</code> will output a <em>copy</em> of <code>df</code> with the row/column of interest removed without modifying the original <code>df</code> table.</p>
 <p>In other words, if we simply call:</p>
-<div id="af00efda" class="cell" data-execution_count="20">
+<div id="495bfe31" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This creates a copy of `babynames` and removes the column "Name"...</span></span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>babynames.drop(<span class="st">"Name"</span>, axis<span class="op">=</span><span class="st">"columns"</span>)</span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1439,7 +1439,7 @@ <h2 data-number="3.3" class="anchored" data-anchor-id="useful-utility-functions"
 <section id="numpy" class="level3" data-number="3.3.1">
 <h3 data-number="3.3.1" class="anchored" data-anchor-id="numpy"><span class="header-section-number">3.3.1</span> <code>NumPy</code></h3>
 <p><code>pandas</code> is designed to work well with <code>NumPy</code>, the framework for array computations you encountered in <a href="https://www.data8.org/su23/reference/#array-functions-and-methods">Data 8</a>. Just about any <code>NumPy</code> function can be applied to <code>pandas</code> <code>DataFrame</code>s and <code>Series</code>.</p>
-<div id="ed50bc7d" class="cell" data-execution_count="21">
+<div id="a0065f4c" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pull out the number of babies named Yash each year</span></span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>yash_count <span class="op">=</span> babynames[babynames[<span class="st">"Name"</span>] <span class="op">==</span> <span class="st">"Yash"</span>][<span class="st">"Count"</span>]</span>
 <span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>yash_count.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1452,14 +1452,14 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="numpy"><span class="hea
 Name: Count, dtype: int64</code></pre>
 </div>
 </div>
-<div id="5c1c2046" class="cell" data-execution_count="22">
+<div id="c40be055" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Average number of babies named Yash each year</span></span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>np.mean(yash_count)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
 <pre><code>17.142857142857142</code></pre>
 </div>
 </div>
-<div id="8283f90f" class="cell" data-execution_count="23">
+<div id="fb2249fa" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Max number of babies named Yash born in any one year</span></span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>np.<span class="bu">max</span>(yash_count)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
@@ -1471,14 +1471,14 @@ <h3 data-number="3.3.1" class="anchored" data-anchor-id="numpy"><span class="hea
 <h3 data-number="3.3.2" class="anchored" data-anchor-id="shape-and-.size"><span class="header-section-number">3.3.2</span> <code>.shape</code> and <code>.size</code></h3>
 <p><code>.shape</code> and <code>.size</code> are attributes of <code>Series</code> and <code>DataFrame</code>s that measure the “amount” of data stored in the structure. Calling <code>.shape</code> returns a tuple containing the number of rows and columns present in the <code>DataFrame</code> or <code>Series</code>. <code>.size</code> is used to find the total number of elements in a structure, equivalent to the number of rows times the number of columns.</p>
 <p>Many functions strictly require the dimensions of the arguments along certain axes to match. Calling these dimension-finding functions is much faster than counting all of the items by hand.</p>
-<div id="77b09d89" class="cell" data-execution_count="24">
+<div id="bf3f2d3d" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Return the shape of the DataFrame, in the format (num_rows, num_columns)</span></span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>babynames.shape</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="24">
 <pre><code>(407428, 5)</code></pre>
 </div>
 </div>
-<div id="966d0b55" class="cell" data-execution_count="25">
+<div id="f2ff225b" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Return the size of the DataFrame, equal to num_rows * num_columns</span></span>
 <span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>babynames.size</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="25">
@@ -1489,7 +1489,7 @@ <h3 data-number="3.3.2" class="anchored" data-anchor-id="shape-and-.size"><span
 <section id="describe" class="level3" data-number="3.3.3">
 <h3 data-number="3.3.3" class="anchored" data-anchor-id="describe"><span class="header-section-number">3.3.3</span> <code>.describe()</code></h3>
 <p>If many statistics are required from a <code>DataFrame</code> (minimum value, maximum value, mean value, etc.), then <code>.describe()</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html">(documentation)</a> can be used to compute all of them at once.</p>
-<div id="c64f7e3b" class="cell" data-execution_count="26">
+<div id="260b59cb" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>babynames.describe()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
 <div>
@@ -1551,7 +1551,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="describe"><span class="
 </div>
 </div>
 <p>A different set of statistics will be reported if <code>.describe()</code> is called on a <code>Series</code>.</p>
-<div id="943b8147" class="cell" data-execution_count="27">
+<div id="10af5f36" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Sex"</span>].describe()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="27">
 <pre><code>count     407428
@@ -1566,7 +1566,7 @@ <h3 data-number="3.3.3" class="anchored" data-anchor-id="describe"><span class="
 <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="header-section-number">3.3.4</span> <code>.sample()</code></h3>
 <p>As we will see later in the semester, random processes are at the heart of many data science techniques (for example, train-test splits, bootstrapping, and cross-validation). <code>.sample()</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html">(documentation)</a> lets us quickly select random entries (a row if called from a <code>DataFrame</code>, or a value if called from a <code>Series</code>).</p>
 <p>By default, <code>.sample()</code> selects entries <em>without</em> replacement. Pass in the argument <code>replace=True</code> to sample with replacement.</p>
-<div id="2311446c" class="cell" data-execution_count="28">
+<div id="4441fbf9" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Sample a single row</span></span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>babynames.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="28">
@@ -1586,12 +1586,12 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </thead>
 <tbody>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">289131</td>
+<td data-quarto-table-cell-role="th">218266</td>
 <td>CA</td>
-<td>M</td>
-<td>1974</td>
-<td>Conrad</td>
-<td>20</td>
+<td>F</td>
+<td>2017</td>
+<td>Calliope</td>
+<td>40</td>
 </tr>
 </tbody>
 </table>
@@ -1600,7 +1600,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </div>
 </div>
 <p>Naturally, this can be chained with other methods and operators (<code>iloc</code>, etc.).</p>
-<div id="ec122822" class="cell" data-execution_count="29">
+<div id="fc7b9d2f" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Sample 5 random rows, and select all columns after column 2</span></span>
 <span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>babynames.sample(<span class="dv">5</span>).iloc[:, <span class="dv">2</span>:]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
@@ -1618,34 +1618,34 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </thead>
 <tbody>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">37643</td>
-<td>1957</td>
-<td>Kristy</td>
-<td>40</td>
+<td data-quarto-table-cell-role="th">89270</td>
+<td>1982</td>
+<td>Suzette</td>
+<td>29</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">36448</td>
-<td>1956</td>
-<td>Adriana</td>
-<td>13</td>
+<td data-quarto-table-cell-role="th">290263</td>
+<td>1975</td>
+<td>Lloyd</td>
+<td>61</td>
 </tr>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">167877</td>
-<td>2004</td>
-<td>Alonna</td>
-<td>5</td>
+<td data-quarto-table-cell-role="th">320664</td>
+<td>1991</td>
+<td>Malik</td>
+<td>26</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">377063</td>
-<td>2012</td>
-<td>Advik</td>
-<td>12</td>
+<td data-quarto-table-cell-role="th">106120</td>
+<td>1988</td>
+<td>Annette</td>
+<td>113</td>
 </tr>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">212060</td>
-<td>2015</td>
-<td>Pyper</td>
-<td>9</td>
+<td data-quarto-table-cell-role="th">257630</td>
+<td>1945</td>
+<td>Sammy</td>
+<td>46</td>
 </tr>
 </tbody>
 </table>
@@ -1653,7 +1653,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </div>
 </div>
 </div>
-<div id="f23c4aa6" class="cell" data-execution_count="30">
+<div id="d690c1ad" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Randomly sample 4 names from the year 2000, with replacement, and select all columns after column 2</span></span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>babynames[babynames[<span class="st">"Year"</span>] <span class="op">==</span> <span class="dv">2000</span>].sample(<span class="dv">4</span>, replace <span class="op">=</span> <span class="va">True</span>).iloc[:, <span class="dv">2</span>:]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
@@ -1671,28 +1671,28 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 </thead>
 <tbody>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">151968</td>
+<td data-quarto-table-cell-role="th">342774</td>
 <td>2000</td>
-<td>Averie</td>
-<td>6</td>
+<td>Alvin</td>
+<td>111</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">150671</td>
+<td data-quarto-table-cell-role="th">150041</td>
 <td>2000</td>
-<td>Darcy</td>
-<td>14</td>
+<td>Kacey</td>
+<td>30</td>
 </tr>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">343634</td>
+<td data-quarto-table-cell-role="th">344634</td>
 <td>2000</td>
-<td>Aydin</td>
-<td>12</td>
+<td>Anwar</td>
+<td>5</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">342976</td>
+<td data-quarto-table-cell-role="th">343348</td>
 <td>2000</td>
-<td>Stefan</td>
-<td>46</td>
+<td>Leopoldo</td>
+<td>19</td>
 </tr>
 </tbody>
 </table>
@@ -1705,7 +1705,7 @@ <h3 data-number="3.3.4" class="anchored" data-anchor-id="sample"><span class="he
 <h3 data-number="3.3.5" class="anchored" data-anchor-id="value_counts"><span class="header-section-number">3.3.5</span> <code>.value_counts()</code></h3>
 <p>The <code>Series.value_counts()</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html">(documentation)</a> method counts the number of occurrence of each unique value in a <code>Series</code>. In other words, it <em>counts</em> the number of times each unique <em>value</em> appears. This is often useful for determining the most or least common entries in a <code>Series</code>.</p>
 <p>In the example below, we can determine the name with the most years in which at least one person has taken that name by counting the number of times each name appears in the <code>"Name"</code> column of <code>babynames</code>. Note that the return value is also a <code>Series</code>.</p>
-<div id="24adede5" class="cell" data-execution_count="31">
+<div id="7a93069d" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Name"</span>].value_counts().head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
 <pre><code>Name
@@ -1721,7 +1721,7 @@ <h3 data-number="3.3.5" class="anchored" data-anchor-id="value_counts"><span cla
 <section id="unique" class="level3" data-number="3.3.6">
 <h3 data-number="3.3.6" class="anchored" data-anchor-id="unique"><span class="header-section-number">3.3.6</span> <code>.unique()</code></h3>
 <p>If we have a <code>Series</code> with many repeated values, then <code>.unique()</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.unique.html">(documentation)</a> can be used to identify only the <em>unique</em> values. Here we return an array of all the names in <code>babynames</code>.</p>
-<div id="87b4c3dd" class="cell" data-execution_count="32">
+<div id="6e0f8cb7" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Name"</span>].unique()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="32">
 <pre><code>array(['Mary', 'Helen', 'Dorothy', ..., 'Zae', 'Zai', 'Zayvier'],
@@ -1732,7 +1732,7 @@ <h3 data-number="3.3.6" class="anchored" data-anchor-id="unique"><span class="he
 <section id="sort_values" class="level3" data-number="3.3.7">
 <h3 data-number="3.3.7" class="anchored" data-anchor-id="sort_values"><span class="header-section-number">3.3.7</span> <code>.sort_values()</code></h3>
 <p>Ordering a <code>DataFrame</code> can be useful for isolating extreme values. For example, the first 5 entries of a row sorted in descending order (that is, from highest to lowest) are the largest 5 values. <code>.sort_values</code> <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html">(documentation)</a> allows us to order a <code>DataFrame</code> or <code>Series</code> by a specified column. We can choose to either receive the rows in <code>ascending</code> order (default) or <code>descending</code> order.</p>
-<div id="b813ad77" class="cell" data-execution_count="33">
+<div id="9d33ef0e" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Sort the "Count" column from highest to lowest</span></span>
 <span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a>babynames.sort_values(by<span class="op">=</span><span class="st">"Count"</span>, ascending<span class="op">=</span><span class="va">False</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="33">
@@ -1798,7 +1798,7 @@ <h3 data-number="3.3.7" class="anchored" data-anchor-id="sort_values"><span clas
 </div>
 </div>
 <p>Unlike when calling <code>.value_counts()</code> on a <code>DataFrame</code>, we do not need to explicitly specify the column used for sorting when calling <code>.value_counts()</code> on a <code>Series</code>. We can still specify the ordering paradigm – that is, whether values are sorted in ascending or descending order.</p>
-<div id="16af28aa" class="cell" data-execution_count="34">
+<div id="3ae14f3b" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Sort the "Name" Series alphabetically</span></span>
 <span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"Name"</span>].sort_values(ascending<span class="op">=</span><span class="va">True</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="34">
diff --git a/docs/pandas_3/pandas_3.html b/docs/pandas_3/pandas_3.html
index 333a2c5c..a617c2fa 100644
--- a/docs/pandas_3/pandas_3.html
+++ b/docs/pandas_3/pandas_3.html
@@ -305,7 +305,7 @@ <h2 id="toc-title">Pandas III</h2>
 <h2 data-number="4.1" class="anchored" data-anchor-id="custom-sorts"><span class="header-section-number">4.1</span> Custom Sorts</h2>
 <p>First, let’s finish our discussion about sorting. Let’s try to solve a sorting problem using different approaches. Assume we want to find the longest baby names and sort our data accordingly.</p>
 <p>We’ll start by loading the <code>babynames</code> dataset. Note that this dataset is filtered to only contain data from California.</p>
-<div id="c3649bde" class="cell" data-execution_count="1">
+<div id="85555765" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This code pulls census data and loads it into a DataFrame</span></span>
@@ -436,7 +436,7 @@ <h2 data-number="4.1" class="anchored" data-anchor-id="custom-sorts"><span class
 <section id="approach-1-create-a-temporary-column" class="level3" data-number="4.1.1">
 <h3 data-number="4.1.1" class="anchored" data-anchor-id="approach-1-create-a-temporary-column"><span class="header-section-number">4.1.1</span> Approach 1: Create a Temporary Column</h3>
 <p>One method to do this is to first start by creating a column that contains the lengths of the names.</p>
-<div id="6e8296fa" class="cell" data-execution_count="2">
+<div id="a0fa68b2" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a Series of the length of each name</span></span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>babyname_lengths <span class="op">=</span> babynames[<span class="st">"Name"</span>].<span class="bu">str</span>.<span class="bu">len</span>()</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -512,7 +512,7 @@ <h3 data-number="4.1.1" class="anchored" data-anchor-id="approach-1-create-a-tem
 </div>
 </div>
 <p>We can then sort the <code>DataFrame</code> by that column using <code>.sort_values()</code>:</p>
-<div id="c069be11" class="cell" data-execution_count="3">
+<div id="38907644" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Sort by the temporary column</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>babynames <span class="op">=</span> babynames.sort_values(by<span class="op">=</span><span class="st">"name_lengths"</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>babynames.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -585,7 +585,7 @@ <h3 data-number="4.1.1" class="anchored" data-anchor-id="approach-1-create-a-tem
 </div>
 </div>
 <p>Finally, we can drop the <code>name_length</code> column from <code>babynames</code> to prevent our table from getting cluttered.</p>
-<div id="96eb664e" class="cell" data-execution_count="4">
+<div id="522005ef" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop the 'name_length' column</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>babynames <span class="op">=</span> babynames.drop(<span class="st">"name_lengths"</span>, axis<span class="op">=</span><span class="st">'columns'</span>)</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>babynames.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -655,7 +655,7 @@ <h3 data-number="4.1.1" class="anchored" data-anchor-id="approach-1-create-a-tem
 <section id="approach-2-sorting-using-the-key-argument" class="level3" data-number="4.1.2">
 <h3 data-number="4.1.2" class="anchored" data-anchor-id="approach-2-sorting-using-the-key-argument"><span class="header-section-number">4.1.2</span> Approach 2: Sorting using the <code>key</code> Argument</h3>
 <p>Another way to approach this is to use the <code>key</code> argument of <code>.sort_values()</code>. Here we can specify that we want to sort <code>"Name"</code> values by their length.</p>
-<div id="10cb8599" class="cell" data-execution_count="5">
+<div id="115dc3d1" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>babynames.sort_values(<span class="st">"Name"</span>, key<span class="op">=</span><span class="kw">lambda</span> x: x.<span class="bu">str</span>.<span class="bu">len</span>(), ascending<span class="op">=</span><span class="va">False</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="5">
 <div>
@@ -723,7 +723,7 @@ <h3 data-number="4.1.2" class="anchored" data-anchor-id="approach-2-sorting-usin
 <section id="approach-3-sorting-using-the-map-function" class="level3" data-number="4.1.3">
 <h3 data-number="4.1.3" class="anchored" data-anchor-id="approach-3-sorting-using-the-map-function"><span class="header-section-number">4.1.3</span> Approach 3: Sorting using the <code>map</code> Function</h3>
 <p>We can also use the <code>map</code> function on a <code>Series</code> to solve this. Say we want to sort the <code>babynames</code> table by the number of <code>"dr"</code>’s and <code>"ea"</code>’s in each <code>"Name"</code>. We’ll define the function <code>dr_ea_count</code> to help us out.</p>
-<div id="beba0998" class="cell" data-execution_count="6">
+<div id="274bc95c" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># First, define a function to count the number of times "dr" or "ea" appear in each name</span></span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> dr_ea_count(string):</span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> string.count(<span class="st">'dr'</span>) <span class="op">+</span> string.count(<span class="st">'ea'</span>)</span>
@@ -803,7 +803,7 @@ <h3 data-number="4.1.3" class="anchored" data-anchor-id="approach-3-sorting-usin
 </div>
 </div>
 <p>We can drop the <code>dr_ea_count</code> once we’re done using it to maintain a neat table.</p>
-<div id="d7a78625" class="cell" data-execution_count="7">
+<div id="7a7ed48e" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Drop the `dr_ea_count` column</span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>babynames <span class="op">=</span> babynames.drop(<span class="st">"dr_ea_count"</span>, axis <span class="op">=</span> <span class="st">'columns'</span>)</span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>babynames.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -875,10 +875,10 @@ <h3 data-number="4.1.3" class="anchored" data-anchor-id="approach-3-sorting-usin
 <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.groupby"><span class="header-section-number">4.2</span> Aggregating Data with <code>.groupby</code></h2>
 <p>Up until this point, we have been working with individual rows of <code>DataFrame</code>s. As data scientists, we often wish to investigate trends across a larger <em>subset</em> of our data. For example, we may want to compute some summary statistic (the mean, median, sum, etc.) for a group of rows in our <code>DataFrame</code>. To do this, we’ll use <code>pandas</code> <code>GroupBy</code> objects. Our goal is to group together rows that fall under the same category and perform an operation that aggregates across all rows in the category.</p>
 <p>Let’s say we wanted to aggregate all rows in <code>babynames</code> for a given year.</p>
-<div id="5990bd7a" class="cell" data-execution_count="8">
+<div id="4f8329d6" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Year"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
-<pre><code>&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x13cf09a90&gt;</code></pre>
+<pre><code>&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x28805ee90&gt;</code></pre>
 </div>
 </div>
 <p>What does this strange output mean? Calling <code>.groupby</code> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html">(documentation)</a> has generated a <code>GroupBy</code> object. You can imagine this as a set of “mini” sub-<code>DataFrame</code>s, where each subframe contains all of the rows from <code>babynames</code> that correspond to a particular year.</p>
@@ -888,7 +888,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 </center>
 <p>We can’t work with a <code>GroupBy</code> object directly – that is why you saw that strange output earlier rather than a standard view of a <code>DataFrame</code>. To actually manipulate values within these “mini” <code>DataFrame</code>s, we’ll need to call an <em>aggregation method</em>. This is a method that tells <code>pandas</code> how to aggregate the values within the <code>GroupBy</code> object. Once the aggregation is applied, <code>pandas</code> will return a normal (now grouped) <code>DataFrame</code>.</p>
 <p>The first aggregation method we’ll consider is <code>.agg</code>. The <code>.agg</code> method takes in a function as its argument; this function is then applied to each column of a “mini” grouped DataFrame. We end up with a new <code>DataFrame</code> with one aggregated row per subframe. Let’s see this in action by finding the <code>sum</code> of all counts for each year in <code>babynames</code> – this is equivalent to finding the number of babies born in each year.</p>
-<div id="54503fd2" class="cell" data-execution_count="9">
+<div id="5fc21af4" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>babynames[[<span class="st">"Year"</span>, <span class="st">"Count"</span>]].groupby(<span class="st">"Year"</span>).agg(<span class="st">"sum"</span>).head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
 <div>
@@ -941,7 +941,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 </div>
 <p>Calling <code>.agg</code> has condensed each subframe back into a single row. This gives us our final output: a <code>DataFrame</code> that is now indexed by <code>"Year"</code>, with a single row for each unique year in the original <code>babynames</code> DataFrame.</p>
 <p>There are many different aggregation functions we can use, all of which are useful in different applications.</p>
-<div id="b43b10ab" class="cell" data-execution_count="10">
+<div id="13f8e235" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>babynames[[<span class="st">"Year"</span>, <span class="st">"Count"</span>]].groupby(<span class="st">"Year"</span>).agg(<span class="st">"min"</span>).head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
 <div>
@@ -985,7 +985,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 </div>
 </div>
 </div>
-<div id="9cc6f324" class="cell" data-execution_count="11">
+<div id="5a8ebc6b" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>babynames[[<span class="st">"Year"</span>, <span class="st">"Count"</span>]].groupby(<span class="st">"Year"</span>).agg(<span class="st">"max"</span>).head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
 <div>
@@ -1029,7 +1029,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 </div>
 </div>
 </div>
-<div id="ad238452" class="cell" data-execution_count="12">
+<div id="a21467b1" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Same result, but now we explicitly tell pandas to only consider the "Count" column when summing</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Year"</span>)[[<span class="st">"Count"</span>]].agg(<span class="st">"sum"</span>).head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
@@ -1083,7 +1083,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions"><span class="header-section-number">4.2.1</span> Aggregation Functions</h3>
 <p>Because of this fairly broad requirement, <code>pandas</code> offers many ways of computing an aggregation.</p>
 <p><strong>In-built</strong> Python operations – such as <code>sum</code>, <code>max</code>, and <code>min</code> – are automatically recognized by <code>pandas</code>.</p>
-<div id="c4f9be73" class="cell" data-execution_count="13">
+<div id="ff1edae2" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># What is the minimum count for each name in any year?</span></span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Name"</span>)[[<span class="st">"Count"</span>]].agg(<span class="st">"min"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
@@ -1128,7 +1128,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions">
 </div>
 </div>
 </div>
-<div id="9d946254" class="cell" data-execution_count="14">
+<div id="bd2a9f46" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># What is the largest single-year count of each name?</span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Name"</span>)[[<span class="st">"Count"</span>]].agg(<span class="st">"max"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
@@ -1174,7 +1174,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions">
 </div>
 </div>
 <p>As mentioned previously, functions from the <code>NumPy</code> library, such as <code>np.mean</code>, <code>np.max</code>, <code>np.min</code>, and <code>np.sum</code>, are also fair game in <code>pandas</code>.</p>
-<div id="c2ccbec1" class="cell" data-execution_count="15">
+<div id="5ff4bb7a" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># What is the average count for each name across all years?</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Name"</span>)[[<span class="st">"Count"</span>]].agg(<span class="st">"mean"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="15">
@@ -1230,7 +1230,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions">
 </ul>
 <p>The latter two entries in this list – <code>"first"</code> and <code>"last"</code> – are unique to <code>pandas</code>. They return the first or last entry in a subframe column. Why might this be useful? Consider a case where <em>multiple</em> columns in a group share identical information. To represent this information in the grouped output, we can simply grab the first or last entry, which we know will be identical to all other entries.</p>
 <p>Let’s illustrate this with an example. Say we add a new column to <code>babynames</code> that contains the first letter of each name.</p>
-<div id="27f3ed85" class="cell" data-execution_count="16">
+<div id="28d07f2a" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Imagine we had an additional column, "First Letter". We'll explain this code next week</span></span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>babynames[<span class="st">"First Letter"</span>] <span class="op">=</span> babynames[<span class="st">"Name"</span>].<span class="bu">str</span>[<span class="dv">0</span>]</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1295,7 +1295,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions">
 <figcaption>Aggregating using “first”</figcaption>
 </figure>
 </div>
-<div id="9b38618c" class="cell" data-execution_count="17">
+<div id="2de6065f" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>babynames_new.groupby(<span class="st">"Name"</span>).agg({<span class="st">"First Letter"</span>:<span class="st">"first"</span>, <span class="st">"Year"</span>:<span class="st">"max"</span>}).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
 <div>
@@ -1350,7 +1350,7 @@ <h3 data-number="4.2.1" class="anchored" data-anchor-id="aggregation-functions">
 <section id="plotting-birth-counts" class="level3" data-number="4.2.2">
 <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts"><span class="header-section-number">4.2.2</span> Plotting Birth Counts</h3>
 <p>Let’s use <code>.agg</code> to find the total number of babies born in each year. Recall that using <code>.agg</code> with <code>.groupby()</code> follows the format: <code>df.groupby(column_name).agg(aggregation_function)</code>. The line of code below gives us the total number of babies born in each year.</p>
-<div id="4665ce62" class="cell" data-execution_count="18">
+<div id="94417bb1" class="cell" data-execution_count="18">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Year"</span>)[[<span class="st">"Count"</span>]].agg(<span class="bu">sum</span>).head(<span class="dv">5</span>)</span>
@@ -1360,7 +1360,7 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts">
 <span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="co"># babynames.groupby("Year").sum(numeric_only=True)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94345/390646742.py:1: FutureWarning:
+<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/390646742.py:1: FutureWarning:
 
 The provided callable &lt;built-in function sum&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
 </code></pre>
@@ -1410,7 +1410,7 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts">
 <p>Here’s an illustration of the process:</p>
 <p><img src="images/aggregation.png" alt="aggregation" width="600"></p>
 <p>Plotting the <code>Dataframe</code> we obtain tells an interesting story.</p>
-<div id="b151a4fb" class="cell" data-execution_count="19">
+<div id="5b6638bf" class="cell" data-execution_count="19">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
@@ -1418,9 +1418,9 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts">
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>px.line(puzzle2, y <span class="op">=</span> <span class="st">"Count"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
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@@ -1464,7 +1464,7 @@ <h3 data-number="4.2.4" class="anchored" data-anchor-id="revisiting-the-.agg-fun
 <pre><code>babynames.groupby("Year").mean().head()</code></pre>
 <p>We can now put this all into practice. Say we want to find the baby name with sex “F” that has fallen in popularity the most in California. To calculate this, we can first create a metric: “Ratio to Peak” (RTP). The RTP is the ratio of babies born with a given name in 2022 to the <em>maximum</em> number of babies born with the name in <em>any</em> year.</p>
 <p>Let’s start with calculating this for one baby, “Jennifer”.</p>
-<div id="865e149c" class="cell" data-execution_count="20">
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 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># We filter by babies with sex "F" and sort by "Year"</span></span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>f_babynames <span class="op">=</span> babynames[babynames[<span class="st">"Sex"</span>] <span class="op">==</span> <span class="st">"F"</span>]</span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>f_babynames <span class="op">=</span> f_babynames.sort_values([<span class="st">"Year"</span>])</span>
@@ -1483,7 +1483,7 @@ <h3 data-number="4.2.4" class="anchored" data-anchor-id="revisiting-the-.agg-fun
 </div>
 </div>
 <p>By creating a function to calculate RTP and applying it to our <code>DataFrame</code> by using <code>.groupby()</code>, we can easily compute the RTP for all names at once!</p>
-<div id="419823a4" class="cell" data-execution_count="21">
+<div id="3228c36d" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> ratio_to_peak(series):</span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> series.iloc[<span class="op">-</span><span class="dv">1</span>] <span class="op">/</span> <span class="bu">max</span>(series)</span>
 <span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1551,7 +1551,7 @@ <h3 data-number="4.2.5" class="anchored" data-anchor-id="nuisance-columns"><span
 <section id="renaming-columns-after-grouping" class="level3" data-number="4.2.6">
 <h3 data-number="4.2.6" class="anchored" data-anchor-id="renaming-columns-after-grouping"><span class="header-section-number">4.2.6</span> Renaming Columns After Grouping</h3>
 <p>By default, <code>.groupby</code> will not rename any aggregated columns. As we can see in the table above, the aggregated column is still named <code>Count</code> even though it now represents the RTP. For better readability, we can rename <code>Count</code> to <code>Count RTP</code></p>
-<div id="6fea5b2f" class="cell" data-execution_count="22">
+<div id="7f11ef09" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>rtp_table <span class="op">=</span> rtp_table.rename(columns <span class="op">=</span> {<span class="st">"Count"</span>: <span class="st">"Count RTP"</span>})</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>rtp_table</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
@@ -1638,7 +1638,7 @@ <h3 data-number="4.2.6" class="anchored" data-anchor-id="renaming-columns-after-
 <section id="some-data-science-payoff" class="level3" data-number="4.2.7">
 <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payoff"><span class="header-section-number">4.2.7</span> Some Data Science Payoff</h3>
 <p>By sorting <code>rtp_table</code>, we can see the names whose popularity has decreased the most.</p>
-<div id="acff97ab" class="cell" data-execution_count="23">
+<div id="953bfc77" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>rtp_table <span class="op">=</span> rtp_table.rename(columns <span class="op">=</span> {<span class="st">"Count"</span>: <span class="st">"Count RTP"</span>})</span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>rtp_table.sort_values(<span class="st">"Count RTP"</span>).head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
@@ -1691,16 +1691,16 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payof
 </div>
 </div>
 <p>To visualize the above <code>DataFrame</code>, let’s look at the line plot below:</p>
-<div id="71279f7a" class="cell" data-execution_count="24">
+<div id="f30a9d66" class="cell" data-execution_count="24">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
 <span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>px.line(f_babynames[f_babynames[<span class="st">"Name"</span>] <span class="op">==</span> <span class="st">"Debra"</span>], x <span class="op">=</span> <span class="st">"Year"</span>, y <span class="op">=</span> <span class="st">"Count"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
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@@ -1726,7 +1726,7 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payof
 </div>
 </div>
 <p>We can get the list of the top 10 names and then plot popularity with the following code:</p>
-<div id="46691cec" class="cell" data-execution_count="25">
+<div id="2a39df79" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>top10 <span class="op">=</span> rtp_table.sort_values(<span class="st">"Count RTP"</span>).head(<span class="dv">10</span>).index</span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>px.line(</span>
 <span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>    f_babynames[f_babynames[<span class="st">"Name"</span>].isin(top10)], </span>
@@ -1735,9 +1735,9 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payof
 <span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>    color <span class="op">=</span> <span class="st">"Name"</span></span>
 <span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1763,7 +1763,7 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payof
 </div>
 </div>
 <p>As a quick exercise, consider what code would compute the total number of babies with each name.</p>
-<div id="c6566582" class="cell" data-execution_count="26">
+<div id="91c80fac" class="cell" data-execution_count="26">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>babynames.groupby(<span class="st">"Name"</span>)[[<span class="st">"Count"</span>]].agg(<span class="st">"sum"</span>).head()</span>
@@ -1817,7 +1817,7 @@ <h3 data-number="4.2.7" class="anchored" data-anchor-id="some-data-science-payof
 <section id="groupby-continued" class="level2" data-number="4.3">
 <h2 data-number="4.3" class="anchored" data-anchor-id="groupby-continued"><span class="header-section-number">4.3</span> <code>.groupby()</code>, Continued</h2>
 <p>We’ll work with the <code>elections</code> <code>DataFrame</code> again.</p>
-<div id="f42de2c9" class="cell" data-execution_count="27">
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 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -1897,7 +1897,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="groupby-continued"><span
 <section id="raw-groupby-objects" class="level3" data-number="4.3.1">
 <h3 data-number="4.3.1" class="anchored" data-anchor-id="raw-groupby-objects"><span class="header-section-number">4.3.1</span> Raw <code>GroupBy</code> Objects</h3>
 <p>The result of <code>groupby</code> applied to a <code>DataFrame</code> is a <code>DataFrameGroupBy</code> object, <strong>not</strong> a <code>DataFrame</code>.</p>
-<div id="44e5fc14" class="cell" data-execution_count="28">
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 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>grouped_by_year <span class="op">=</span> elections.groupby(<span class="st">"Year"</span>)</span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(grouped_by_year)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1905,14 +1905,14 @@ <h3 data-number="4.3.1" class="anchored" data-anchor-id="raw-groupby-objects"><s
 </div>
 </div>
 <p>There are several ways to look into <code>DataFrameGroupBy</code> objects:</p>
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 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>grouped_by_party <span class="op">=</span> elections.groupby(<span class="st">"Party"</span>)</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>grouped_by_party.groups</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="29">
 <pre><code>{'American': [22, 126], 'American Independent': [115, 119, 124], 'Anti-Masonic': [6], 'Anti-Monopoly': [38], 'Citizens': [127], 'Communist': [89], 'Constitution': [160, 164, 172], 'Constitutional Union': [24], 'Democratic': [2, 4, 8, 10, 13, 14, 17, 20, 28, 29, 34, 37, 39, 45, 47, 52, 55, 57, 64, 70, 74, 77, 81, 83, 86, 91, 94, 97, 100, 105, 108, 111, 114, 116, 118, 123, 129, 134, 137, 140, 144, 151, 158, 162, 168, 176, 178], 'Democratic-Republican': [0, 1], 'Dixiecrat': [103], 'Farmer–Labor': [78], 'Free Soil': [15, 18], 'Green': [149, 155, 156, 165, 170, 177, 181], 'Greenback': [35], 'Independent': [121, 130, 143, 161, 167, 174], 'Liberal Republican': [31], 'Libertarian': [125, 128, 132, 138, 139, 146, 153, 159, 163, 169, 175, 180], 'National Democratic': [50], 'National Republican': [3, 5], 'National Union': [27], 'Natural Law': [148], 'New Alliance': [136], 'Northern Democratic': [26], 'Populist': [48, 61, 141], 'Progressive': [68, 82, 101, 107], 'Prohibition': [41, 44, 49, 51, 54, 59, 63, 67, 73, 75, 99], 'Reform': [150, 154], 'Republican': [21, 23, 30, 32, 33, 36, 40, 43, 46, 53, 56, 60, 65, 69, 72, 79, 80, 84, 87, 90, 96, 98, 104, 106, 109, 112, 113, 117, 120, 122, 131, 133, 135, 142, 145, 152, 157, 166, 171, 173, 179], 'Socialist': [58, 62, 66, 71, 76, 85, 88, 92, 95, 102], 'Southern Democratic': [25], 'States' Rights': [110], 'Taxpayers': [147], 'Union': [93], 'Union Labor': [42], 'Whig': [7, 9, 11, 12, 16, 19]}</code></pre>
 </div>
 </div>
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 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>grouped_by_party.get_group(<span class="st">"Socialist"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <div>
@@ -2040,7 +2040,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="other-groupby-methods">
 <li><a href="https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.count.html#pandas.core.groupby.DataFrameGroupBy.count"><code>.count</code></a>: creates a new <strong><code>DataFrame</code></strong> with the number of entries, excluding missing values.</li>
 </ul>
 <p>Let’s illustrate some examples by creating a <code>DataFrame</code> called <code>df</code>.</p>
-<div id="1fe2f380" class="cell" data-execution_count="31">
+<div id="2f596cc0" class="cell" data-execution_count="31">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame({<span class="st">'letter'</span>:[<span class="st">'A'</span>,<span class="st">'A'</span>,<span class="st">'B'</span>,<span class="st">'C'</span>,<span class="st">'C'</span>,<span class="st">'C'</span>], </span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>                   <span class="st">'num'</span>:[<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>,np.nan,<span class="dv">4</span>], </span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>                   <span class="st">'state'</span>:[np.nan, <span class="st">'tx'</span>, <span class="st">'fl'</span>, <span class="st">'hi'</span>, np.nan, <span class="st">'ak'</span>]})</span>
@@ -2102,7 +2102,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="other-groupby-methods">
 </div>
 </div>
 <p>Note the slight difference between <code>.size()</code> and <code>.count()</code>: while <code>.size()</code> returns a <code>Series</code> and counts the number of entries including the missing values, <code>.count()</code> returns a <code>DataFrame</code> and counts the number of entries in each column <em>excluding missing values</em>.</p>
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 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>df.groupby(<span class="st">"letter"</span>).size()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <pre><code>letter
@@ -2112,7 +2112,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="other-groupby-methods">
 dtype: int64</code></pre>
 </div>
 </div>
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 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>df.groupby(<span class="st">"letter"</span>).count()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <div>
@@ -2154,7 +2154,7 @@ <h3 data-number="4.3.2" class="anchored" data-anchor-id="other-groupby-methods">
 </div>
 </div>
 <p>You might recall that the <code>value_counts()</code> function in the previous note does something similar. It turns out <code>value_counts()</code> and <code>groupby.size()</code> are the same, except <code>value_counts()</code> sorts the resulting <code>Series</code> in descending order automatically.</p>
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+<div id="b9c61de5" class="cell" data-execution_count="34">
 <div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>df[<span class="st">"letter"</span>].value_counts()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="34">
 <pre><code>letter
@@ -2194,7 +2194,7 @@ <h3 data-number="4.3.3" class="anchored" data-anchor-id="filtering-by-group"><sp
 <li>Return all <code>DataFrame</code> rows that correspond to these years</li>
 </ul>
 <p>For each year, we need to find the maximum <code>%</code> among <em>all</em> rows for that year. If this maximum <code>%</code> is lower than 45%, we will tell <code>pandas</code> to keep all rows corresponding to that year.</p>
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 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>elections.groupby(<span class="st">"Year"</span>).<span class="bu">filter</span>(<span class="kw">lambda</span> sf: sf[<span class="st">"%"</span>].<span class="bu">max</span>() <span class="op">&lt;</span> <span class="dv">45</span>).head(<span class="dv">9</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <div>
@@ -2309,10 +2309,10 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 <p>What if we wish to aggregate our <code>DataFrame</code> using a non-standard function – for example, a function of our own design? We can do so by combining <code>.agg</code> with <code>lambda</code> expressions.</p>
 <p>Let’s first consider a puzzle to jog our memory. We will attempt to find the <code>Candidate</code> from each <code>Party</code> with the highest <code>%</code> of votes.</p>
 <p>A naive approach may be to group by the <code>Party</code> column and aggregate by the maximum.</p>
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+<div id="88eef7e2" class="cell" data-execution_count="36">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>elections.groupby(<span class="st">"Party"</span>).agg(<span class="bu">max</span>).head(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94345/4278286395.py:1: FutureWarning:
+<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/4278286395.py:1: FutureWarning:
 
 The provided callable &lt;built-in function max&gt; is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
 </code></pre>
@@ -2440,7 +2440,7 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 <li>Group by <code>Party</code> and select the first row of each sub-<code>DataFrame</code></li>
 </ol>
 <p>While it may seem unintuitive, sorting <code>elections</code> by descending order of <code>%</code> is extremely helpful. If we then group by <code>Party</code>, the first row of each <code>GroupBy</code> object will contain information about the <code>Candidate</code> with the highest voter <code>%</code>.</p>
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 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>elections_sorted_by_percent <span class="op">=</span> elections.sort_values(<span class="st">"%"</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
 <span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>elections_sorted_by_percent.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="37">
@@ -2511,7 +2511,7 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 </div>
 </div>
 </div>
-<div id="a7ff04ec" class="cell" data-execution_count="38">
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 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>elections_sorted_by_percent.groupby(<span class="st">"Party"</span>).agg(<span class="kw">lambda</span> x : x.iloc[<span class="dv">0</span>]).head(<span class="dv">10</span>)</span>
 <span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Equivalent to the below code</span></span>
@@ -2632,7 +2632,7 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 <p>More generally, <code>lambda</code> functions are used to design custom aggregation functions that aren’t pre-defined by Python. The input parameter <code>x</code> to the <code>lambda</code> function is a <code>GroupBy</code> object. Therefore, it should make sense why <code>lambda x : x.iloc[0]</code> selects the first row in each groupby object.</p>
 <p>In fact, there’s a few different ways to approach this problem. Each approach has different tradeoffs in terms of readability, performance, memory consumption, complexity, etc. We’ve given a few examples below.</p>
 <p><strong>Note</strong>: Understanding these alternative solutions is not required. They are given to demonstrate the vast number of problem-solving approaches in <code>pandas</code>.</p>
-<div id="c5ec65d4" class="cell" data-execution_count="39">
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 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Using the idxmax function</span></span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>best_per_party <span class="op">=</span> elections.loc[elections.groupby(<span class="st">'Party'</span>)[<span class="st">'%'</span>].idxmax()]</span>
 <span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a>best_per_party.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2704,7 +2704,7 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 </div>
 </div>
 </div>
-<div id="2c145941" class="cell" data-execution_count="40">
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 <div class="sourceCode cell-code" id="cb49"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Using the .drop_duplicates function</span></span>
 <span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a>best_per_party2 <span class="op">=</span> elections.sort_values(<span class="st">'%'</span>).drop_duplicates([<span class="st">'Party'</span>], keep<span class="op">=</span><span class="st">'last'</span>)</span>
 <span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a>best_per_party2.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2782,7 +2782,7 @@ <h3 data-number="4.3.4" class="anchored" data-anchor-id="aggregation-with-lambda
 <h2 data-number="4.4" class="anchored" data-anchor-id="aggregating-data-with-pivot-tables"><span class="header-section-number">4.4</span> Aggregating Data with Pivot Tables</h2>
 <p>We know now that <code>.groupby</code> gives us the ability to group and aggregate data across our <code>DataFrame</code>. The examples above formed groups using just one column in the <code>DataFrame</code>. It’s possible to group by multiple columns at once by passing in a list of column names to <code>.groupby</code>.</p>
 <p>Let’s consider the <code>babynames</code> dataset again. In this problem, we will find the total number of baby names associated with each sex for each year. To do this, we’ll group by <em>both</em> the <code>"Year"</code> and <code>"Sex"</code> columns.</p>
-<div id="4405f270" class="cell" data-execution_count="41">
+<div id="970ac0d3" class="cell" data-execution_count="41">
 <div class="sourceCode cell-code" id="cb50"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>babynames.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="41">
 <div>
@@ -2852,12 +2852,12 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="aggregating-data-with-piv
 </div>
 </div>
 </div>
-<div id="a1a4969a" class="cell" data-execution_count="42">
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 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Find the total number of baby names associated with each sex for each </span></span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a><span class="co"># year in the data</span></span>
 <span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a>babynames.groupby([<span class="st">"Year"</span>, <span class="st">"Sex"</span>])[[<span class="st">"Count"</span>]].agg(<span class="bu">sum</span>).head(<span class="dv">6</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94345/3186035650.py:3: FutureWarning:
+<pre><code>/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/3186035650.py:3: FutureWarning:
 
 The provided callable &lt;built-in function sum&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
 </code></pre>
@@ -2919,7 +2919,7 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="aggregating-data-with-piv
 <p>Here’s an illustration of the process:</p>
 <p><img src="images/pivot.png" alt="groupby_demo" width="600"></p>
 <p>The best way to understand pivot tables is to see one in action. Let’s return to our original goal of summing the total number of names associated with each combination of year and sex. We’ll call the <code>pandas</code> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html"><code>.pivot_table</code></a> method to create a new table.</p>
-<div id="be09083b" class="cell" data-execution_count="43">
+<div id="9b85655b" class="cell" data-execution_count="43">
 <div class="sourceCode cell-code" id="cb53"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The `pivot_table` method is used to generate a Pandas pivot table</span></span>
 <span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a>babynames.pivot_table(</span>
@@ -2986,7 +2986,7 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="aggregating-data-with-piv
 <li><code>aggfunc = np.sum</code> tells <code>pandas</code> what function to use when aggregating the data specified by <code>values</code>. Here, we are summing the name counts for each pair of <code>"Year"</code> and <code>"Sex"</code></li>
 </ul>
 <p>We can even include multiple values in the index or columns of our pivot tables.</p>
-<div id="4752e26f" class="cell" data-execution_count="44">
+<div id="be589cc6" class="cell" data-execution_count="44">
 <div class="sourceCode cell-code" id="cb54"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>babynames_pivot <span class="op">=</span> babynames.pivot_table(</span>
 <span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a>    index<span class="op">=</span><span class="st">"Year"</span>,     <span class="co"># the rows (turned into index)</span></span>
 <span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a>    columns<span class="op">=</span><span class="st">"Sex"</span>,    <span class="co"># the column values</span></span>
@@ -3075,7 +3075,7 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="aggregating-data-with-piv
 <h2 data-number="4.5" class="anchored" data-anchor-id="joining-tables"><span class="header-section-number">4.5</span> Joining Tables</h2>
 <p>When working on data science projects, we’re unlikely to have absolutely all the data we want contained in a single <code>DataFrame</code> – a real-world data scientist needs to grapple with data coming from multiple sources. If we have access to multiple datasets with related information, we can join two or more tables into a single <code>DataFrame</code>.</p>
 <p>To put this into practice, we’ll revisit the <code>elections</code> dataset.</p>
-<div id="6ac44d02" class="cell" data-execution_count="45">
+<div id="8776770e" class="cell" data-execution_count="45">
 <div class="sourceCode cell-code" id="cb55"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a>elections.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="45">
 <div>
@@ -3147,7 +3147,7 @@ <h2 data-number="4.5" class="anchored" data-anchor-id="joining-tables"><span cla
 </div>
 <p>Say we want to understand the popularity of the names of each presidential candidate in 2022. To do this, we’ll need the combined data of <code>babynames</code> <em>and</em> <code>elections</code>.</p>
 <p>We’ll start by creating a new column containing the first name of each presidential candidate. This will help us join each name in <code>elections</code> to the corresponding name data in <code>babynames</code>.</p>
-<div id="c6c25307" class="cell" data-execution_count="46">
+<div id="54073a8a" class="cell" data-execution_count="46">
 <div class="sourceCode cell-code" id="cb56"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This `str` operation splits each candidate's full name at each </span></span>
 <span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a><span class="co"># blank space, then takes just the candidate's first name</span></span>
 <span id="cb56-3"><a href="#cb56-3" aria-hidden="true" tabindex="-1"></a>elections[<span class="st">"First Name"</span>] <span class="op">=</span> elections[<span class="st">"Candidate"</span>].<span class="bu">str</span>.split().<span class="bu">str</span>[<span class="dv">0</span>]</span>
@@ -3226,7 +3226,7 @@ <h2 data-number="4.5" class="anchored" data-anchor-id="joining-tables"><span cla
 </div>
 </div>
 </div>
-<div id="4745e773" class="cell" data-execution_count="47">
+<div id="d2d246f3" class="cell" data-execution_count="47">
 <div class="sourceCode cell-code" id="cb57"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Here, we'll only consider `babynames` data from 2022</span></span>
 <span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>babynames_2022 <span class="op">=</span> babynames[babynames[<span class="st">"Year"</span>]<span class="op">==</span><span class="dv">2022</span>]</span>
 <span id="cb57-3"><a href="#cb57-3" aria-hidden="true" tabindex="-1"></a>babynames_2022.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3299,7 +3299,7 @@ <h2 data-number="4.5" class="anchored" data-anchor-id="joining-tables"><span cla
 </div>
 </div>
 <p>Now, we’re ready to join the two tables. <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html"><code>pd.merge</code></a> is the <code>pandas</code> method used to join <code>DataFrame</code>s together.</p>
-<div id="11468aac" class="cell" data-execution_count="48">
+<div id="19ed9a10" class="cell" data-execution_count="48">
 <div class="sourceCode cell-code" id="cb58"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb58-1"><a href="#cb58-1" aria-hidden="true" tabindex="-1"></a>merged <span class="op">=</span> pd.merge(left <span class="op">=</span> elections, right <span class="op">=</span> babynames_2022, <span class="op">\</span></span>
 <span id="cb58-2"><a href="#cb58-2" aria-hidden="true" tabindex="-1"></a>                  left_on <span class="op">=</span> <span class="st">"First Name"</span>, right_on <span class="op">=</span> <span class="st">"Name"</span>)</span>
 <span id="cb58-3"><a href="#cb58-3" aria-hidden="true" tabindex="-1"></a>merged.head()</span>
diff --git a/docs/regex/regex.html b/docs/regex/regex.html
index 64333f7b..3e80d518 100644
--- a/docs/regex/regex.html
+++ b/docs/regex/regex.html
@@ -397,7 +397,7 @@ <h2 data-number="6.2" class="anchored" data-anchor-id="python-string-methods"><s
 <section id="canonicalization" class="level3" data-number="6.2.1">
 <h3 data-number="6.2.1" class="anchored" data-anchor-id="canonicalization"><span class="header-section-number">6.2.1</span> Canonicalization</h3>
 <p>Assume we want to merge the given tables.</p>
-<div id="1ee532ee" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="1">
+<div id="aec72b89" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -409,7 +409,7 @@ <h3 data-number="6.2.1" class="anchored" data-anchor-id="canonicalization"><span
 <span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>    county_and_pop <span class="op">=</span> pd.read_csv(f)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
-<div id="554cfd27" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="2">
+<div id="495ea3c5" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>display(county_and_state), display(county_and_pop)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
@@ -492,7 +492,7 @@ <h3 data-number="6.2.1" class="anchored" data-anchor-id="canonicalization"><span
 <section id="canonicalization-with-python-string-manipulation" class="level4" data-number="6.2.1.1">
 <h4 data-number="6.2.1.1" class="anchored" data-anchor-id="canonicalization-with-python-string-manipulation"><span class="header-section-number">6.2.1.1</span> Canonicalization with Python String Manipulation</h4>
 <p>The following function uses Python string manipulation to convert a single county name into canonical form. It does so by eliminating whitespace, punctuation, and unnecessary text.</p>
-<div id="a9034208" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="3">
+<div id="7c50fd3c" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> canonicalize_county(county_name):</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (</span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>        county_name</span>
@@ -510,7 +510,7 @@ <h4 data-number="6.2.1.1" class="anchored" data-anchor-id="canonicalization-with
 </div>
 </div>
 <p>We will use the <code>pandas</code> <code>map</code> function to apply the <code>canonicalize_county</code> function to every row in both <code>DataFrame</code>s. In doing so, we’ll create a new column in each called <code>clean_county_python</code> with the canonical form.</p>
-<div id="1341caf2" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="4">
+<div id="a434b911" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>county_and_pop[<span class="st">'clean_county_python'</span>] <span class="op">=</span> county_and_pop[<span class="st">'County'</span>].<span class="bu">map</span>(canonicalize_county)</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>county_and_state[<span class="st">'clean_county_python'</span>] <span class="op">=</span> county_and_state[<span class="st">'County'</span>].<span class="bu">map</span>(canonicalize_county)</span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>display(county_and_state), display(county_and_pop)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -606,7 +606,7 @@ <h4 data-number="6.2.1.1" class="anchored" data-anchor-id="canonicalization-with
 <h4 data-number="6.2.1.2" class="anchored" data-anchor-id="canonicalization-with-pandas-series-methods"><span class="header-section-number">6.2.1.2</span> Canonicalization with Pandas Series Methods</h4>
 <p>Alternatively, we can use <code>pandas</code> <code>Series</code> methods to create this standardized column. To do so, we must call the <code>.str</code> attribute of our <code>Series</code> object prior to calling any methods, like <code>.lower</code> and <code>.replace</code>. Notice how these method names match their equivalent built-in Python string functions.</p>
 <p>Chaining multiple <code>Series</code> methods in this manner eliminates the need to use the <code>map</code> function (as this code is vectorized).</p>
-<div id="b547372d" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="5">
+<div id="65ab4491" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> canonicalize_county_series(county_series):</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (</span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>        county_series</span>
@@ -724,7 +724,7 @@ <h4 data-number="6.2.1.2" class="anchored" data-anchor-id="canonicalization-with
 <h3 data-number="6.2.2" class="anchored" data-anchor-id="extraction"><span class="header-section-number">6.2.2</span> Extraction</h3>
 <p>Extraction explores the idea of obtaining useful information from text data. This will be particularily important in model building, which we’ll study in a few weeks.</p>
 <p>Say we want to read some data from a <code>.txt</code> file.</p>
-<div id="dbb19b85" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="6">
+<div id="617a7801" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="st">'data/log.txt'</span>, <span class="st">'r'</span>) <span class="im">as</span> f:</span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>    log_lines <span class="op">=</span> f.readlines()</span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -737,7 +737,7 @@ <h3 data-number="6.2.2" class="anchored" data-anchor-id="extraction"><span class
 </div>
 <p>Suppose we want to extract the day, month, year, hour, minutes, seconds, and time zone. Unfortunately, these items are not in a fixed position from the beginning of the string, so slicing by some fixed offset won’t work.</p>
 <p>Instead, we can use some clever thinking. Notice how the relevant information is contained within a set of brackets, further separated by <code>/</code> and <code>:</code>. We can hone in on this region of text, and split the data on these characters. Python’s built-in <code>.split</code> function makes this easy.</p>
-<div id="0cfe1d26" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="7">
+<div id="ef45b61b" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>first <span class="op">=</span> log_lines[<span class="dv">0</span>] <span class="co"># Only considering the first row of data</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>pertinent <span class="op">=</span> first.split(<span class="st">"["</span>)[<span class="dv">1</span>].split(<span class="st">']'</span>)[<span class="dv">0</span>]</span>
@@ -767,7 +767,7 @@ <h3 data-number="6.2.2" class="anchored" data-anchor-id="extraction"><span class
 <h2 data-number="6.3" class="anchored" data-anchor-id="regex-basics"><span class="header-section-number">6.3</span> RegEx Basics</h2>
 <p>A <strong>regular expression (“RegEx”)</strong> is a sequence of characters that specifies a search pattern. They are written to extract specific information from text. Regular expressions are essentially part of a smaller programming language embedded in Python, made available through the <code>re</code> module. As such, they have a stand-alone syntax and methods for various capabilities.</p>
 <p>Regular expressions are useful in many applications beyond data science. For example, Social Security Numbers (SSNs) are often validated with regular expressions.</p>
-<div id="7031deed" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="8">
+<div id="ee2e70cd" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co">r"[0-9]{3}-[0-9]{2}-[0-9]{4}"</span> <span class="co"># Regular Expression Syntax</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="co"># 3 of any digit, then a dash,</span></span>
@@ -1070,7 +1070,7 @@ <h3 data-number="6.5.1" class="anchored" data-anchor-id="greediness"><span class
 <section id="examples-2" class="level3" data-number="6.5.2">
 <h3 data-number="6.5.2" class="anchored" data-anchor-id="examples-2"><span class="header-section-number">6.5.2</span> Examples</h3>
 <p>Let’s revisit our earlier problem of extracting date/time data from the given <code>.txt</code> files. Here is how the data looked.</p>
-<div id="2f9a676d" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="9">
+<div id="43c377ad" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>log_lines[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
 <pre><code>'169.237.46.168 - - [26/Jan/2014:10:47:58 -0800] "GET /stat141/Winter04/ HTTP/1.1" 200 2585 "http://anson.ucdavis.edu/courses/"\n'</code></pre>
@@ -1103,7 +1103,7 @@ <h4 data-number="6.6.1.1" class="anchored" data-anchor-id="canonicalization-with
 <p>The regular expression here removes text surrounded by <code>&lt;&gt;</code> (also known as HTML tags).</p>
 <p>In order, the pattern matches … 1. a single <code>&lt;</code> 2. any character that is not a <code>&gt;</code> : div, td valign…, /td, /div 3. a single <code>&gt;</code></p>
 <p>Any substring in <code>text</code> that fulfills all three conditions will be replaced by <code>''</code>.</p>
-<div id="d272f283" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="10">
+<div id="d96a3a55" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> re</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>text <span class="op">=</span> <span class="st">"&lt;div&gt;&lt;td valign='top'&gt;Moo&lt;/td&gt;&lt;/div&gt;"</span></span>
@@ -1120,7 +1120,7 @@ <h4 data-number="6.6.1.1" class="anchored" data-anchor-id="canonicalization-with
 <h4 data-number="6.6.1.2" class="anchored" data-anchor-id="canonicalization-with-pandas"><span class="header-section-number">6.6.1.2</span> Canonicalization with <code>pandas</code></h4>
 <p>We can also use regular expressions with <code>pandas</code> <code>Series</code> methods. This gives us the benefit of operating on an entire column of data as opposed to a single value. The code is simple: <br> <code>ser.str.replace(pattern, repl, regex=True</code>).</p>
 <p>Consider the following <code>DataFrame</code> <code>html_data</code> with a single column.</p>
-<div id="8cb3c0b6" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="11">
+<div id="cce8b261" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="11">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> {<span class="st">"HTML"</span>: [<span class="st">"&lt;div&gt;&lt;td valign='top'&gt;Moo&lt;/td&gt;&lt;/div&gt;"</span>, <span class="op">\</span></span>
@@ -1129,7 +1129,7 @@ <h4 data-number="6.6.1.2" class="anchored" data-anchor-id="canonicalization-with
 <span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>html_data <span class="op">=</span> pd.DataFrame(data)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
-<div id="6558a1cf" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="12">
+<div id="329a4991" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>html_data</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="12">
 <div>
@@ -1161,7 +1161,7 @@ <h4 data-number="6.6.1.2" class="anchored" data-anchor-id="canonicalization-with
 </div>
 </div>
 </div>
-<div id="c6adbea1" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="13">
+<div id="f855b5e4" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>pattern <span class="op">=</span> <span class="vs">r"&lt;[^&gt;]+&gt;"</span></span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>html_data[<span class="st">'HTML'</span>].<span class="bu">str</span>.replace(pattern, <span class="st">''</span>, regex<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="13">
@@ -1179,7 +1179,7 @@ <h3 data-number="6.6.2" class="anchored" data-anchor-id="extraction-1"><span cla
 <h4 data-number="6.6.2.1" class="anchored" data-anchor-id="extraction-with-regex"><span class="header-section-number">6.6.2.1</span> Extraction with RegEx</h4>
 <p>Just like with canonicalization, the <code>re</code> module provides capability to extract relevant text from a string: <br> <code>re.findall(pattern, text)</code>. This function returns a list of all matches to <code>pattern</code>.</p>
 <p>Using the familiar regular expression for Social Security Numbers:</p>
-<div id="90df9e93" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="14">
+<div id="994b44ed" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>text <span class="op">=</span> <span class="st">"My social security number is 123-45-6789 bro, or maybe it’s 321-45-6789."</span></span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>pattern <span class="op">=</span> <span class="vs">r"[0-9]</span><span class="sc">{3}</span><span class="vs">-[0-9]</span><span class="sc">{2}</span><span class="vs">-[0-9]</span><span class="sc">{4}</span><span class="vs">"</span></span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>re.findall(pattern, text)  </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1192,7 +1192,7 @@ <h4 data-number="6.6.2.1" class="anchored" data-anchor-id="extraction-with-regex
 <h4 data-number="6.6.2.2" class="anchored" data-anchor-id="extraction-with-pandas"><span class="header-section-number">6.6.2.2</span> Extraction with <code>pandas</code></h4>
 <p><code>pandas</code> similarily provides extraction functionality on a <code>Series</code> of data: <code>ser.str.findall(pattern)</code></p>
 <p>Consider the following <code>DataFrame</code> <code>ssn_data</code>.</p>
-<div id="fe76fed4" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="15">
+<div id="9021ebeb" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="15">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> {<span class="st">"SSN"</span>: [<span class="st">"987-65-4321"</span>, <span class="st">"forty"</span>, <span class="op">\</span></span>
@@ -1201,7 +1201,7 @@ <h4 data-number="6.6.2.2" class="anchored" data-anchor-id="extraction-with-panda
 <span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>ssn_data <span class="op">=</span> pd.DataFrame(data)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 </div>
-<div id="5bf85f6d" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="16">
+<div id="a57d13ca" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>ssn_data</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="16">
 <div>
@@ -1237,7 +1237,7 @@ <h4 data-number="6.6.2.2" class="anchored" data-anchor-id="extraction-with-panda
 </div>
 </div>
 </div>
-<div id="4b2ca118" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="17">
+<div id="cf8302f5" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>ssn_data[<span class="st">"SSN"</span>].<span class="bu">str</span>.findall(pattern)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
 <pre><code>0                 [987-65-4321]
@@ -1249,7 +1249,7 @@ <h4 data-number="6.6.2.2" class="anchored" data-anchor-id="extraction-with-panda
 </div>
 <p>This function returns a list for every row containing the pattern matches in a given string.</p>
 <p>As you may expect, there are similar <code>pandas</code> equivalents for other <code>re</code> functions as well. <code>Series.str.extract</code> takes in a pattern and returns a <code>DataFrame</code> of each capture group’s first match in the string. In contrast, <code>Series.str.extractall</code> returns a multi-indexed <code>DataFrame</code> of all matches for each capture group. You can see the difference in the outputs below:</p>
-<div id="2bd4871e" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
+<div id="af47b44a" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>pattern_cg <span class="op">=</span> <span class="vs">r"([0-9]</span><span class="sc">{3}</span><span class="vs">)-([0-9]</span><span class="sc">{2}</span><span class="vs">)-([0-9]</span><span class="sc">{4}</span><span class="vs">)"</span></span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>ssn_data[<span class="st">"SSN"</span>].<span class="bu">str</span>.extract(pattern_cg)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
@@ -1296,7 +1296,7 @@ <h4 data-number="6.6.2.2" class="anchored" data-anchor-id="extraction-with-panda
 </div>
 </div>
 </div>
-<div id="313ff76e" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="19">
+<div id="d0b17ac1" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>ssn_data[<span class="st">"SSN"</span>].<span class="bu">str</span>.extractall(pattern_cg)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="19">
 <div>
@@ -1361,12 +1361,12 @@ <h3 data-number="6.6.3" class="anchored" data-anchor-id="regular-expression-capt
 <p>Let’s take a look at an example.</p>
 <section id="example-1" class="level4" data-number="6.6.3.1">
 <h4 data-number="6.6.3.1" class="anchored" data-anchor-id="example-1"><span class="header-section-number">6.6.3.1</span> Example 1</h4>
-<div id="0258a111" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="20">
+<div id="298cb0c6" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>text <span class="op">=</span> <span class="st">"Observations: 03:04:53 - Horse awakens. </span><span class="ch">\</span></span>
 <span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="st">        03:05:14 - Horse goes back to sleep."</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Say we want to capture all occurences of time data (hour, minute, and second) as <em>separate entities</em>.</p>
-<div id="d5041598" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="21">
+<div id="002d5864" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>pattern_1 <span class="op">=</span> <span class="vs">r"(\d\d):(\d\d):(\d\d)"</span></span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>re.findall(pattern_1, text)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
@@ -1375,7 +1375,7 @@ <h4 data-number="6.6.3.1" class="anchored" data-anchor-id="example-1"><span clas
 </div>
 <p>Notice how the given pattern has 3 capture groups, each specified by the regular expression <code>(\d\d)</code>. We then use <code>re.findall</code> to return these capture groups, each as tuples containing 3 matches.</p>
 <p>These regular expression capture groups can be different. We can use the <code>(\d{2})</code> shorthand to extract the same data.</p>
-<div id="3d3491c5" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="22">
+<div id="8e400ebd" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>pattern_2 <span class="op">=</span> <span class="vs">r"(\d\d):(\d\d):(\d</span><span class="sc">{2}</span><span class="vs">)"</span></span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>re.findall(pattern_2, text)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
@@ -1386,14 +1386,14 @@ <h4 data-number="6.6.3.1" class="anchored" data-anchor-id="example-1"><span clas
 <section id="example-2" class="level4" data-number="6.6.3.2">
 <h4 data-number="6.6.3.2" class="anchored" data-anchor-id="example-2"><span class="header-section-number">6.6.3.2</span> Example 2</h4>
 <p>With the notion of capture groups, convince yourself how the following regular expression works.</p>
-<div id="6fb7c11d" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="23">
+<div id="8cedbb28" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>first <span class="op">=</span> log_lines[<span class="dv">0</span>]</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>first</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
 <pre><code>'169.237.46.168 - - [26/Jan/2014:10:47:58 -0800] "GET /stat141/Winter04/ HTTP/1.1" 200 2585 "http://anson.ucdavis.edu/courses/"\n'</code></pre>
 </div>
 </div>
-<div id="a05b80b4" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="24">
+<div id="a4fa6c70" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>pattern <span class="op">=</span> <span class="vs">r'\[(\d+)\/(\w+)\/(\d+):(\d+):(\d+):(\d+) (.+)\]'</span></span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>day, month, year, hour, minute, second, time_zone <span class="op">=</span> re.findall(pattern, first)[<span class="dv">0</span>]</span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(day, month, year, hour, minute, second, time_zone)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
diff --git a/docs/search.json b/docs/search.json
index 2730f97a..6601f8a3 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -19,6 +19,136 @@
       "Welcome"
     ]
   },
+  {
+    "objectID": "visualization_2/visualization_2.html",
+    "href": "visualization_2/visualization_2.html",
+    "title": "8  Visualization II",
+    "section": "",
+    "text": "8.1 Kernel Density Estimation\nOften, we want to identify general trends across a distribution, rather than focus on detail. Smoothing a distribution helps generalize the structure of the data and eliminate noise.",
+    "crumbs": [
+      "<span class='chapter-number'>8</span>  <span class='chapter-title'>Visualization II</span>"
+    ]
+  },
+  {
+    "objectID": "visualization_2/visualization_2.html#kernel-density-estimation",
+    "href": "visualization_2/visualization_2.html#kernel-density-estimation",
+    "title": "8  Visualization II",
+    "section": "",
+    "text": "8.1.1 KDE Theory\nA kernel density estimate (KDE) is a smooth, continuous function that approximates a curve. It allows us to represent general trends in a distribution without focusing on the details, which is useful for analyzing the broad structure of a dataset.\nMore formally, a KDE attempts to approximate the underlying probability distribution from which our dataset was drawn. You may have encountered the idea of a probability distribution in your other classes; if not, we’ll discuss it at length in the next lecture. For now, you can think of a probability distribution as a description of how likely it is for us to sample a particular value in our dataset.\nA KDE curve estimates the probability density function of a random variable. Consider the example below, where we have used sns.displot to plot both a histogram (containing the data points we actually collected) and a KDE curve (representing the approximated probability distribution from which this data was drawn) using data from the World Bank dataset (wb).\n\n\nCode\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings \n\nwarnings.filterwarnings(\"ignore\", \"use_inf_as_na\") # Supresses distracting deprecation warnings\n\nwb = pd.read_csv(\"data/world_bank.csv\", index_col=0)\nwb = wb.rename(columns={'Antiretroviral therapy coverage: % of people living with HIV: 2015':\"HIV rate\",\n                       'Gross national income per capita, Atlas method: $: 2016':'gni'})\nwb.head()\n\n\n\n\n\n\n\n\n\nContinent\nCountry\nPrimary completion rate: Male: % of relevant age group: 2015\nPrimary completion rate: Female: % of relevant age group: 2015\nLower secondary completion rate: Male: % of relevant age group: 2015\nLower secondary completion rate: Female: % of relevant age group: 2015\nYouth literacy rate: Male: % of ages 15-24: 2005-14\nYouth literacy rate: Female: % of ages 15-24: 2005-14\nAdult literacy rate: Male: % ages 15 and older: 2005-14\nAdult literacy rate: Female: % ages 15 and older: 2005-14\n...\nAccess to improved sanitation facilities: % of population: 1990\nAccess to improved sanitation facilities: % of population: 2015\nChild immunization rate: Measles: % of children ages 12-23 months: 2015\nChild immunization rate: DTP3: % of children ages 12-23 months: 2015\nChildren with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016\nChildren with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016\nChildren sleeping under treated bed nets: % of children under age 5: 2009-2016\nChildren with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016\nTuberculosis: Treatment success rate: % of new cases: 2014\nTuberculosis: Cases detection rate: % of new estimated cases: 2015\n\n\n\n\n0\nAfrica\nAlgeria\n106.0\n105.0\n68.0\n85.0\n96.0\n92.0\n83.0\n68.0\n...\n80.0\n88.0\n95.0\n95.0\n66.0\n42.0\nNaN\nNaN\n88.0\n80.0\n\n\n1\nAfrica\nAngola\nNaN\nNaN\nNaN\nNaN\n79.0\n67.0\n82.0\n60.0\n...\n22.0\n52.0\n55.0\n64.0\nNaN\nNaN\n25.9\n28.3\n34.0\n64.0\n\n\n2\nAfrica\nBenin\n83.0\n73.0\n50.0\n37.0\n55.0\n31.0\n41.0\n18.0\n...\n7.0\n20.0\n75.0\n79.0\n23.0\n33.0\n72.7\n25.9\n89.0\n61.0\n\n\n3\nAfrica\nBotswana\n98.0\n101.0\n86.0\n87.0\n96.0\n99.0\n87.0\n89.0\n...\n39.0\n63.0\n97.0\n95.0\nNaN\nNaN\nNaN\nNaN\n77.0\n62.0\n\n\n5\nAfrica\nBurundi\n58.0\n66.0\n35.0\n30.0\n90.0\n88.0\n89.0\n85.0\n...\n42.0\n48.0\n93.0\n94.0\n55.0\n43.0\n53.8\n25.4\n91.0\n51.0\n\n\n\n\n5 rows × 47 columns\n\n\n\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nsns.displot(data = wb, x = 'HIV rate', \\\n                       kde = True, stat = \"density\")\n\nplt.title(\"Distribution of HIV rates\");\n\n\n\n\n\n\n\n\nNotice that the smooth KDE curve is higher when the histogram bins are taller. You can think of the height of the KDE curve as representing how “probable” it is that we randomly sample a datapoint with the corresponding value. This intuitively makes sense – if we have already collected more datapoints with a particular value (resulting in a tall histogram bin), it is more likely that, if we randomly sample another datapoint, we will sample one with a similar value (resulting in a high KDE curve).\nThe area under a probability density function should always integrate to 1, representing the fact that the total probability of a distribution should always sum to 100%. Hence, a KDE curve will always have an area under the curve of 1.\n\n\n8.1.2 Constructing a KDE\nWe perform kernel density estimation using three steps.\n\nPlace a kernel at each datapoint.\nNormalize the kernels to have a total area of 1 (across all kernels).\nSum the normalized kernels.\n\nWe’ll explain what a “kernel” is momentarily.\nTo make things simpler, let’s construct a KDE for a small, artificially generated dataset of 5 datapoints: \\([2.2, 2.8, 3.7, 5.3, 5.7]\\). In the plot below, each vertical bar represents one data point.\n\n\nCode\ndata = [2.2, 2.8, 3.7, 5.3, 5.7]\n\nsns.rugplot(data, height=0.3)\n\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5);\n\n\n\n\n\n\n\n\n\nOur goal is to create the following KDE curve, which was generated automatically by sns.kdeplot.\n\n\nCode\nsns.kdeplot(data)\n\nplt.xlabel(\"Data\")\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5);\n\n\n\n\n\n\n\n\n\n\n8.1.2.1 Step 1: Place a Kernel at Each Data Point\nTo begin generating a density curve, we need to choose a kernel and bandwidth value (\\(\\alpha\\)). What are these exactly?\nA kernel is a density curve. It is the mathematical function that attempts to capture the randomness of each data point in our sampled data. To explain what this means, consider just one of the datapoints in our dataset: \\(2.2\\). We obtained this datapoint by randomly sampling some information out in the real world (you can imagine \\(2.2\\) as representing a single measurement taken in an experiment, for example). If we were to sample a new datapoint, we may obtain a slightly different value. It could be higher than \\(2.2\\); it could also be lower than \\(2.2\\). We make the assumption that any future sampled datapoints will likely be similar in value to the data we’ve already drawn. This means that our kernel – our description of the probability of randomly sampling any new value – will be greatest at the datapoint we’ve already drawn but still have non-zero probability above and below it. The area under any kernel should integrate to 1, representing the total probability of drawing a new datapoint.\nA bandwidth value, usually denoted by \\(\\alpha\\), represents the width of the kernel. A large value of \\(\\alpha\\) will result in a wide, short kernel function, while a small value with result in a narrow, tall kernel.\nBelow, we place a Gaussian kernel, plotted in orange, over the datapoint \\(2.2\\). A Gaussian kernel is simply the normal distribution, which you may have called a bell curve in Data 8.\n\n\nCode\ndef gaussian_kernel(x, z, a):\n    # We'll discuss where this mathematical formulation came from later\n    return (1/np.sqrt(2*np.pi*a**2)) * np.exp((-(x - z)**2 / (2 * a**2)))\n\n# Plot our datapoint\nsns.rugplot([2.2], height=0.3)\n\n# Plot the kernel\nx = np.linspace(-3, 10, 1000)\nplt.plot(x, gaussian_kernel(x, 2.2, 1))\n\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5);\n\n\n\n\n\n\n\n\n\nTo begin creating our KDE, we place a kernel on each datapoint in our dataset. For our dataset of 5 points, we will have 5 kernels.\n\n\nCode\n# You will work with the functions below in Lab 4\ndef create_kde(kernel, pts, a):\n    # Takes in a kernel, set of points, and alpha\n    # Returns the KDE as a function\n    def f(x):\n        output = 0\n        for pt in pts:\n            output += kernel(x, pt, a)\n        return output / len(pts) # Normalization factor\n    return f\n\ndef plot_kde(kernel, pts, a):\n    # Calls create_kde and plots the corresponding KDE\n    f = create_kde(kernel, pts, a)\n    x = np.linspace(min(pts) - 5, max(pts) + 5, 1000)\n    y = [f(xi) for xi in x]\n    plt.plot(x, y);\n    \ndef plot_separate_kernels(kernel, pts, a, norm=False):\n    # Plots individual kernels, which are then summed to create the KDE\n    x = np.linspace(min(pts) - 5, max(pts) + 5, 1000)\n    for pt in pts:\n        y = kernel(x, pt, a)\n        if norm:\n            y /= len(pts)\n        plt.plot(x, y)\n    \n    plt.show();\n    \nplt.xlim(-3, 10)\nplt.ylim(0, 0.5)\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\n\nplot_separate_kernels(gaussian_kernel, data, a = 1)\n\n\n\n\n\n\n\n\n\n\n\n8.1.2.2 Step 2: Normalize Kernels to Have a Total Area of 1\nAbove, we said that each kernel has an area of 1. Earlier, we also said that our goal is to construct a KDE curve using these kernels with a total area of 1. If we were to directly sum the kernels as they are, we would produce a KDE curve with an integrated area of (5 kernels) \\(\\times\\) (area of 1 each) = 5. To avoid this, we will normalize each of our kernels. This involves multiplying each kernel by \\(\\frac{1}{\\#\\:\\text{datapoints}}\\).\nIn the cell below, we multiply each of our 5 kernels by \\(\\frac{1}{5}\\) to apply normalization.\n\n\nCode\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5)\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\n\n# The `norm` argument specifies whether or not to normalize the kernels\nplot_separate_kernels(gaussian_kernel, data, a = 1, norm = True)\n\n\n\n\n\n\n\n\n\n\n\n8.1.2.3 Step 3: Sum the Normalized Kernels\nOur KDE curve is the sum of the normalized kernels. Notice that the final curve is identical to the plot generated by sns.kdeplot we saw earlier!\n\n\nCode\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5)\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\n\nsns.kdeplot(data, bw_method=0.65)\nsns.histplot(data, stat=\"density\", bins=2);\n\n\n\n\n\n\n\n\n\nAn alternative method to generate the above KDE is shown below, this time using sns.histplot’s arguments.\n\n\nCode\nplt.xlim(-3, 10)\nplt.ylim(0, 0.5)\nplt.xlabel(\"Data\")\nplt.ylabel(\"Density\")\n\nsns.histplot(data, bins=2, kde=True, stat=\"density\", kde_kws=dict(cut=3, bw_method=0.65))\n\n\n\n\n\n\n\n\n\n\n\n\n8.1.3 Kernel Functions and Bandwidths\n\n\n\nA general “KDE formula” function is given above.\n\n\\(K_{\\alpha}(x, x_i)\\) is the kernel centered on the observation i.\n\nEach kernel individually has area 1.\nx represents any number on the number line. It is the input to our function.\n\n\\(n\\) is the number of observed datapoints that we have.\n\nWe multiply by \\(\\frac{1}{n}\\) so that the total area of the KDE is still 1.\n\nEach \\(x_i \\in \\{x_1, x_2, \\dots, x_n\\}\\) represents an observed datapoint.\n\nThese are what we use to create our KDE by summing multiple shifted kernels centered at these points.\n\n\n\n\\(\\alpha\\) (alpha) is the bandwidth or smoothing parameter.\n\nA kernel (for our purposes) is a valid density function. This means it:\n\nMust be non-negative for all inputs.\nMust integrate to 1.\n\n\n8.1.3.1 Gaussian Kernel\nThe most common kernel is the Gaussian kernel. The Gaussian kernel is equivalent to the Gaussian probability density function (the Normal distribution), centered at the observed value with a standard deviation of (this is known as the bandwidth parameter).\n\\[K_a(x, x_i) = \\frac{1}{\\sqrt{2\\pi\\alpha^{2}}}e^{-\\frac{(x-x_i)^{2}}{2\\alpha^{2}}}\\]\nIn this formula:\n\n\\(x\\) (no subscript) represents any value along the x-axis of our plot\n\\(x_i\\) represents the \\(i\\) -th datapoint in our dataset. It is one of the values that we have actually collected in our data sampling process. In our example earlier, \\(x_i=2.2\\). Those of you who have taken a probability class may recognize \\(x_i\\) as the mean of the normal distribution.\nEach kernel is centered on our observed values, so its distribution mean is \\(x_i\\).\n\\(\\alpha\\) is the bandwidth parameter, representing the width of our kernel. More formally, \\(\\alpha\\) is the standard deviation of the Gaussian curve.\n\nA large value of \\(\\alpha\\) will produce a kernel that is wider and shorter – this leads to a smoother KDE when the kernels are summed together.\nA small value of \\(\\alpha\\) will produce a narrower, taller kernel, and, with it, a noisier KDE.\n\n\nThe details of this (admittedly intimidating) formula are less important than understanding its role in kernel density estimation – this equation gives us the shape of each kernel.\n\n\n\n\n\n\n\nGaussian Kernel, \\(\\alpha\\) = 0.1\nGaussian Kernel, \\(\\alpha\\) = 1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaussian Kernel, \\(\\alpha\\) = 2\nGaussian Kernel, \\(\\alpha\\) = 5\n\n\n\n\n\n\n\n\n\n\n\n8.1.3.2 Boxcar Kernel\nAnother example of a kernel is the Boxcar kernel. The boxcar kernel assigns a uniform density to points within a “window” of the observation, and a density of 0 elsewhere. The equation below is a boxcar kernel with the center at \\(x_i\\) and the bandwidth of \\(\\alpha\\).\n\\[K_a(x, x_i) = \\begin{cases}\n        \\frac{1}{\\alpha}, & |x - x_i| \\le \\frac{\\alpha}{2}\\\\\n        0, & \\text{else }\n    \\end{cases}\\]\nThe boxcar kernel is seldom used in practice – we include it here to demonstrate that a kernel function can take whatever form you would like, provided it integrates to 1 and does not output negative values.\n\n\nCode\ndef boxcar_kernel(alpha, x, z):\n    return (((x-z)&gt;=-alpha/2)&((x-z)&lt;=alpha/2))/alpha\n\nxs = np.linspace(-5, 5, 200)\nalpha=1\nkde_curve = [boxcar_kernel(alpha, x, 0) for x in xs]\nplt.plot(xs, kde_curve);\n\n\n\n\n\nThe Boxcar kernel centered at 0 with bandwidth \\(\\alpha\\) = 1.\n\n\n\n\nThe diagram on the right is how the density curve for our 5 point dataset would have looked had we used the Boxcar kernel with bandwidth \\(\\alpha\\) = 1.\n\n\n\n\n\n\n\nKDE\nBoxcar",
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+    "text": "8.2 Diving Deeper into displot\nAs we saw earlier, we can use seaborn’s displot function to plot various distributions. In particular, displot allows you to specify the kind of plot and is a wrapper for histplot, kdeplot, and ecdfplot.\nBelow, we can see a couple of examples of how sns.displot can be used to plot various distributions.\nFirst, we can plot a histogram by setting kind to \"hist\". Note that here we’ve specified stat = density to normalize the histogram such that the area under the histogram is equal to 1.\n\nsns.displot(data=wb, \n            x=\"gni\", \n            kind=\"hist\", \n            stat=\"density\") # default: stat=count and density integrates to 1\nplt.title(\"Distribution of gross national income per capita\");\n\n\n\n\n\n\n\n\nNow, what if we want to generate a KDE plot? We can set kind = to \"kde\"!\n\nsns.displot(data=wb, \n            x=\"gni\", \n            kind='kde')\nplt.title(\"Distribution of gross national income per capita\");\n\n\n\n\n\n\n\n\nAnd finally, if we want to generate an Empirical Cumulative Distribution Function (ECDF), we can specify kind = \"ecdf\".\n\nsns.displot(data=wb, \n            x=\"gni\", \n            kind='ecdf')\nplt.title(\"Cumulative Distribution of gross national income per capita\");",
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+    "text": "8.3 Relationships Between Quantitative Variables\nUp until now, we’ve discussed how to visualize single-variable distributions. Going beyond this, we want to understand the relationship between pairs of numerical variables.\n\n8.3.0.1 Scatter Plots\nScatter plots are one of the most useful tools in representing the relationship between pairs of quantitative variables. They are particularly important in gauging the strength, or correlation, of the relationship between variables. Knowledge of these relationships can then motivate decisions in our modeling process.\nIn matplotlib, we use the function plt.scatter to generate a scatter plot. Notice that, unlike our examples of plotting single-variable distributions, now we specify sequences of values to be plotted along the x-axis and the y-axis.\n\nplt.scatter(wb[\"per capita: % growth: 2016\"], \\\n            wb['Adult literacy rate: Female: % ages 15 and older: 2005-14'])\n\nplt.xlabel(\"% growth per capita\")\nplt.ylabel(\"Female adult literacy rate\")\nplt.title(\"Female adult literacy against % growth\");\n\n\n\n\n\n\n\n\nIn seaborn, we call the function sns.scatterplot. We use the x and y parameters to indicate the values to be plotted along the x and y axes, respectively. By using the hue parameter, we can specify a third variable to be used for coloring each scatter point.\n\nsns.scatterplot(data = wb, x = \"per capita: % growth: 2016\", \\\n               y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \n               hue = \"Continent\")\n\nplt.title(\"Female adult literacy against % growth\");\n\n\n\n\n\n\n\n\n\n8.3.0.1.1 Overplotting\nAlthough the plots above communicate the general relationship between the two plotted variables, they both suffer a major limitation – overplotting. Overplotting occurs when scatter points with similar values are stacked on top of one another, making it difficult to see the number of scatter points actually plotted in the visualization. Notice how in the upper righthand region of the plots, we cannot easily tell just how many points have been plotted. This makes our visualizations difficult to interpret.\nWe have a few methods to help reduce overplotting:\n\nDecreasing the size of the scatter point markers can improve readability. We do this by setting a new value to the size parameter, s, of plt.scatter or sns.scatterplot.\nJittering is the process of adding a small amount of random noise to all x and y values to slightly shift the position of each datapoint. By randomly shifting all the data by some small distance, we can discern individual points more clearly without modifying the major trends of the original dataset.\n\nIn the cell below, we first jitter the data using np.random.uniform, then re-plot it with smaller markers. The resulting plot is much easier to interpret.\n\n# Setting a seed ensures that we produce the same plot each time\n# This means that the course notes will not change each time you access them\nnp.random.seed(150)\n\n# This call to np.random.uniform generates random numbers between -1 and 1\n# We add these random numbers to the original x data to jitter it slightly\nx_noise = np.random.uniform(-1, 1, len(wb))\njittered_x = wb[\"per capita: % growth: 2016\"] + x_noise\n\n# Repeat for y data\ny_noise = np.random.uniform(-5, 5, len(wb))\njittered_y = wb[\"Adult literacy rate: Female: % ages 15 and older: 2005-14\"] + y_noise\n\n# Setting the size parameter `s` changes the size of each point\nplt.scatter(jittered_x, jittered_y, s=15)\n\nplt.xlabel(\"% growth per capita (jittered)\")\nplt.ylabel(\"Female adult literacy rate (jittered)\")\nplt.title(\"Female adult literacy against % growth\");\n\n\n\n\n\n\n\n\n\n\n\n8.3.0.2 lmplot and jointplot\nseaborn also includes several built-in functions for creating more sophisticated scatter plots. Two of the most commonly used examples are sns.lmplot and sns.jointplot.\nsns.lmplot plots both a scatter plot and a linear regression line, all in one function call. We’ll discuss linear regression in a few lectures.\n\nsns.lmplot(data = wb, x = \"per capita: % growth: 2016\", \\\n           y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\")\n\nplt.title(\"Female adult literacy against % growth\");\n\n\n\n\n\n\n\n\nsns.jointplot creates a visualization with three components: a scatter plot, a histogram of the distribution of x values, and a histogram of the distribution of y values.\n\nsns.jointplot(data = wb, x = \"per capita: % growth: 2016\", \\\n           y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\")\n\n# plt.suptitle allows us to shift the title up so it does not overlap with the histogram\nplt.suptitle(\"Female adult literacy against % growth\")\nplt.subplots_adjust(top=0.9);\n\n\n\n\n\n\n\n\n\n\n8.3.0.3 Hex plots\nFor datasets with a very large number of datapoints, jittering is unlikely to fully resolve the issue of overplotting. In these cases, we can attempt to visualize our data by its density, rather than displaying each individual datapoint.\nHex plots can be thought of as two-dimensional histograms that show the joint distribution between two variables. This is particularly useful when working with very dense data. In a hex plot, the x-y plane is binned into hexagons. Hexagons that are darker in color indicate a greater density of data – that is, there are more data points that lie in the region enclosed by the hexagon.\nWe can generate a hex plot using sns.jointplot modified with the kind parameter.\n\nsns.jointplot(data = wb, x = \"per capita: % growth: 2016\", \\\n              y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \\\n              kind = \"hex\")\n\n# plt.suptitle allows us to shift the title up so it does not overlap with the histogram\nplt.suptitle(\"Female adult literacy against % growth\")\nplt.subplots_adjust(top=0.9);\n\n\n\n\n\n\n\n\n\n\n8.3.0.4 Contour Plots\nContour plots are an alternative way of plotting the joint distribution of two variables. You can think of them as the 2-dimensional versions of KDE plots. A contour plot can be interpreted in a similar way to a topographic map. Each contour line represents an area that has the same density of datapoints throughout the region. Contours marked with darker colors contain more datapoints (a higher density) in that region.\nsns.kdeplot will generate a contour plot if we specify both x and y data.\n\nsns.kdeplot(data = wb, x = \"per capita: % growth: 2016\", \\\n            y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \\\n            fill = True)\n\nplt.title(\"Female adult literacy against % growth\");",
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+    "text": "8.4 Transformations\nWe have now covered visualizations in great depth, looking into various forms of visualizations, plotting libraries, and high-level theory.\nMuch of this was done to uncover insights in data, which will prove necessary when we begin building models of data later in the course. A strong graphical correlation between two variables hints at an underlying relationship that we may want to study in greater detail. However, relying on visual relationships alone is limiting - not all plots show association. The presence of outliers and other statistical anomalies makes it hard to interpret data.\nTransformations are the process of manipulating data to find significant relationships between variables. These are often found by applying mathematical functions to variables that “transform” their range of possible values and highlight some previously hidden associations between data.\nTo see why we may want to transform data, consider the following plot of adult literacy rates against gross national income.\n\n\nCode\n# Some data cleaning to help with the next example\ndf = pd.DataFrame(index=wb.index)\ndf['lit'] = wb['Adult literacy rate: Female: % ages 15 and older: 2005-14'] \\\n            + wb[\"Adult literacy rate: Male: % ages 15 and older: 2005-14\"]\ndf['inc'] = wb['gni']\ndf.dropna(inplace=True)\n\nplt.scatter(df[\"inc\"], df[\"lit\"])\nplt.xlabel(\"Gross national income per capita\")\nplt.ylabel(\"Adult literacy rate\")\nplt.title(\"Adult literacy rate against GNI per capita\");\n\n\n\n\n\n\n\n\n\nThis plot is difficult to interpret for two reasons:\n\nThe data shown in the visualization appears almost “smushed” – it is heavily concentrated in the upper lefthand region of the plot. Even if we jittered the dataset, we likely would not be able to fully assess all datapoints in that area.\nIt is hard to generalize a clear relationship between the two plotted variables. While adult literacy rate appears to share some positive relationship with gross national income, we are not able to describe the specifics of this trend in much detail.\n\nA transformation would allow us to visualize this data more clearly, which, in turn, would enable us to describe the underlying relationship between our variables of interest.\nWe will most commonly apply a transformation to linearize a relationship between variables. If we find a transformation to make a scatter plot of two variables linear, we can “backtrack” to find the exact relationship between the variables. This helps us in two major ways. Firstly, linear relationships are particularly simple to interpret – we have an intuitive sense of what the slope and intercept of a linear trend represent, and how they can help us understand the relationship between two variables. Secondly, linear relationships are the backbone of linear models. We will begin exploring linear modeling in great detail next week. As we’ll soon see, linear models become much more effective when we are working with linearized data.\nIn the remainder of this note, we will discuss how to linearize a dataset to produce the result below. Notice that the resulting plot displays a rough linear relationship between the values plotted on the x and y axes.\n\n\n8.4.1 Linearization and Applying Transformations\nTo linearize a relationship, begin by asking yourself: what makes the data non-linear? It is helpful to repeat this question for each variable in your visualization.\nLet’s start by considering the gross national income variable in our plot above. Looking at the y values in the scatter plot, we can see that many large y values are all clumped together, compressing the vertical axis. The scale of the horizontal axis is also being distorted by the few large outlying x values on the right.\n\nIf we decreased the size of these outliers relative to the bulk of the data, we could reduce the distortion of the horizontal axis. How can we do this? We need a transformation that will:\n\nDecrease the magnitude of large x values by a significant amount.\nNot drastically change the magnitude of small x values.\n\nOne function that produces this result is the log transformation. When we take the logarithm of a large number, the original number will decrease in magnitude dramatically. Conversely, when we take the logarithm of a small number, the original number does not change its value by as significant of an amount (to illustrate this, consider the difference between \\(\\log{(100)} = 4.61\\) and \\(\\log{(10)} = 2.3\\)).\nIn Data 100 (and most upper-division STEM classes), \\(\\log\\) is used to refer to the natural logarithm with base \\(e\\).\n\n# np.log takes the logarithm of an array or Series\nplt.scatter(np.log(df[\"inc\"]), df[\"lit\"])\n\nplt.xlabel(\"Log(gross national income per capita)\")\nplt.ylabel(\"Adult literacy rate\")\nplt.title(\"Adult literacy rate against Log(GNI per capita)\");\n\n\n\n\n\n\n\n\nAfter taking the logarithm of our x values, our plot appears much more balanced in its horizontal scale. We no longer have many datapoints clumped on one end and a few outliers out at extreme values.\nLet’s repeat this reasoning for the y values. Considering only the vertical axis of the plot, notice how there are many datapoints concentrated at large y values. Only a few datapoints lie at smaller values of y.\nIf we were to “spread out” these large values of y more, we would no longer see the dense concentration in one region of the y-axis. We need a transformation that will:\n\nIncrease the magnitude of large values of y so these datapoints are distributed more broadly on the vertical scale,\nNot substantially alter the scaling of small values of y (we do not want to drastically modify the lower end of the y axis, which is already distributed evenly on the vertical scale).\n\nIn this case, it is helpful to apply a power transformation – that is, raise our y values to a power. Let’s try raising our adult literacy rate values to the power of 4. Large values raised to the power of 4 will increase in magnitude proportionally much more than small values raised to the power of 4 (consider the difference between \\(2^4 = 16\\) and \\(200^4 = 1600000000\\)).\n\n# Apply a log transformation to the x values and a power transformation to the y values\nplt.scatter(np.log(df[\"inc\"]), df[\"lit\"]**4)\n\nplt.xlabel(\"Log(gross national income per capita)\")\nplt.ylabel(\"Adult literacy rate (4th power)\")\nplt.suptitle(\"Adult literacy rate (4th power) against Log(GNI per capita)\")\nplt.subplots_adjust(top=0.9);\n\n\n\n\n\n\n\n\nOur scatter plot is looking a lot better! Now, we are plotting the log of our original x values on the horizontal axis, and the 4th power of our original y values on the vertical axis. We start to see an approximate linear relationship between our transformed variables.\nWhat can we take away from this? We now know that the log of gross national income and adult literacy to the power of 4 are roughly linearly related. If we denote the original, untransformed gross national income values as \\(x\\) and the original adult literacy rate values as \\(y\\), we can use the standard form of a linear fit to express this relationship:\n\\[y^4 = m(\\log{x}) + b\\]\nWhere \\(m\\) represents the slope of the linear fit, while \\(b\\) represents the intercept.\nThe cell below computes \\(m\\) and \\(b\\) for our transformed data. We’ll discuss how this code was generated in a future lecture.\n\n\nCode\n# The code below fits a linear regression model. We'll discuss it at length in a future lecture\nfrom sklearn.linear_model import LinearRegression\n\nmodel = LinearRegression()\nmodel.fit(np.log(df[[\"inc\"]]), df[\"lit\"]**4)\nm, b = model.coef_[0], model.intercept_\n\nprint(f\"The slope, m, of the transformed data is: {m}\")\nprint(f\"The intercept, b, of the transformed data is: {b}\")\n\ndf = df.sort_values(\"inc\")\nplt.scatter(np.log(df[\"inc\"]), df[\"lit\"]**4, label=\"Transformed data\")\nplt.plot(np.log(df[\"inc\"]), m*np.log(df[\"inc\"])+b, c=\"red\", label=\"Linear regression\")\nplt.xlabel(\"Log(gross national income per capita)\")\nplt.ylabel(\"Adult literacy rate (4th power)\")\nplt.legend();\n\n\nThe slope, m, of the transformed data is: 336400693.43172693\nThe intercept, b, of the transformed data is: -1802204836.0479977\n\n\n\n\n\n\n\n\n\nWhat if we want to understand the underlying relationship between our original variables, before they were transformed? We can simply rearrange our linear expression above!\nRecall our linear relationship between the transformed variables \\(\\log{x}\\) and \\(y^4\\).\n\\[y^4 = m(\\log{x}) + b\\]\nBy rearranging the equation, we find a relationship between the untransformed variables \\(x\\) and \\(y\\).\n\\[y = [m(\\log{x}) + b]^{(1/4)}\\]\nWhen we plug in the values for \\(m\\) and \\(b\\) computed above, something interesting happens.\n\n\nCode\n# Now, plug the values for m and b into the relationship between the untransformed x and y\nplt.scatter(df[\"inc\"], df[\"lit\"], label=\"Untransformed data\")\nplt.plot(df[\"inc\"], (m*np.log(df[\"inc\"])+b)**(1/4), c=\"red\", label=\"Modeled relationship\")\nplt.xlabel(\"Gross national income per capita\")\nplt.ylabel(\"Adult literacy rate\")\nplt.legend();\n\n\n\n\n\n\n\n\n\nWe have found a relationship between our original variables – gross national income and adult literacy rate!\nTransformations are powerful tools for understanding our data in greater detail. To summarize what we just achieved:\n\nWe identified appropriate transformations to linearize the original data.\nWe used our knowledge of linear curves to compute the slope and intercept of the transformed data.\nWe used this slope and intercept information to derive a relationship in the untransformed data.\n\nLinearization will be an important tool as we begin our work on linear modeling next week.\n\n8.4.1.1 Tukey-Mosteller Bulge Diagram\nThe Tukey-Mosteller Bulge Diagram is a good guide when determining possible transformations to achieve linearity. It is a visual summary of the reasoning we just worked through above.\n\nHow does it work? Each curved “bulge” represents a possible shape of non-linear data. To use the diagram, find which of the four bulges resembles your dataset the most closely. Then, look at the axes of the quadrant for this bulge. The horizontal axis will list possible transformations that could be applied to your x data for linearization. Similarly, the vertical axis will list possible transformations that could be applied to your y data. Note that each axis lists two possible transformations. While either of these transformations has the potential to linearize your dataset, note that this is an iterative process. It’s important to try out these transformations and look at the results to see whether you’ve actually achieved linearity. If not, you’ll need to continue testing other possible transformations.\nGenerally:\n\n\\(\\sqrt{}\\) and \\(\\log{}\\) will reduce the magnitude of large values.\nPowers (\\(^2\\) and \\(^3\\)) will increase the spread in magnitude of large values.\n\n\n\n\nImportant: You should still understand the logic we worked through to determine how best to transform the data. The bulge diagram is just a summary of this same reasoning. You will be expected to be able to explain why a given transformation is or is not appropriate for linearization.\n\n\n\n8.4.2 Additional Remarks\nVisualization requires a lot of thought!\n\nThere are many tools for visualizing distributions.\n\nDistribution of a single variable:\n\nRugplot\nHistogram\nDensity plot\nBox plot\nViolin plot\n\nJoint distribution of two quantitative variables:\n\nScatter plot\nHex plot\nContour plot\n\n\n\nThis class primarily uses seaborn and matplotlib, but pandas also has basic built-in plotting methods. Many other visualization libraries exist, and plotly is one of them.\n\nplotly creates very easily creates interactive plots.\nplotly will occasionally appear in lecture code, labs, and assignments!\n\nNext, we’ll go deeper into the theory behind visualization.",
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+    "text": "8.5 Visualization Theory\nThis section marks a pivot to the second major topic of this lecture - visualization theory. We’ll discuss the abstract nature of visualizations and analyze how they convey information.\nRemember, we had two goals for visualizing data. This section is particularly important in:\n\nHelping us understand the data and results,\nCommunicating our results and conclusions with others.\n\n\n8.5.1 Information Channels\nVisualizations are able to convey information through various encodings. In the remainder of this lecture, we’ll look at the use of color, scale, and depth, to name a few.\n\n8.5.1.1 Encodings in Rugplots\nOne detail that we may have overlooked in our earlier discussion of rugplots is the importance of encodings. Rugplots are effective visuals because they utilize line thickness to encode frequency. Consider the following diagram:\n\n\n\n8.5.1.2 Multi-Dimensional Encodings\nEncodings are also useful for representing multi-dimensional data. Notice how the following visual highlights four distinct “dimensions” of data:\n\nX-axis\nY-axis\nArea\nColor\n\n\nThe human visual perception system is only capable of visualizing data in a three-dimensional plane, but as you’ve seen, we can encode many more channels of information.\n\n\n\n8.5.2 Harnessing the Axes\n\n8.5.2.1 Consider the Scale of the Data\nHowever, we should be careful to not misrepresent relationships in our data by manipulating the scale or axes. The visualization below improperly portrays two seemingly independent relationships on the same plot. The authors have clearly changed the scale of the y-axis to mislead their audience.\n\nNotice how the downwards-facing line segment contains values in the millions, while the upwards-trending segment only contains values near three hundred thousand. These lines should not be intersecting.\nWhen there is a large difference in the magnitude of the data, it’s advised to analyze percentages instead of counts. The following diagrams correctly display the trends in cancer screening and abortion rates.\n\n\n\n\n\n\n\n\n\n\n\n8.5.2.2 Reveal the Data\nGreat visualizations not only consider the scale of the data but also utilize the axes in a way that best conveys information. For example, data scientists commonly set certain axes limits to highlight parts of the visualization they are most interested in.\n\n\n\n\n\n\n\n\n\nThe visualization on the right captures the trend in coronavirus cases during March of 2020. From only looking at the visualization on the left, a viewer may incorrectly believe that coronavirus began to skyrocket on March 4th, 2020. However, the second illustration tells a different story - cases rose closer to March 21th, 2020.\n\n\n\n8.5.3 Harnessing Color\nColor is another important feature in visualizations that does more than what meets the eye.\nWe already explored using color to encode a categorical variable in our scatter plot. Let’s now discuss the uses of color in novel visualizations like colormaps and heatmaps.\n5-8% of the world is red-green color blind, so we have to be very particular about our color scheme. We want to make these as accessible as possible. Choosing a set of colors that work together is evidently a challenging task!\n\n8.5.3.1 Colormaps\nColormaps are mappings from pixel data to color values, and they’re often used to highlight distinct parts of an image. Let’s investigate a few properties of colormaps.\n\n\nJet Colormap \n\n\n\nViridis Colormap \n\n\nThe jet colormap is infamous for being misleading. While it seems more vibrant than viridis, the aggressive colors poorly encode numerical data. To understand why, let’s analyze the following images.\n\n\n\n\n\n\n\n\n\nThe diagram on the left compares how a variety of colormaps represent pixel data that transitions from a high to low intensity. These include the jet colormap (row a) and grayscale (row b). Notice how the grayscale images do the best job in smoothly transitioning between pixel data. The jet colormap is the worst at this - the four images in row (a) look like a conglomeration of individual colors.\nThe difference is also evident in the images labeled (a) and (b) on the left side. The grayscale image is better at preserving finer detail in the vertical line strokes. Additionally, grayscale is preferred in X-ray scans for being more neutral. The intensity of the dark red color in the jet colormap is frightening and indicates something is wrong.\nWhy is the jet colormap so much worse? The answer lies in how its color composition is perceived to the human eye.\n\n\nJet Colormap Perception \n\n\n\nViridis Colormap Perception \n\n\nThe jet colormap is largely misleading because it is not perceptually uniform. Perceptually uniform colormaps have the property that if the pixel data goes from 0.1 to 0.2, the perceptual change is the same as when the data goes from 0.8 to 0.9.\nNotice how the said uniformity is present within the linear trend displayed in the viridis colormap. On the other hand, the jet colormap is largely non-linear - this is precisely why it’s considered a worse colormap.\n\n\n\n8.5.4 Harnessing Markings\nIn our earlier discussion of multi-dimensional encodings, we analyzed a scatter plot with four pseudo-dimensions: the two axes, area, and color. Were these appropriate to use? The following diagram analyzes how well the human eye can distinguish between these “markings”.\n\nThere are a few key takeaways from this diagram\n\nLengths are easy to discern. Don’t use plots with jiggled baselines - keep everything axis-aligned.\nAvoid pie charts! Angle judgments are inaccurate.\nAreas and volumes are hard to distinguish (area charts, word clouds, etc.).\n\n\n\n8.5.5 Harnessing Conditioning\nConditioning is the process of comparing data that belong to separate groups. We’ve seen this before in overlayed distributions, side-by-side box plots, and scatter plots with categorical encodings. Here, we’ll introduce terminology that formalizes these examples.\nConsider an example where we want to analyze income earnings for males and females with varying levels of education. There are multiple ways to compare this data.\n\n\n\n\n\n\n\n\n\nThe barplot is an example of juxtaposition: placing multiple plots side by side, with the same scale. The scatter plot is an example of superposition: placing multiple density curves and scatter plots on top of each other.\nWhich is better depends on the problem at hand. Here, superposition makes the precise wage difference very clear from a quick glance. However, many sophisticated plots convey information that favors the use of juxtaposition. Below is one example.\n\n\n\n8.5.6 Harnessing Context\nThe last component of a great visualization is perhaps the most critical - the use of context. Adding informative titles, axis labels, and descriptive captions are all best practices that we’ve heard repeatedly in Data 8.\nA publication-ready plot (and every Data 100 plot) needs:\n\nInformative title (takeaway, not description),\nAxis labels,\nReference lines, markers, etc,\nLegends, if appropriate,\nCaptions that describe data,\n\nCaptions should:\n\nBe comprehensive and self-contained,\nDescribe what has been graphed,\nDraw attention to important features,\nDescribe conclusions drawn from graphs.",
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+    "text": "7.1 Visualizations in Data 8 and Data 100 (so far)\nYou’ve likely encountered several forms of data visualizations in your studies. You may remember two such examples from Data 8: line plots, scatter plots, and histograms. Each of these served a unique purpose. For example, line plots displayed how numerical quantities changed over time, while histograms were useful in understanding a variable’s distribution.",
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+    "text": "Line Chart\nScatter Plot\n\n\n\n\n\n\n\n\n\n\n\n\nHistogram",
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+    "text": "7.2 Goals of Visualization\nVisualizations are useful for a number of reasons. In Data 100, we consider two areas in particular:\n\nTo broaden your understanding of the data. Summarizing trends visually before in-depth analysis is a key part of exploratory data analysis. Creating these graphs is a lightweight, iterative and flexible process that helps us investigate relationships between variables.\nTo communicate results/conclusions to others. These visualizations are highly editorial, selective, and fine-tuned to achieve a communications goal, so be thoughtful and careful about its clarity, accessibility, and necessary context.\n\nAltogether, these goals emphasize the fact that visualizations aren’t a matter of making “pretty” pictures; we need to do a lot of thinking about what stylistic choices communicate ideas most effectively.\nThis course note will focus on the first half of visualization topics in Data 100. The goal here is to understand how to choose the “right” plot depending on different variable types and, secondly, how to generate these plots using code.",
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+    "text": "7.3 An Overview of Distributions\nA distribution describes both the set of values that a single variable can take and the frequency of unique values in a single variable. For example, if we’re interested in the distribution of students across Data 100 discussion sections, the set of possible values is a list of discussion sections (10-11am, 11-12pm, etc.), and the frequency that each of those values occur is the number of students enrolled in each section. In other words, the we’re interested in how a variable is distributed across it’s possible values. Therefore, distributions must satisfy two properties:\n\nThe total frequency of all categories must sum to 100%\nTotal count should sum to the total number of datapoints if we’re using raw counts.\n\n\n\n\n\n\n\n\nNot a Valid Distribution\nValid Distribution\n\n\n\n\n\n\n\n\nThis is not a valid distribution since individuals can be associated with more than one category and the bar values demonstrate values in minutes and not probability.\nThis example satisfies the two properties of distributions, so it is a valid distribution.",
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+    "text": "7.4 Variable Types Should Inform Plot Choice\nDifferent plots are more or less suited for displaying particular types of variables, laid out in the diagram below:\n\n\n\nThe first step of any visualization is to identify the type(s) of variables we’re working with. From here, we can select an appropriate plot type:",
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+    "section": "7.5 Qualitative Variables: Bar Plots",
+    "text": "7.5 Qualitative Variables: Bar Plots\nA bar plot is one of the most common ways of displaying the distribution of a qualitative (categorical) variable. The length of a bar plot encodes the frequency of a category; the width encodes no useful information. The color could indicate a sub-category, but this is not necessarily the case.\nLet’s contextualize this in an example. We will use the World Bank dataset (wb) in our analysis.\n\n\nCode\nimport pandas as pd\nimport numpy as np\nimport warnings \n\nwarnings.filterwarnings(\"ignore\", \"use_inf_as_na\") # Supresses distracting deprecation warnings\n\nwb = pd.read_csv(\"data/world_bank.csv\", index_col=0)\nwb.head()\n\n\n\n\n\n\n\n\n\nContinent\nCountry\nPrimary completion rate: Male: % of relevant age group: 2015\nPrimary completion rate: Female: % of relevant age group: 2015\nLower secondary completion rate: Male: % of relevant age group: 2015\nLower secondary completion rate: Female: % of relevant age group: 2015\nYouth literacy rate: Male: % of ages 15-24: 2005-14\nYouth literacy rate: Female: % of ages 15-24: 2005-14\nAdult literacy rate: Male: % ages 15 and older: 2005-14\nAdult literacy rate: Female: % ages 15 and older: 2005-14\n...\nAccess to improved sanitation facilities: % of population: 1990\nAccess to improved sanitation facilities: % of population: 2015\nChild immunization rate: Measles: % of children ages 12-23 months: 2015\nChild immunization rate: DTP3: % of children ages 12-23 months: 2015\nChildren with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016\nChildren with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016\nChildren sleeping under treated bed nets: % of children under age 5: 2009-2016\nChildren with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016\nTuberculosis: Treatment success rate: % of new cases: 2014\nTuberculosis: Cases detection rate: % of new estimated cases: 2015\n\n\n\n\n0\nAfrica\nAlgeria\n106.0\n105.0\n68.0\n85.0\n96.0\n92.0\n83.0\n68.0\n...\n80.0\n88.0\n95.0\n95.0\n66.0\n42.0\nNaN\nNaN\n88.0\n80.0\n\n\n1\nAfrica\nAngola\nNaN\nNaN\nNaN\nNaN\n79.0\n67.0\n82.0\n60.0\n...\n22.0\n52.0\n55.0\n64.0\nNaN\nNaN\n25.9\n28.3\n34.0\n64.0\n\n\n2\nAfrica\nBenin\n83.0\n73.0\n50.0\n37.0\n55.0\n31.0\n41.0\n18.0\n...\n7.0\n20.0\n75.0\n79.0\n23.0\n33.0\n72.7\n25.9\n89.0\n61.0\n\n\n3\nAfrica\nBotswana\n98.0\n101.0\n86.0\n87.0\n96.0\n99.0\n87.0\n89.0\n...\n39.0\n63.0\n97.0\n95.0\nNaN\nNaN\nNaN\nNaN\n77.0\n62.0\n\n\n5\nAfrica\nBurundi\n58.0\n66.0\n35.0\n30.0\n90.0\n88.0\n89.0\n85.0\n...\n42.0\n48.0\n93.0\n94.0\n55.0\n43.0\n53.8\n25.4\n91.0\n51.0\n\n\n\n\n5 rows × 47 columns\n\n\n\nWe can visualize the distribution of the Continent column using a bar plot. There are a few ways to do this.\n\n7.5.1 Plotting in Pandas\n\nwb['Continent'].value_counts().plot(kind='bar');\n\n\n\n\n\n\n\n\nRecall that .value_counts() returns a Series with the total count of each unique value. We call .plot(kind='bar') on this result to visualize these counts as a bar plot.\nPlotting methods in pandas are the least preferred and not supported in Data 100, as their functionality is limited. Instead, future examples will focus on other libraries built specifically for visualizing data. The most well-known library here is matplotlib.\n\n\n7.5.2 Plotting in Matplotlib\n\nimport matplotlib.pyplot as plt # matplotlib is typically given the alias plt\n\ncontinent = wb['Continent'].value_counts()\nplt.bar(continent.index, continent)\nplt.xlabel('Continent')\nplt.ylabel('Count');\n\n\n\n\n\n\n\n\nWhile more code is required to achieve the same result, matplotlib is often used over pandas for its ability to plot more complex visualizations, some of which are discussed shortly.\nHowever, note how we needed to label the axes with plt.xlabel and plt.ylabel, as matplotlib does not support automatic axis labeling. To get around these inconveniences, we can use a more efficient plotting library: seaborn.\n\n\n7.5.3 Plotting in Seaborn\n\nimport seaborn as sns # seaborn is typically given the alias sns\nsns.countplot(data = wb, x = 'Continent');\n\n\n\n\n\n\n\n\nIn contrast to matplotlib, the general structure of a seaborn call involves passing in an entire DataFrame, and then specifying what column(s) to plot. seaborn.countplot both counts and visualizes the number of unique values in a given column. This column is specified by the x argument to sns.countplot, while the DataFrame is specified by the data argument.\nFor the vast majority of visualizations, seaborn is far more concise and aesthetically pleasing than matplotlib. However, the color scheme of this particular bar plot is arbitrary - it encodes no additional information about the categories themselves. This is not always true; color may signify meaningful detail in other visualizations. We’ll explore this more in-depth during the next lecture.\nBy now, you’ll have noticed that each of these plotting libraries have a very different syntax. As with pandas, we’ll teach you the important methods in matplotlib and seaborn, but you’ll learn more through documentation.\n\nMatplotlib Documentation\nSeaborn Documentation",
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+    "text": "7.6 Distributions of Quantitative Variables\nRevisiting our example with the wb DataFrame, let’s plot the distribution of Gross national income per capita.\n\n\nCode\nwb.head(5)\n\n\n\n\n\n\n\n\n\nContinent\nCountry\nPrimary completion rate: Male: % of relevant age group: 2015\nPrimary completion rate: Female: % of relevant age group: 2015\nLower secondary completion rate: Male: % of relevant age group: 2015\nLower secondary completion rate: Female: % of relevant age group: 2015\nYouth literacy rate: Male: % of ages 15-24: 2005-14\nYouth literacy rate: Female: % of ages 15-24: 2005-14\nAdult literacy rate: Male: % ages 15 and older: 2005-14\nAdult literacy rate: Female: % ages 15 and older: 2005-14\n...\nAccess to improved sanitation facilities: % of population: 1990\nAccess to improved sanitation facilities: % of population: 2015\nChild immunization rate: Measles: % of children ages 12-23 months: 2015\nChild immunization rate: DTP3: % of children ages 12-23 months: 2015\nChildren with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016\nChildren with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016\nChildren sleeping under treated bed nets: % of children under age 5: 2009-2016\nChildren with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016\nTuberculosis: Treatment success rate: % of new cases: 2014\nTuberculosis: Cases detection rate: % of new estimated cases: 2015\n\n\n\n\n0\nAfrica\nAlgeria\n106.0\n105.0\n68.0\n85.0\n96.0\n92.0\n83.0\n68.0\n...\n80.0\n88.0\n95.0\n95.0\n66.0\n42.0\nNaN\nNaN\n88.0\n80.0\n\n\n1\nAfrica\nAngola\nNaN\nNaN\nNaN\nNaN\n79.0\n67.0\n82.0\n60.0\n...\n22.0\n52.0\n55.0\n64.0\nNaN\nNaN\n25.9\n28.3\n34.0\n64.0\n\n\n2\nAfrica\nBenin\n83.0\n73.0\n50.0\n37.0\n55.0\n31.0\n41.0\n18.0\n...\n7.0\n20.0\n75.0\n79.0\n23.0\n33.0\n72.7\n25.9\n89.0\n61.0\n\n\n3\nAfrica\nBotswana\n98.0\n101.0\n86.0\n87.0\n96.0\n99.0\n87.0\n89.0\n...\n39.0\n63.0\n97.0\n95.0\nNaN\nNaN\nNaN\nNaN\n77.0\n62.0\n\n\n5\nAfrica\nBurundi\n58.0\n66.0\n35.0\n30.0\n90.0\n88.0\n89.0\n85.0\n...\n42.0\n48.0\n93.0\n94.0\n55.0\n43.0\n53.8\n25.4\n91.0\n51.0\n\n\n\n\n5 rows × 47 columns\n\n\n\nHow should we define our categories for this variable? In the previous example, these were a few unique values of the Continent column. If we use similar logic here, our categories are the different numerical values contained in the Gross national income per capita column.\nUnder this assumption, let’s plot this distribution using the seaborn.countplot function.\n\nsns.countplot(data = wb, x = 'Gross national income per capita, Atlas method: $: 2016');\n\n\n\n\n\n\n\n\nWhat happened? A bar plot (either plt.bar or sns.countplot) will create a separate bar for each unique value of a variable. With a continuous variable, we may not have a finite number of possible values, which can lead to situations like above where we would need many, many bars to display each unique value.\nSpecifically, we can say this histogram suffers from overplotting as we are unable to interpret the plot and gain any meaningful insight.\nRather than bar plots, to visualize the distribution of a continuous variable, we use one of the following types of plots:\n\nHistogram\nBox plot\nViolin plot\n\n\n7.6.1 Box Plots and Violin Plots\nBox plots and violin plots are two very similar kinds of visualizations. Both display the distribution of a variable using information about quartiles.\nIn a box plot, the width of the box at any point does not encode meaning. In a violin plot, the width of the plot indicates the density of the distribution at each possible value.\n\nsns.boxplot(data=wb, y='Gross national income per capita, Atlas method: $: 2016');\n\n\n\n\n\n\n\n\n\nsns.violinplot(data=wb, y=\"Gross national income per capita, Atlas method: $: 2016\");\n\n\n\n\n\n\n\n\nA quartile represents a 25% portion of the data. We say that:\n\nThe first quartile (Q1) represents the 25th percentile – 25% of the data is smaller than or equal to the first quartile.\nThe second quartile (Q2) represents the 50th percentile, also known as the median – 50% of the data is smaller than or equal to the second quartile.\nThe third quartile (Q3) represents the 75th percentile – 75% of the data is smaller than or equal to the third quartile.\n\nThis means that the middle 50% of the data lies between the first and third quartiles. This is demonstrated in the histogram below. The three quartiles are marked with red vertical bars.\n\n\nCode\ngdp = wb['Gross domestic product: % growth : 2016']\ngdp = gdp[~gdp.isna()]\n\nq1, q2, q3 = np.percentile(gdp, [25, 50, 75])\n\nwb_quartiles = wb.copy()\nwb_quartiles['category'] = None\nwb_quartiles.loc[(wb_quartiles['Gross domestic product: % growth : 2016'] &lt; q1) | (wb_quartiles['Gross domestic product: % growth : 2016'] &gt; q3), 'category'] = 'Outside of the middle 50%'\nwb_quartiles.loc[(wb_quartiles['Gross domestic product: % growth : 2016'] &gt; q1) & (wb_quartiles['Gross domestic product: % growth : 2016'] &lt; q3), 'category'] = 'In the middle 50%'\n\nsns.histplot(wb_quartiles, x=\"Gross domestic product: % growth : 2016\", hue=\"category\")\nsns.rugplot([q1, q2, q3], c=\"firebrick\", lw=6, height=0.1);\n\n\n\n\n\n\n\n\n\nIn a box plot, the lower extent of the box lies at Q1, while the upper extent of the box lies at Q3. The horizontal line in the middle of the box corresponds to Q2 (equivalently, the median).\n\nsns.boxplot(data=wb, y='Gross domestic product: % growth : 2016');\n\n\n\n\n\n\n\n\nThe whiskers of a box-plot are the two points that lie at the [\\(1^{st}\\) Quartile \\(-\\) (\\(1.5\\times\\) IQR)], and the [\\(3^{rd}\\) Quartile \\(+\\) (\\(1.5\\times\\) IQR)]. They are the lower and upper ranges of “normal” data (the points excluding outliers).\nThe different forms of information contained in a box plot can be summarised as follows:\n\n\n\nA violin plot displays quartile information, albeit a bit more subtly through smoothed density curves. Look closely at the center vertical bar of the violin plot below; the three quartiles and “whiskers” are still present!\n\nsns.violinplot(data=wb, y='Gross domestic product: % growth : 2016');\n\n\n\n\n\n\n\n\n\n\n7.6.2 Side-by-Side Box and Violin Plots\nPlotting side-by-side box or violin plots allows us to compare distributions across different categories. In other words, they enable us to plot both a qualitative variable and a quantitative continuous variable in one visualization.\nWith seaborn, we can easily create side-by-side plots by specifying both an x and y column.\n\nsns.boxplot(data=wb, x=\"Continent\", y='Gross domestic product: % growth : 2016');\n\n\n\n\n\n\n\n\n\n\n7.6.3 Histograms\nYou are likely familiar with histograms from Data 8. A histogram collects continuous data into bins, then plots this binned data. Each bin reflects the density of datapoints with values that lie between the left and right ends of the bin; in other words, the area of each bin is proportional to the percentage of datapoints it contains.\n\n7.6.3.1 Plotting Histograms\nBelow, we plot a histogram using matplotlib and seaborn. Which graph do you prefer?\n\n# The `edgecolor` argument controls the color of the bin edges\ngni = wb[\"Gross national income per capita, Atlas method: $: 2016\"]\nplt.hist(gni, density=True, edgecolor=\"white\")\n\n# Add labels\nplt.xlabel(\"Gross national income per capita\")\nplt.ylabel(\"Density\")\nplt.title(\"Distribution of gross national income per capita\");\n\n\n\n\n\n\n\n\n\nsns.histplot(data=wb, x=\"Gross national income per capita, Atlas method: $: 2016\", stat=\"density\")\nplt.title(\"Distribution of gross national income per capita\");\n\n\n\n\n\n\n\n\n\n\n7.6.3.2 Overlaid Histograms\nWe can overlay histograms (or density curves) to compare distributions across qualitative categories.\nThe hue parameter of sns.histplot specifies the column that should be used to determine the color of each category. hue can be used in many seaborn plotting functions.\nNotice that the resulting plot includes a legend describing which color corresponds to each hemisphere – a legend should always be included if color is used to encode information in a visualization!\n\n# Create a new variable to store the hemisphere in which each country is located\nnorth = [\"Asia\", \"Europe\", \"N. America\"]\nsouth = [\"Africa\", \"Oceania\", \"S. America\"]\nwb.loc[wb[\"Continent\"].isin(north), \"Hemisphere\"] = \"Northern\"\nwb.loc[wb[\"Continent\"].isin(south), \"Hemisphere\"] = \"Southern\"\n\n\nsns.histplot(data=wb, x=\"Gross national income per capita, Atlas method: $: 2016\", hue=\"Hemisphere\", stat=\"density\")\nplt.title(\"Distribution of gross national income per capita\");\n\n\n\n\n\n\n\n\nAgain, each bin of a histogram is scaled such that its area is proportional to the percentage of all datapoints that it contains.\n\ndensities, bins, _ = plt.hist(gni, density=True, edgecolor=\"white\", bins=5)\nplt.xlabel(\"Gross national income per capita\")\nplt.ylabel(\"Density\")\n\nprint(f\"First bin has width {bins[1]-bins[0]} and height {densities[0]}\")\nprint(f\"This corresponds to {bins[1]-bins[0]} * {densities[0]} = {(bins[1]-bins[0])*densities[0]*100}% of the data\")\n\nFirst bin has width 16410.0 and height 4.7741589911386953e-05\nThis corresponds to 16410.0 * 4.7741589911386953e-05 = 78.343949044586% of the data\n\n\n\n\n\n\n\n\n\n\n\n7.6.3.3 Evaluating Histograms\nHistograms allow us to assess a distribution by their shape. There are a few properties of histograms we can analyze:\n\nSkewness and Tails\n\nSkewed left vs skewed right\nLeft tail vs right tail\n\nOutliers\n\nUsing percentiles\n\nModes\n\nMost commonly occuring data\n\n\n\n7.6.3.3.1 Skewness and Tails\nThe skew of a histogram describes the direction in which its “tail” extends. - A distribution with a long right tail is skewed right (such as Gross national income per capita). In a right-skewed distribution, the few large outliers “pull” the mean to the right of the median.\n\nsns.histplot(data = wb, x = 'Gross national income per capita, Atlas method: $: 2016', stat = 'density');\nplt.title('Distribution with a long right tail')\n\nText(0.5, 1.0, 'Distribution with a long right tail')\n\n\n\n\n\n\n\n\n\n\nA distribution with a long left tail is skewed left (such as Access to an improved water source). In a left-skewed distribution, the few small outliers “pull” the mean to the left of the median.\n\nIn the case where a distribution has equal-sized right and left tails, it is symmetric. The mean is approximately equal to the median. Think of mean as the balancing point of the distribution.\n\nsns.histplot(data = wb, x = 'Access to an improved water source: % of population: 2015', stat = 'density');\nplt.title('Distribution with a long left tail')\n\nText(0.5, 1.0, 'Distribution with a long left tail')\n\n\n\n\n\n\n\n\n\n\n\n7.6.3.3.2 Outliers\nLoosely speaking, an outlier is defined as a data point that lies an abnormally large distance away from other values. Let’s make this more concrete. As you may have observed in the box plot infographic earlier, we define outliers to be the data points that fall beyond the whiskers. Specifically, values that are less than the [\\(1^{st}\\) Quartile \\(-\\) (\\(1.5\\times\\) IQR)], or greater than [\\(3^{rd}\\) Quartile \\(+\\) (\\(1.5\\times\\) IQR).]\n\n\n7.6.3.3.3 Modes\nIn Data 100, we describe a “mode” of a histogram as a peak in the distribution. Often, however, it is difficult to determine what counts as its own “peak.” For example, the number of peaks in the distribution of HIV rates across different countries varies depending on the number of histogram bins we plot.\nIf we set the number of bins to 5, the distribution appears unimodal.\n\n# Rename the very long column name for convenience\nwb = wb.rename(columns={'Antiretroviral therapy coverage: % of people living with HIV: 2015':\"HIV rate\"})\n# With 5 bins, it seems that there is only one peak\nsns.histplot(data=wb, x=\"HIV rate\", stat=\"density\", bins=5)\nplt.title(\"5 histogram bins\");\n\n\n\n\n\n\n\n\n\n# With 10 bins, there seem to be two peaks\n\nsns.histplot(data=wb, x=\"HIV rate\", stat=\"density\", bins=10)\nplt.title(\"10 histogram bins\");\n\n\n\n\n\n\n\n\n\n# And with 20 bins, it becomes hard to say what counts as a \"peak\"!\n\nsns.histplot(data=wb, x =\"HIV rate\", stat=\"density\", bins=20)\nplt.title(\"20 histogram bins\");\n\n\n\n\n\n\n\n\nIn part, it is these ambiguities that motivate us to consider using Kernel Density Estimation (KDE), which we will explore more in the next lecture.",
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+    "text": "5.1 Structure\nWe often prefer rectangular data for data analysis. Rectangular structures are easy to manipulate and analyze. A key element of data cleaning is about transforming data to be more rectangular.\nThere are two kinds of rectangular data: tables and matrices. Tables have named columns with different data types and are manipulated using data transformation languages. Matrices contain numeric data of the same type and are manipulated using linear algebra.",
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+    "text": "5.1.1 File Formats\nThere are many file types for storing structured data: TSV, JSON, XML, ASCII, SAS, etc. We’ll only cover CSV, TSV, and JSON in lecture, but you’ll likely encounter other formats as you work with different datasets. Reading documentation is your best bet for understanding how to process the multitude of different file types.\n\n5.1.1.1 CSV\nCSVs, which stand for Comma-Separated Values, are a common tabular data format. In the past two pandas lectures, we briefly touched on the idea of file format: the way data is encoded in a file for storage. Specifically, our elections and babynames datasets were stored and loaded as CSVs:\n\npd.read_csv(\"data/elections.csv\").head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.21\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.79\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.20\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.80\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.57\n\n\n\n\n\n\n\nTo better understand the properties of a CSV, let’s take a look at the first few rows of the raw data file to see what it looks like before being loaded into a DataFrame. We’ll use the repr() function to return the raw string with its special characters:\n\nwith open(\"data/elections.csv\", \"r\") as table:\n    i = 0\n    for row in table:\n        print(repr(row))\n        i += 1\n        if i &gt; 3:\n            break\n\n'Year,Candidate,Party,Popular vote,Result,%\\n'\n'1824,Andrew Jackson,Democratic-Republican,151271,loss,57.21012204\\n'\n'1824,John Quincy Adams,Democratic-Republican,113142,win,42.78987796\\n'\n'1828,Andrew Jackson,Democratic,642806,win,56.20392707\\n'\n\n\nEach row, or record, in the data is delimited by a newline \\n. Each column, or field, in the data is delimited by a comma , (hence, comma-separated!).\n\n\n5.1.1.2 TSV\nAnother common file type is TSV (Tab-Separated Values). In a TSV, records are still delimited by a newline \\n, while fields are delimited by \\t tab character.\nLet’s check out the first few rows of the raw TSV file. Again, we’ll use the repr() function so that print shows the special characters.\n\nwith open(\"data/elections.txt\", \"r\") as table:\n    i = 0\n    for row in table:\n        print(repr(row))\n        i += 1\n        if i &gt; 3:\n            break\n\n'\\ufeffYear\\tCandidate\\tParty\\tPopular vote\\tResult\\t%\\n'\n'1824\\tAndrew Jackson\\tDemocratic-Republican\\t151271\\tloss\\t57.21012204\\n'\n'1824\\tJohn Quincy Adams\\tDemocratic-Republican\\t113142\\twin\\t42.78987796\\n'\n'1828\\tAndrew Jackson\\tDemocratic\\t642806\\twin\\t56.20392707\\n'\n\n\nTSVs can be loaded into pandas using pd.read_csv. We’ll need to specify the delimiter with parametersep='\\t' (documentation).\n\npd.read_csv(\"data/elections.txt\", sep='\\t').head(3)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.21\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.79\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.20\n\n\n\n\n\n\n\nAn issue with CSVs and TSVs comes up whenever there are commas or tabs within the records. How does pandas differentiate between a comma delimiter vs. a comma within the field itself, for example 8,900? To remedy this, check out the quotechar parameter.\n\n\n5.1.1.3 JSON\nJSON (JavaScript Object Notation) files behave similarly to Python dictionaries. A raw JSON is shown below.\n\nwith open(\"data/elections.json\", \"r\") as table:\n    i = 0\n    for row in table:\n        print(row)\n        i += 1\n        if i &gt; 8:\n            break\n\n[\n\n {\n\n   \"Year\": 1824,\n\n   \"Candidate\": \"Andrew Jackson\",\n\n   \"Party\": \"Democratic-Republican\",\n\n   \"Popular vote\": 151271,\n\n   \"Result\": \"loss\",\n\n   \"%\": 57.21012204\n\n },\n\n\n\nJSON files can be loaded into pandas using pd.read_json.\n\npd.read_json('data/elections.json').head(3)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.21\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.79\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.20\n\n\n\n\n\n\n\n\n5.1.1.3.1 EDA with JSON: Berkeley COVID-19 Data\nThe City of Berkeley Open Data website has a dataset with COVID-19 Confirmed Cases among Berkeley residents by date. Let’s download the file and save it as a JSON (note the source URL file type is also a JSON). In the interest of reproducible data science, we will download the data programatically. We have defined some helper functions in the ds100_utils.py file that we can reuse these helper functions in many different notebooks.\n\nfrom ds100_utils import fetch_and_cache\n\ncovid_file = fetch_and_cache(\n    \"https://data.cityofberkeley.info/api/views/xn6j-b766/rows.json?accessType=DOWNLOAD\",\n    \"confirmed-cases.json\",\n    force=False)\ncovid_file          # a file path wrapper object\n\nUsing cached version that was downloaded (UTC): Wed Aug 28 15:54:12 2024\n\n\nPosixPath('data/confirmed-cases.json')\n\n\n\n5.1.1.3.1.1 File Size\nLet’s start our analysis by getting a rough estimate of the size of the dataset to inform the tools we use to view the data. For relatively small datasets, we can use a text editor or spreadsheet. For larger datasets, more programmatic exploration or distributed computing tools may be more fitting. Here we will use Python tools to probe the file.\nSince there seem to be text files, let’s investigate the number of lines, which often corresponds to the number of records\n\nimport os\n\nprint(covid_file, \"is\", os.path.getsize(covid_file) / 1e6, \"MB\")\n\nwith open(covid_file, \"r\") as f:\n    print(covid_file, \"is\", sum(1 for l in f), \"lines.\")\n\ndata/confirmed-cases.json is 0.116367 MB\ndata/confirmed-cases.json is 1110 lines.\n\n\n\n\n5.1.1.3.1.2 Unix Commands\nAs part of the EDA workflow, Unix commands can come in very handy. In fact, there’s an entire book called “Data Science at the Command Line” that explores this idea in depth! In Jupyter/IPython, you can prefix lines with ! to execute arbitrary Unix commands, and within those lines, you can refer to Python variables and expressions with the syntax {expr}.\nHere, we use the ls command to list files, using the -lh flags, which request “long format with information in human-readable form.” We also use the wc command for “word count,” but with the -l flag, which asks for line counts instead of words.\nThese two give us the same information as the code above, albeit in a slightly different form:\n\n!ls -lh {covid_file}\n!wc -l {covid_file}\n\n-rw-r--r--  1 xiaoruiliu  staff   114K Aug 28 15:54 data/confirmed-cases.json\n    1109 data/confirmed-cases.json\n\n\n\n\n5.1.1.3.1.3 File Contents\nLet’s explore the data format using Python.\n\nwith open(covid_file, \"r\") as f:\n    for i, row in enumerate(f):\n        print(repr(row)) # print raw strings\n        if i &gt;= 4: break\n\n'{\\n'\n'  \"meta\" : {\\n'\n'    \"view\" : {\\n'\n'      \"id\" : \"xn6j-b766\",\\n'\n'      \"name\" : \"COVID-19 Confirmed Cases\",\\n'\n\n\nWe can use the head Unix command (which is where pandas’ head method comes from!) to see the first few lines of the file:\n\n!head -5 {covid_file}\n\n{\n  \"meta\" : {\n    \"view\" : {\n      \"id\" : \"xn6j-b766\",\n      \"name\" : \"COVID-19 Confirmed Cases\",\n\n\nIn order to load the JSON file into pandas, Let’s first do some EDA with Oython’s json package to understand the particular structure of this JSON file so that we can decide what (if anything) to load into pandas. Python has relatively good support for JSON data since it closely matches the internal python object model. In the following cell we import the entire JSON datafile into a python dictionary using the json package.\n\nimport json\n\nwith open(covid_file, \"rb\") as f:\n    covid_json = json.load(f)\n\nThe covid_json variable is now a dictionary encoding the data in the file:\n\ntype(covid_json)\n\ndict\n\n\nWe can examine what keys are in the top level JSON object by listing out the keys.\n\ncovid_json.keys()\n\ndict_keys(['meta', 'data'])\n\n\nObservation: The JSON dictionary contains a meta key which likely refers to metadata (data about the data). Metadata is often maintained with the data and can be a good source of additional information.\nWe can investigate the metadata further by examining the keys associated with the metadata.\n\ncovid_json['meta'].keys()\n\ndict_keys(['view'])\n\n\nThe meta key contains another dictionary called view. This likely refers to metadata about a particular “view” of some underlying database. We will learn more about views when we study SQL later in the class.\n\ncovid_json['meta']['view'].keys()\n\ndict_keys(['id', 'name', 'assetType', 'attribution', 'averageRating', 'category', 'createdAt', 'description', 'displayType', 'downloadCount', 'hideFromCatalog', 'hideFromDataJson', 'newBackend', 'numberOfComments', 'oid', 'provenance', 'publicationAppendEnabled', 'publicationDate', 'publicationGroup', 'publicationStage', 'rowsUpdatedAt', 'rowsUpdatedBy', 'tableId', 'totalTimesRated', 'viewCount', 'viewLastModified', 'viewType', 'approvals', 'columns', 'grants', 'metadata', 'owner', 'query', 'rights', 'tableAuthor', 'tags', 'flags'])\n\n\nNotice that this a nested/recursive data structure. As we dig deeper we reveal more and more keys and the corresponding data:\nmeta\n|-&gt; data\n    | ... (haven't explored yet)\n|-&gt; view\n    | -&gt; id\n    | -&gt; name\n    | -&gt; attribution \n    ...\n    | -&gt; description\n    ...\n    | -&gt; columns\n    ...\nThere is a key called description in the view sub dictionary. This likely contains a description of the data:\n\nprint(covid_json['meta']['view']['description'])\n\nCounts of confirmed COVID-19 cases among Berkeley residents by date.\n\n\n\n\n5.1.1.3.1.4 Examining the Data Field for Records\nWe can look at a few entries in the data field. This is what we’ll load into pandas.\n\nfor i in range(3):\n    print(f\"{i:03} | {covid_json['data'][i]}\")\n\n000 | ['row-kzbg.v7my-c3y2', '00000000-0000-0000-0405-CB14DE51DAA7', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-28T00:00:00', '1', '1']\n001 | ['row-jkyx_9u4r-h2yw', '00000000-0000-0000-F806-86D0DBE0E17F', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-29T00:00:00', '0', '1']\n002 | ['row-qifg_4aug-y3ym', '00000000-0000-0000-2DCE-4D1872F9B216', 0, 1643733903, None, 1643733903, None, '{ }', '2020-03-01T00:00:00', '0', '1']\n\n\nObservations: * These look like equal-length records, so maybe data is a table! * But what do each of values in the record mean? Where can we find column headers?\nFor that, we’ll need the columns key in the metadata dictionary. This returns a list:\n\ntype(covid_json['meta']['view']['columns'])\n\nlist\n\n\n\n\n5.1.1.3.1.5 Summary of exploring the JSON file\n\nThe above metadata tells us a lot about the columns in the data including column names, potential data anomalies, and a basic statistic.\nBecause of its non-tabular structure, JSON makes it easier (than CSV) to create self-documenting data, meaning that information about the data is stored in the same file as the data.\nSelf-documenting data can be helpful since it maintains its own description and these descriptions are more likely to be updated as data changes.\n\n\n\n5.1.1.3.1.6 Loading COVID Data into pandas\nFinally, let’s load the data (not the metadata) into a pandas DataFrame. In the following block of code we:\n\nTranslate the JSON records into a DataFrame:\n\nfields: covid_json['meta']['view']['columns']\nrecords: covid_json['data']\n\nRemove columns that have no metadata description. This would be a bad idea in general, but here we remove these columns since the above analysis suggests they are unlikely to contain useful information.\nExamine the tail of the table.\n\n\n# Load the data from JSON and assign column titles\ncovid = pd.DataFrame(\n    covid_json['data'],\n    columns=[c['name'] for c in covid_json['meta']['view']['columns']])\n\ncovid.tail()\n\n\n\n\n\n\n\n\nsid\nid\nposition\ncreated_at\ncreated_meta\nupdated_at\nupdated_meta\nmeta\nDate\nNew Cases\nCumulative Cases\n\n\n\n\n699\nrow-49b6_x8zv.gyum\n00000000-0000-0000-A18C-9174A6D05774\n0\n1643733903\nNone\n1643733903\nNone\n{ }\n2022-01-27T00:00:00\n106\n10694\n\n\n700\nrow-gs55-p5em.y4v9\n00000000-0000-0000-F41D-5724AEABB4D6\n0\n1643733903\nNone\n1643733903\nNone\n{ }\n2022-01-28T00:00:00\n223\n10917\n\n\n701\nrow-3pyj.tf95-qu67\n00000000-0000-0000-BEE3-B0188D2518BD\n0\n1643733903\nNone\n1643733903\nNone\n{ }\n2022-01-29T00:00:00\n139\n11056\n\n\n702\nrow-cgnd.8syv.jvjn\n00000000-0000-0000-C318-63CF75F7F740\n0\n1643733903\nNone\n1643733903\nNone\n{ }\n2022-01-30T00:00:00\n33\n11089\n\n\n703\nrow-qywv_24x6-237y\n00000000-0000-0000-FE92-9789FED3AA20\n0\n1643733903\nNone\n1643733903\nNone\n{ }\n2022-01-31T00:00:00\n42\n11131\n\n\n\n\n\n\n\n\n\n\n\n\n5.1.2 Primary and Foreign Keys\nLast time, we introduced .merge as the pandas method for joining multiple DataFrames together. In our discussion of joins, we touched on the idea of using a “key” to determine what rows should be merged from each table. Let’s take a moment to examine this idea more closely.\nThe primary key is the column or set of columns in a table that uniquely determine the values of the remaining columns. It can be thought of as the unique identifier for each individual row in the table. For example, a table of Data 100 students might use each student’s Cal ID as the primary key.\n\n\n\n\n\n\n\n\n\nCal ID\nName\nMajor\n\n\n\n\n0\n3034619471\nOski\nData Science\n\n\n1\n3035619472\nOllie\nComputer Science\n\n\n2\n3025619473\nOrrie\nData Science\n\n\n3\n3046789372\nOllie\nEconomics\n\n\n\n\n\n\n\nThe foreign key is the column or set of columns in a table that reference primary keys in other tables. Knowing a dataset’s foreign keys can be useful when assigning the left_on and right_on parameters of .merge. In the table of office hour tickets below, \"Cal ID\" is a foreign key referencing the previous table.\n\n\n\n\n\n\n\n\n\nOH Request\nCal ID\nQuestion\n\n\n\n\n0\n1\n3034619471\nHW 2 Q1\n\n\n1\n2\n3035619472\nHW 2 Q3\n\n\n2\n3\n3025619473\nLab 3 Q4\n\n\n3\n4\n3035619472\nHW 2 Q7\n\n\n\n\n\n\n\n\n\n5.1.3 Variable Types\nVariables are columns. A variable is a measurement of a particular concept. Variables have two common properties: data type/storage type and variable type/feature type. The data type of a variable indicates how each variable value is stored in memory (integer, floating point, boolean, etc.) and affects which pandas functions are used. The variable type is a conceptualized measurement of information (and therefore indicates what values a variable can take on). Variable type is identified through expert knowledge, exploring the data itself, or consulting the data codebook. The variable type affects how one visualizes and inteprets the data. In this class, “variable types” are conceptual.\nAfter loading data into a file, it’s a good idea to take the time to understand what pieces of information are encoded in the dataset. In particular, we want to identify what variable types are present in our data. Broadly speaking, we can categorize variables into one of two overarching types.\nQuantitative variables describe some numeric quantity or amount. We can divide quantitative data further into:\n\nContinuous quantitative variables: numeric data that can be measured on a continuous scale to arbitrary precision. Continuous variables do not have a strict set of possible values – they can be recorded to any number of decimal places. For example, weights, GPA, or CO2 concentrations.\nDiscrete quantitative variables: numeric data that can only take on a finite set of possible values. For example, someone’s age or the number of siblings they have.\n\nQualitative variables, also known as categorical variables, describe data that isn’t measuring some quantity or amount. The sub-categories of categorical data are:\n\nOrdinal qualitative variables: categories with ordered levels. Specifically, ordinal variables are those where the difference between levels has no consistent, quantifiable meaning. Some examples include levels of education (high school, undergrad, grad, etc.), income bracket (low, medium, high), or Yelp rating.\nNominal qualitative variables: categories with no specific order. For example, someone’s political affiliation or Cal ID number.\n\n\n\n\nClassification of variable types\n\n\nNote that many variables don’t sit neatly in just one of these categories. Qualitative variables could have numeric levels, and conversely, quantitative variables could be stored as strings.",
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+    "text": "5.2 Granularity, Scope, and Temporality\nAfter understanding the structure of the dataset, the next task is to determine what exactly the data represents. We’ll do so by considering the data’s granularity, scope, and temporality.\n\n5.2.1 Granularity\nThe granularity of a dataset is what a single row represents. You can also think of it as the level of detail included in the data. To determine the data’s granularity, ask: what does each row in the dataset represent? Fine-grained data contains a high level of detail, with a single row representing a small individual unit. For example, each record may represent one person. Coarse-grained data is encoded such that a single row represents a large individual unit – for example, each record may represent a group of people.\n\n\n5.2.2 Scope\nThe scope of a dataset is the subset of the population covered by the data. If we were investigating student performance in Data Science courses, a dataset with a narrow scope might encompass all students enrolled in Data 100 whereas a dataset with an expansive scope might encompass all students in California.\n\n\n5.2.3 Temporality\nThe temporality of a dataset describes the periodicity over which the data was collected as well as when the data was most recently collected or updated.\nTime and date fields of a dataset could represent a few things:\n\nwhen the “event” happened\nwhen the data was collected, or when it was entered into the system\nwhen the data was copied into the database\n\nTo fully understand the temporality of the data, it also may be necessary to standardize time zones or inspect recurring time-based trends in the data (do patterns recur in 24-hour periods? Over the course of a month? Seasonally?). The convention for standardizing time is the Coordinated Universal Time (UTC), an international time standard measured at 0 degrees latitude that stays consistent throughout the year (no daylight savings). We can represent Berkeley’s time zone, Pacific Standard Time (PST), as UTC-7 (with daylight savings).\n\n5.2.3.1 Temporality with pandas’ dt accessors\nLet’s briefly look at how we can use pandas’ dt accessors to work with dates/times in a dataset using the dataset you’ll see in Lab 3: the Berkeley PD Calls for Service dataset.\n\n\nCode\ncalls = pd.read_csv(\"data/Berkeley_PD_-_Calls_for_Service.csv\")\ncalls.head()\n\n\n\n\n\n\n\n\n\nCASENO\nOFFENSE\nEVENTDT\nEVENTTM\nCVLEGEND\nCVDOW\nInDbDate\nBlock_Location\nBLKADDR\nCity\nState\n\n\n\n\n0\n21014296\nTHEFT MISD. (UNDER $950)\n04/01/2021 12:00:00 AM\n10:58\nLARCENY\n4\n06/15/2021 12:00:00 AM\nBerkeley, CA\\n(37.869058, -122.270455)\nNaN\nBerkeley\nCA\n\n\n1\n21014391\nTHEFT MISD. (UNDER $950)\n04/01/2021 12:00:00 AM\n10:38\nLARCENY\n4\n06/15/2021 12:00:00 AM\nBerkeley, CA\\n(37.869058, -122.270455)\nNaN\nBerkeley\nCA\n\n\n2\n21090494\nTHEFT MISD. (UNDER $950)\n04/19/2021 12:00:00 AM\n12:15\nLARCENY\n1\n06/15/2021 12:00:00 AM\n2100 BLOCK HASTE ST\\nBerkeley, CA\\n(37.864908,...\n2100 BLOCK HASTE ST\nBerkeley\nCA\n\n\n3\n21090204\nTHEFT FELONY (OVER $950)\n02/13/2021 12:00:00 AM\n17:00\nLARCENY\n6\n06/15/2021 12:00:00 AM\n2600 BLOCK WARRING ST\\nBerkeley, CA\\n(37.86393...\n2600 BLOCK WARRING ST\nBerkeley\nCA\n\n\n4\n21090179\nBURGLARY AUTO\n02/08/2021 12:00:00 AM\n6:20\nBURGLARY - VEHICLE\n1\n06/15/2021 12:00:00 AM\n2700 BLOCK GARBER ST\\nBerkeley, CA\\n(37.86066,...\n2700 BLOCK GARBER ST\nBerkeley\nCA\n\n\n\n\n\n\n\nLooks like there are three columns with dates/times: EVENTDT, EVENTTM, and InDbDate.\nMost likely, EVENTDT stands for the date when the event took place, EVENTTM stands for the time of day the event took place (in 24-hr format), and InDbDate is the date this call is recorded onto the database.\nIf we check the data type of these columns, we will see they are stored as strings. We can convert them to datetime objects using pandas to_datetime function.\n\ncalls[\"EVENTDT\"] = pd.to_datetime(calls[\"EVENTDT\"])\ncalls.head()\n\n/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19259/874729699.py:1: UserWarning:\n\nCould not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n\n\n\n\n\n\n\n\n\n\nCASENO\nOFFENSE\nEVENTDT\nEVENTTM\nCVLEGEND\nCVDOW\nInDbDate\nBlock_Location\nBLKADDR\nCity\nState\n\n\n\n\n0\n21014296\nTHEFT MISD. (UNDER $950)\n2021-04-01\n10:58\nLARCENY\n4\n06/15/2021 12:00:00 AM\nBerkeley, CA\\n(37.869058, -122.270455)\nNaN\nBerkeley\nCA\n\n\n1\n21014391\nTHEFT MISD. (UNDER $950)\n2021-04-01\n10:38\nLARCENY\n4\n06/15/2021 12:00:00 AM\nBerkeley, CA\\n(37.869058, -122.270455)\nNaN\nBerkeley\nCA\n\n\n2\n21090494\nTHEFT MISD. (UNDER $950)\n2021-04-19\n12:15\nLARCENY\n1\n06/15/2021 12:00:00 AM\n2100 BLOCK HASTE ST\\nBerkeley, CA\\n(37.864908,...\n2100 BLOCK HASTE ST\nBerkeley\nCA\n\n\n3\n21090204\nTHEFT FELONY (OVER $950)\n2021-02-13\n17:00\nLARCENY\n6\n06/15/2021 12:00:00 AM\n2600 BLOCK WARRING ST\\nBerkeley, CA\\n(37.86393...\n2600 BLOCK WARRING ST\nBerkeley\nCA\n\n\n4\n21090179\nBURGLARY AUTO\n2021-02-08\n6:20\nBURGLARY - VEHICLE\n1\n06/15/2021 12:00:00 AM\n2700 BLOCK GARBER ST\\nBerkeley, CA\\n(37.86066,...\n2700 BLOCK GARBER ST\nBerkeley\nCA\n\n\n\n\n\n\n\nNow, we can use the dt accessor on this column.\nWe can get the month:\n\ncalls[\"EVENTDT\"].dt.month.head()\n\n0    4\n1    4\n2    4\n3    2\n4    2\nName: EVENTDT, dtype: int32\n\n\nWhich day of the week the date is on:\n\ncalls[\"EVENTDT\"].dt.dayofweek.head()\n\n0    3\n1    3\n2    0\n3    5\n4    0\nName: EVENTDT, dtype: int32\n\n\nCheck the mimimum values to see if there are any suspicious-looking, 70s dates:\n\ncalls.sort_values(\"EVENTDT\").head()\n\n\n\n\n\n\n\n\nCASENO\nOFFENSE\nEVENTDT\nEVENTTM\nCVLEGEND\nCVDOW\nInDbDate\nBlock_Location\nBLKADDR\nCity\nState\n\n\n\n\n2513\n20057398\nBURGLARY COMMERCIAL\n2020-12-17\n16:05\nBURGLARY - COMMERCIAL\n4\n06/15/2021 12:00:00 AM\n600 BLOCK GILMAN ST\\nBerkeley, CA\\n(37.878405,...\n600 BLOCK GILMAN ST\nBerkeley\nCA\n\n\n624\n20057207\nASSAULT/BATTERY MISD.\n2020-12-17\n16:50\nASSAULT\n4\n06/15/2021 12:00:00 AM\n2100 BLOCK SHATTUCK AVE\\nBerkeley, CA\\n(37.871...\n2100 BLOCK SHATTUCK AVE\nBerkeley\nCA\n\n\n154\n20092214\nTHEFT FROM AUTO\n2020-12-17\n18:30\nLARCENY - FROM VEHICLE\n4\n06/15/2021 12:00:00 AM\n800 BLOCK SHATTUCK AVE\\nBerkeley, CA\\n(37.8918...\n800 BLOCK SHATTUCK AVE\nBerkeley\nCA\n\n\n659\n20057324\nTHEFT MISD. (UNDER $950)\n2020-12-17\n15:44\nLARCENY\n4\n06/15/2021 12:00:00 AM\n1800 BLOCK 4TH ST\\nBerkeley, CA\\n(37.869888, -...\n1800 BLOCK 4TH ST\nBerkeley\nCA\n\n\n993\n20057573\nBURGLARY RESIDENTIAL\n2020-12-17\n22:15\nBURGLARY - RESIDENTIAL\n4\n06/15/2021 12:00:00 AM\n1700 BLOCK STUART ST\\nBerkeley, CA\\n(37.857495...\n1700 BLOCK STUART ST\nBerkeley\nCA\n\n\n\n\n\n\n\nDoesn’t look like it! We are good!\nWe can also do many things with the dt accessor like switching time zones and converting time back to UNIX/POSIX time. Check out the documentation on .dt accessor and time series/date functionality.",
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+    "text": "5.3 Faithfulness\nAt this stage in our data cleaning and EDA workflow, we’ve achieved quite a lot: we’ve identified how our data is structured, come to terms with what information it encodes, and gained insight as to how it was generated. Throughout this process, we should always recall the original intent of our work in Data Science – to use data to better understand and model the real world. To achieve this goal, we need to ensure that the data we use is faithful to reality; that is, that our data accurately captures the “real world.”\nData used in research or industry is often “messy” – there may be errors or inaccuracies that impact the faithfulness of the dataset. Signs that data may not be faithful include:\n\nUnrealistic or “incorrect” values, such as negative counts, locations that don’t exist, or dates set in the future\nViolations of obvious dependencies, like an age that does not match a birthday\nClear signs that data was entered by hand, which can lead to spelling errors or fields that are incorrectly shifted\nSigns of data falsification, such as fake email addresses or repeated use of the same names\nDuplicated records or fields containing the same information\nTruncated data, e.g. Microsoft Excel would limit the number of rows to 655536 and the number of columns to 255\n\nWe often solve some of these more common issues in the following ways:\n\nSpelling errors: apply corrections or drop records that aren’t in a dictionary\nTime zone inconsistencies: convert to a common time zone (e.g. UTC)\nDuplicated records or fields: identify and eliminate duplicates (using primary keys)\nUnspecified or inconsistent units: infer the units and check that values are in reasonable ranges in the data\n\n\n5.3.1 Missing Values\nAnother common issue encountered with real-world datasets is that of missing data. One strategy to resolve this is to simply drop any records with missing values from the dataset. This does, however, introduce the risk of inducing biases – it is possible that the missing or corrupt records may be systemically related to some feature of interest in the data. Another solution is to keep the data as NaN values.\nA third method to address missing data is to perform imputation: infer the missing values using other data available in the dataset. There is a wide variety of imputation techniques that can be implemented; some of the most common are listed below.\n\nAverage imputation: replace missing values with the average value for that field\nHot deck imputation: replace missing values with some random value\nRegression imputation: develop a model to predict missing values and replace with the predicted value from the model.\nMultiple imputation: replace missing values with multiple random values\n\nRegardless of the strategy used to deal with missing data, we should think carefully about why particular records or fields may be missing – this can help inform whether or not the absence of these values is significant or meaningful.",
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+    "section": "5.4 EDA Demo 1: Tuberculosis in the United States",
+    "text": "5.4 EDA Demo 1: Tuberculosis in the United States\nNow, let’s walk through the data-cleaning and EDA workflow to see what can we learn about the presence of Tuberculosis in the United States!\nWe will examine the data included in the original CDC article published in 2021.\n\n5.4.1 CSVs and Field Names\nSuppose Table 1 was saved as a CSV file located in data/cdc_tuberculosis.csv.\nWe can then explore the CSV (which is a text file, and does not contain binary-encoded data) in many ways: 1. Using a text editor like emacs, vim, VSCode, etc. 2. Opening the CSV directly in DataHub (read-only), Excel, Google Sheets, etc. 3. The Python file object 4. pandas, using pd.read_csv()\nTo try out options 1 and 2, you can view or download the Tuberculosis from the lecture demo notebook under the data folder in the left hand menu. Notice how the CSV file is a type of rectangular data (i.e., tabular data) stored as comma-separated values.\nNext, let’s try out option 3 using the Python file object. We’ll look at the first four lines:\n\n\nCode\nwith open(\"data/cdc_tuberculosis.csv\", \"r\") as f:\n    i = 0\n    for row in f:\n        print(row)\n        i += 1\n        if i &gt; 3:\n            break\n\n\n,No. of TB cases,,,TB incidence,,\n\nU.S. jurisdiction,2019,2020,2021,2019,2020,2021\n\nTotal,\"8,900\",\"7,173\",\"7,860\",2.71,2.16,2.37\n\nAlabama,87,72,92,1.77,1.43,1.83\n\n\n\nWhoa, why are there blank lines interspaced between the lines of the CSV?\nYou may recall that all line breaks in text files are encoded as the special newline character \\n. Python’s print() prints each string (including the newline), and an additional newline on top of that.\nIf you’re curious, we can use the repr() function to return the raw string with all special characters:\n\n\nCode\nwith open(\"data/cdc_tuberculosis.csv\", \"r\") as f:\n    i = 0\n    for row in f:\n        print(repr(row)) # print raw strings\n        i += 1\n        if i &gt; 3:\n            break\n\n\n',No. of TB cases,,,TB incidence,,\\n'\n'U.S. jurisdiction,2019,2020,2021,2019,2020,2021\\n'\n'Total,\"8,900\",\"7,173\",\"7,860\",2.71,2.16,2.37\\n'\n'Alabama,87,72,92,1.77,1.43,1.83\\n'\n\n\nFinally, let’s try option 4 and use the tried-and-true Data 100 approach: pandas.\n\ntb_df = pd.read_csv(\"data/cdc_tuberculosis.csv\")\ntb_df.head()\n\n\n\n\n\n\n\n\nUnnamed: 0\nNo. of TB cases\nUnnamed: 2\nUnnamed: 3\nTB incidence\nUnnamed: 5\nUnnamed: 6\n\n\n\n\n0\nU.S. jurisdiction\n2019\n2020\n2021\n2019.00\n2020.00\n2021.00\n\n\n1\nTotal\n8,900\n7,173\n7,860\n2.71\n2.16\n2.37\n\n\n2\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\n3\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\n4\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\n\n\n\n\n\nYou may notice some strange things about this table: what’s up with the “Unnamed” column names and the first row?\nCongratulations — you’re ready to wrangle your data! Because of how things are stored, we’ll need to clean the data a bit to name our columns better.\nA reasonable first step is to identify the row with the right header. The pd.read_csv() function (documentation) has the convenient header parameter that we can set to use the elements in row 1 as the appropriate columns:\n\ntb_df = pd.read_csv(\"data/cdc_tuberculosis.csv\", header=1) # row index\ntb_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\n2019\n2020\n2021\n2019.1\n2020.1\n2021.1\n\n\n\n\n0\nTotal\n8,900\n7,173\n7,860\n2.71\n2.16\n2.37\n\n\n1\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\n2\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\n3\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\n4\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\nWait…but now we can’t differentiate betwen the “Number of TB cases” and “TB incidence” year columns. pandas has tried to make our lives easier by automatically adding “.1” to the latter columns, but this doesn’t help us, as humans, understand the data.\nWe can do this manually with df.rename() (documentation):\n\nrename_dict = {'2019': 'TB cases 2019',\n               '2020': 'TB cases 2020',\n               '2021': 'TB cases 2021',\n               '2019.1': 'TB incidence 2019',\n               '2020.1': 'TB incidence 2020',\n               '2021.1': 'TB incidence 2021'}\ntb_df = tb_df.rename(columns=rename_dict)\ntb_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n\n\n\n\n0\nTotal\n8,900\n7,173\n7,860\n2.71\n2.16\n2.37\n\n\n1\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\n2\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\n3\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\n4\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\n\n\n5.4.2 Record Granularity\nYou might already be wondering: what’s up with that first record?\nRow 0 is what we call a rollup record, or summary record. It’s often useful when displaying tables to humans. The granularity of record 0 (Totals) vs the rest of the records (States) is different.\nOkay, EDA step two. How was the rollup record aggregated?\nLet’s check if Total TB cases is the sum of all state TB cases. If we sum over all rows, we should get 2x the total cases in each of our TB cases by year (why do you think this is?).\n\n\nCode\ntb_df.sum(axis=0)\n\n\nU.S. jurisdiction    TotalAlabamaAlaskaArizonaArkansasCaliforniaCol...\nTB cases 2019        8,9008758183642,111666718245583029973261085237...\nTB cases 2020        7,1737258136591,706525417194122219282169239376...\nTB cases 2021        7,8609258129691,750585443194992281064255127494...\nTB incidence 2019                                               109.94\nTB incidence 2020                                                93.09\nTB incidence 2021                                               102.94\ndtype: object\n\n\nWhoa, what’s going on with the TB cases in 2019, 2020, and 2021? Check out the column types:\n\n\nCode\ntb_df.dtypes\n\n\nU.S. jurisdiction     object\nTB cases 2019         object\nTB cases 2020         object\nTB cases 2021         object\nTB incidence 2019    float64\nTB incidence 2020    float64\nTB incidence 2021    float64\ndtype: object\n\n\nSince there are commas in the values for TB cases, the numbers are read as the object datatype, or storage type (close to the Python string datatype), so pandas is concatenating strings instead of adding integers (recall that Python can “sum”, or concatenate, strings together: \"data\" + \"100\" evaluates to \"data100\").\nFortunately read_csv also has a thousands parameter (documentation):\n\n# improve readability: chaining method calls with outer parentheses/line breaks\ntb_df = (\n    pd.read_csv(\"data/cdc_tuberculosis.csv\", header=1, thousands=',')\n    .rename(columns=rename_dict)\n)\ntb_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n\n\n\n\n0\nTotal\n8900\n7173\n7860\n2.71\n2.16\n2.37\n\n\n1\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\n2\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\n3\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\n4\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\n\ntb_df.sum()\n\nU.S. jurisdiction    TotalAlabamaAlaskaArizonaArkansasCaliforniaCol...\nTB cases 2019                                                    17800\nTB cases 2020                                                    14346\nTB cases 2021                                                    15720\nTB incidence 2019                                               109.94\nTB incidence 2020                                                93.09\nTB incidence 2021                                               102.94\ndtype: object\n\n\nThe total TB cases look right. Phew!\nLet’s just look at the records with state-level granularity:\n\n\nCode\nstate_tb_df = tb_df[1:]\nstate_tb_df.head(5)\n\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n\n\n\n\n1\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\n2\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\n3\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\n4\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n5\nCalifornia\n2111\n1706\n1750\n5.35\n4.32\n4.46\n\n\n\n\n\n\n\n\n\n5.4.3 Gather Census Data\nU.S. Census population estimates source (2019), source (2020-2021).\nRunning the below cells cleans the data. There are a few new methods here: * df.convert_dtypes() (documentation) conveniently converts all float dtypes into ints and is out of scope for the class. * df.drop_na() (documentation) will be explained in more detail next time.\n\n\nCode\n# 2010s census data\ncensus_2010s_df = pd.read_csv(\"data/nst-est2019-01.csv\", header=3, thousands=\",\")\ncensus_2010s_df = (\n    census_2010s_df\n    .reset_index()\n    .drop(columns=[\"index\", \"Census\", \"Estimates Base\"])\n    .rename(columns={\"Unnamed: 0\": \"Geographic Area\"})\n    .convert_dtypes()                 # \"smart\" converting of columns, use at your own risk\n    .dropna()                         # we'll introduce this next time\n)\ncensus_2010s_df['Geographic Area'] = census_2010s_df['Geographic Area'].str.strip('.')\n\n# with pd.option_context('display.min_rows', 30): # shows more rows\n#     display(census_2010s_df)\n    \ncensus_2010s_df.head(5)\n\n\n\n\n\n\n\n\n\nGeographic Area\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\n\n\n\n\n0\nUnited States\n309321666\n311556874\n313830990\n315993715\n318301008\n320635163\n322941311\n324985539\n326687501\n328239523\n\n\n1\nNortheast\n55380134\n55604223\n55775216\n55901806\n56006011\n56034684\n56042330\n56059240\n56046620\n55982803\n\n\n2\nMidwest\n66974416\n67157800\n67336743\n67560379\n67745167\n67860583\n67987540\n68126781\n68236628\n68329004\n\n\n3\nSouth\n114866680\n116006522\n117241208\n118364400\n119624037\n120997341\n122351760\n123542189\n124569433\n125580448\n\n\n4\nWest\n72100436\n72788329\n73477823\n74167130\n74925793\n75742555\n76559681\n77257329\n77834820\n78347268\n\n\n\n\n\n\n\nOccasionally, you will want to modify code that you have imported. To reimport those modifications you can either use python’s importlib library:\nfrom importlib import reload\nreload(utils)\nor use iPython magic which will intelligently import code when files change:\n%load_ext autoreload\n%autoreload 2\n\n\nCode\n# census 2020s data\ncensus_2020s_df = pd.read_csv(\"data/NST-EST2022-POP.csv\", header=3, thousands=\",\")\ncensus_2020s_df = (\n    census_2020s_df\n    .reset_index()\n    .drop(columns=[\"index\", \"Unnamed: 1\"])\n    .rename(columns={\"Unnamed: 0\": \"Geographic Area\"})\n    .convert_dtypes()                 # \"smart\" converting of columns, use at your own risk\n    .dropna()                         # we'll introduce this next time\n)\ncensus_2020s_df['Geographic Area'] = census_2020s_df['Geographic Area'].str.strip('.')\n\ncensus_2020s_df.head(5)\n\n\n\n\n\n\n\n\n\nGeographic Area\n2020\n2021\n2022\n\n\n\n\n0\nUnited States\n331511512\n332031554\n333287557\n\n\n1\nNortheast\n57448898\n57259257\n57040406\n\n\n2\nMidwest\n68961043\n68836505\n68787595\n\n\n3\nSouth\n126450613\n127346029\n128716192\n\n\n4\nWest\n78650958\n78589763\n78743364\n\n\n\n\n\n\n\n\n\n5.4.4 Joining Data (Merging DataFrames)\nTime to merge! Here we use the DataFrame method df1.merge(right=df2, ...) on DataFrame df1 (documentation). Contrast this with the function pd.merge(left=df1, right=df2, ...) (documentation). Feel free to use either.\n\n# merge TB DataFrame with two US census DataFrames\ntb_census_df = (\n    tb_df\n    .merge(right=census_2010s_df,\n           left_on=\"U.S. jurisdiction\", right_on=\"Geographic Area\")\n    .merge(right=census_2020s_df,\n           left_on=\"U.S. jurisdiction\", right_on=\"Geographic Area\")\n)\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\nGeographic Area_x\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\nGeographic Area_y\n2020\n2021\n2022\n\n\n\n\n0\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\nAlabama\n4785437\n4799069\n4815588\n4830081\n4841799\n4852347\n4863525\n4874486\n4887681\n4903185\nAlabama\n5031362\n5049846\n5074296\n\n\n1\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\nAlaska\n713910\n722128\n730443\n737068\n736283\n737498\n741456\n739700\n735139\n731545\nAlaska\n732923\n734182\n733583\n\n\n2\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\nArizona\n6407172\n6472643\n6554978\n6632764\n6730413\n6829676\n6941072\n7044008\n7158024\n7278717\nArizona\n7179943\n7264877\n7359197\n\n\n3\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\nArkansas\n2921964\n2940667\n2952164\n2959400\n2967392\n2978048\n2989918\n3001345\n3009733\n3017804\nArkansas\n3014195\n3028122\n3045637\n\n\n4\nCalifornia\n2111\n1706\n1750\n5.35\n4.32\n4.46\nCalifornia\n37319502\n37638369\n37948800\n38260787\n38596972\n38918045\n39167117\n39358497\n39461588\n39512223\nCalifornia\n39501653\n39142991\n39029342\n\n\n\n\n\n\n\nHaving all of these columns is a little unwieldy. We could either drop the unneeded columns now, or just merge on smaller census DataFrames. Let’s do the latter.\n\n# try merging again, but cleaner this time\ntb_census_df = (\n    tb_df\n    .merge(right=census_2010s_df[[\"Geographic Area\", \"2019\"]],\n           left_on=\"U.S. jurisdiction\", right_on=\"Geographic Area\")\n    .drop(columns=\"Geographic Area\")\n    .merge(right=census_2020s_df[[\"Geographic Area\", \"2020\", \"2021\"]],\n           left_on=\"U.S. jurisdiction\", right_on=\"Geographic Area\")\n    .drop(columns=\"Geographic Area\")\n)\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\n\n\n\n\n0\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n4903185\n5031362\n5049846\n\n\n1\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n731545\n732923\n734182\n\n\n2\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n7278717\n7179943\n7264877\n\n\n3\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n3017804\n3014195\n3028122\n\n\n4\nCalifornia\n2111\n1706\n1750\n5.35\n4.32\n4.46\n39512223\n39501653\n39142991\n\n\n\n\n\n\n\n\n\n5.4.5 Reproducing Data: Compute Incidence\nLet’s recompute incidence to make sure we know where the original CDC numbers came from.\nFrom the CDC report: TB incidence is computed as “Cases per 100,000 persons using mid-year population estimates from the U.S. Census Bureau.”\nIf we define a group as 100,000 people, then we can compute the TB incidence for a given state population as\n\\[\\text{TB incidence} = \\frac{\\text{TB cases in population}}{\\text{groups in population}} = \\frac{\\text{TB cases in population}}{\\text{population}/100000} \\]\n\\[= \\frac{\\text{TB cases in population}}{\\text{population}} \\times 100000\\]\nLet’s try this for 2019:\n\ntb_census_df[\"recompute incidence 2019\"] = tb_census_df[\"TB cases 2019\"]/tb_census_df[\"2019\"]*100000\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\nrecompute incidence 2019\n\n\n\n\n0\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n4903185\n5031362\n5049846\n1.77\n\n\n1\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n731545\n732923\n734182\n7.93\n\n\n2\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n7278717\n7179943\n7264877\n2.51\n\n\n3\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n3017804\n3014195\n3028122\n2.12\n\n\n4\nCalifornia\n2111\n1706\n1750\n5.35\n4.32\n4.46\n39512223\n39501653\n39142991\n5.34\n\n\n\n\n\n\n\nAwesome!!!\nLet’s use a for-loop and Python format strings to compute TB incidence for all years. Python f-strings are just used for the purposes of this demo, but they’re handy to know when you explore data beyond this course (documentation).\n\n# recompute incidence for all years\nfor year in [2019, 2020, 2021]:\n    tb_census_df[f\"recompute incidence {year}\"] = tb_census_df[f\"TB cases {year}\"]/tb_census_df[f\"{year}\"]*100000\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nU.S. jurisdiction\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\nrecompute incidence 2019\nrecompute incidence 2020\nrecompute incidence 2021\n\n\n\n\n0\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n4903185\n5031362\n5049846\n1.77\n1.43\n1.82\n\n\n1\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n731545\n732923\n734182\n7.93\n7.91\n7.90\n\n\n2\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n7278717\n7179943\n7264877\n2.51\n1.89\n1.78\n\n\n3\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n3017804\n3014195\n3028122\n2.12\n1.96\n2.28\n\n\n4\nCalifornia\n2111\n1706\n1750\n5.35\n4.32\n4.46\n39512223\n39501653\n39142991\n5.34\n4.32\n4.47\n\n\n\n\n\n\n\nThese numbers look pretty close!!! There are a few errors in the hundredths place, particularly in 2021. It may be useful to further explore reasons behind this discrepancy.\n\ntb_census_df.describe()\n\n\n\n\n\n\n\n\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\nrecompute incidence 2019\nrecompute incidence 2020\nrecompute incidence 2021\n\n\n\n\ncount\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n51.00\n\n\nmean\n174.51\n140.65\n154.12\n2.10\n1.78\n1.97\n6436069.08\n6500225.73\n6510422.63\n2.10\n1.78\n1.97\n\n\nstd\n341.74\n271.06\n286.78\n1.50\n1.34\n1.48\n7360660.47\n7408168.46\n7394300.08\n1.50\n1.34\n1.47\n\n\nmin\n1.00\n0.00\n2.00\n0.17\n0.00\n0.21\n578759.00\n577605.00\n579483.00\n0.17\n0.00\n0.21\n\n\n25%\n25.50\n29.00\n23.00\n1.29\n1.21\n1.23\n1789606.00\n1820311.00\n1844920.00\n1.30\n1.21\n1.23\n\n\n50%\n70.00\n67.00\n69.00\n1.80\n1.52\n1.70\n4467673.00\n4507445.00\n4506589.00\n1.81\n1.52\n1.69\n\n\n75%\n180.50\n139.00\n150.00\n2.58\n1.99\n2.22\n7446805.00\n7451987.00\n7502811.00\n2.58\n1.99\n2.22\n\n\nmax\n2111.00\n1706.00\n1750.00\n7.91\n7.92\n7.92\n39512223.00\n39501653.00\n39142991.00\n7.93\n7.91\n7.90\n\n\n\n\n\n\n\n\n\n5.4.6 Bonus EDA: Reproducing the Reported Statistic\nHow do we reproduce that reported statistic in the original CDC report?\n\nReported TB incidence (cases per 100,000 persons) increased 9.4%, from 2.2 during 2020 to 2.4 during 2021 but was lower than incidence during 2019 (2.7). Increases occurred among both U.S.-born and non–U.S.-born persons.\n\nThis is TB incidence computed across the entire U.S. population! How do we reproduce this? * We need to reproduce the “Total” TB incidences in our rolled record. * But our current tb_census_df only has 51 entries (50 states plus Washington, D.C.). There is no rolled record. * What happened…?\nLet’s get exploring!\nBefore we keep exploring, we’ll set all indexes to more meaningful values, instead of just numbers that pertain to some row at some point. This will make our cleaning slightly easier.\n\n\nCode\ntb_df = tb_df.set_index(\"U.S. jurisdiction\")\ntb_df.head(5)\n\n\n\n\n\n\n\n\n\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n\n\nU.S. jurisdiction\n\n\n\n\n\n\n\n\n\n\nTotal\n8900\n7173\n7860\n2.71\n2.16\n2.37\n\n\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\n\ncensus_2010s_df = census_2010s_df.set_index(\"Geographic Area\")\ncensus_2010s_df.head(5)\n\n\n\n\n\n\n\n\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\n\n\nGeographic Area\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUnited States\n309321666\n311556874\n313830990\n315993715\n318301008\n320635163\n322941311\n324985539\n326687501\n328239523\n\n\nNortheast\n55380134\n55604223\n55775216\n55901806\n56006011\n56034684\n56042330\n56059240\n56046620\n55982803\n\n\nMidwest\n66974416\n67157800\n67336743\n67560379\n67745167\n67860583\n67987540\n68126781\n68236628\n68329004\n\n\nSouth\n114866680\n116006522\n117241208\n118364400\n119624037\n120997341\n122351760\n123542189\n124569433\n125580448\n\n\nWest\n72100436\n72788329\n73477823\n74167130\n74925793\n75742555\n76559681\n77257329\n77834820\n78347268\n\n\n\n\n\n\n\n\ncensus_2020s_df = census_2020s_df.set_index(\"Geographic Area\")\ncensus_2020s_df.head(5)\n\n\n\n\n\n\n\n\n2020\n2021\n2022\n\n\nGeographic Area\n\n\n\n\n\n\n\nUnited States\n331511512\n332031554\n333287557\n\n\nNortheast\n57448898\n57259257\n57040406\n\n\nMidwest\n68961043\n68836505\n68787595\n\n\nSouth\n126450613\n127346029\n128716192\n\n\nWest\n78650958\n78589763\n78743364\n\n\n\n\n\n\n\nIt turns out that our merge above only kept state records, even though our original tb_df had the “Total” rolled record:\n\ntb_df.head()\n\n\n\n\n\n\n\n\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n\n\nU.S. jurisdiction\n\n\n\n\n\n\n\n\n\n\nTotal\n8900\n7173\n7860\n2.71\n2.16\n2.37\n\n\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n\n\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n\n\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n\n\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\nRecall that merge by default does an inner merge by default, meaning that it only preserves keys that are present in both DataFrames.\nThe rolled records in our census DataFrame have different Geographic Area fields, which was the key we merged on:\n\ncensus_2010s_df.head(5)\n\n\n\n\n\n\n\n\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\n\n\nGeographic Area\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUnited States\n309321666\n311556874\n313830990\n315993715\n318301008\n320635163\n322941311\n324985539\n326687501\n328239523\n\n\nNortheast\n55380134\n55604223\n55775216\n55901806\n56006011\n56034684\n56042330\n56059240\n56046620\n55982803\n\n\nMidwest\n66974416\n67157800\n67336743\n67560379\n67745167\n67860583\n67987540\n68126781\n68236628\n68329004\n\n\nSouth\n114866680\n116006522\n117241208\n118364400\n119624037\n120997341\n122351760\n123542189\n124569433\n125580448\n\n\nWest\n72100436\n72788329\n73477823\n74167130\n74925793\n75742555\n76559681\n77257329\n77834820\n78347268\n\n\n\n\n\n\n\nThe Census DataFrame has several rolled records. The aggregate record we are looking for actually has the Geographic Area named “United States”.\nOne straightforward way to get the right merge is to rename the value itself. Because we now have the Geographic Area index, we’ll use df.rename() (documentation):\n\n# rename rolled record for 2010s\ncensus_2010s_df.rename(index={'United States':'Total'}, inplace=True)\ncensus_2010s_df.head(5)\n\n\n\n\n\n\n\n\n2010\n2011\n2012\n2013\n2014\n2015\n2016\n2017\n2018\n2019\n\n\nGeographic Area\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal\n309321666\n311556874\n313830990\n315993715\n318301008\n320635163\n322941311\n324985539\n326687501\n328239523\n\n\nNortheast\n55380134\n55604223\n55775216\n55901806\n56006011\n56034684\n56042330\n56059240\n56046620\n55982803\n\n\nMidwest\n66974416\n67157800\n67336743\n67560379\n67745167\n67860583\n67987540\n68126781\n68236628\n68329004\n\n\nSouth\n114866680\n116006522\n117241208\n118364400\n119624037\n120997341\n122351760\n123542189\n124569433\n125580448\n\n\nWest\n72100436\n72788329\n73477823\n74167130\n74925793\n75742555\n76559681\n77257329\n77834820\n78347268\n\n\n\n\n\n\n\n\n# same, but for 2020s rename rolled record\ncensus_2020s_df.rename(index={'United States':'Total'}, inplace=True)\ncensus_2020s_df.head(5)\n\n\n\n\n\n\n\n\n2020\n2021\n2022\n\n\nGeographic Area\n\n\n\n\n\n\n\nTotal\n331511512\n332031554\n333287557\n\n\nNortheast\n57448898\n57259257\n57040406\n\n\nMidwest\n68961043\n68836505\n68787595\n\n\nSouth\n126450613\n127346029\n128716192\n\n\nWest\n78650958\n78589763\n78743364\n\n\n\n\n\n\n\n\nNext let’s rerun our merge. Note the different chaining, because we are now merging on indexes (df.merge() documentation).\n\ntb_census_df = (\n    tb_df\n    .merge(right=census_2010s_df[[\"2019\"]],\n           left_index=True, right_index=True)\n    .merge(right=census_2020s_df[[\"2020\", \"2021\"]],\n           left_index=True, right_index=True)\n)\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\n\n\n\n\nTotal\n8900\n7173\n7860\n2.71\n2.16\n2.37\n328239523\n331511512\n332031554\n\n\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n4903185\n5031362\n5049846\n\n\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n731545\n732923\n734182\n\n\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n7278717\n7179943\n7264877\n\n\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n3017804\n3014195\n3028122\n\n\n\n\n\n\n\n\nFinally, let’s recompute our incidences:\n\n# recompute incidence for all years\nfor year in [2019, 2020, 2021]:\n    tb_census_df[f\"recompute incidence {year}\"] = tb_census_df[f\"TB cases {year}\"]/tb_census_df[f\"{year}\"]*100000\ntb_census_df.head(5)\n\n\n\n\n\n\n\n\nTB cases 2019\nTB cases 2020\nTB cases 2021\nTB incidence 2019\nTB incidence 2020\nTB incidence 2021\n2019\n2020\n2021\nrecompute incidence 2019\nrecompute incidence 2020\nrecompute incidence 2021\n\n\n\n\nTotal\n8900\n7173\n7860\n2.71\n2.16\n2.37\n328239523\n331511512\n332031554\n2.71\n2.16\n2.37\n\n\nAlabama\n87\n72\n92\n1.77\n1.43\n1.83\n4903185\n5031362\n5049846\n1.77\n1.43\n1.82\n\n\nAlaska\n58\n58\n58\n7.91\n7.92\n7.92\n731545\n732923\n734182\n7.93\n7.91\n7.90\n\n\nArizona\n183\n136\n129\n2.51\n1.89\n1.77\n7278717\n7179943\n7264877\n2.51\n1.89\n1.78\n\n\nArkansas\n64\n59\n69\n2.12\n1.96\n2.28\n3017804\n3014195\n3028122\n2.12\n1.96\n2.28\n\n\n\n\n\n\n\nWe reproduced the total U.S. incidences correctly!\nWe’re almost there. Let’s revisit the quote:\n\nReported TB incidence (cases per 100,000 persons) increased 9.4%, from 2.2 during 2020 to 2.4 during 2021 but was lower than incidence during 2019 (2.7). Increases occurred among both U.S.-born and non–U.S.-born persons.\n\nRecall that percent change from \\(A\\) to \\(B\\) is computed as \\(\\text{percent change} = \\frac{B - A}{A} \\times 100\\).\n\nincidence_2020 = tb_census_df.loc['Total', 'recompute incidence 2020']\nincidence_2020\n\n2.1637257652759883\n\n\n\nincidence_2021 = tb_census_df.loc['Total', 'recompute incidence 2021']\nincidence_2021\n\n2.3672448914298068\n\n\n\ndifference = (incidence_2021 - incidence_2020)/incidence_2020 * 100\ndifference\n\n9.405957511804143",
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+      "<span class='chapter-number'>5</span>  <span class='chapter-title'>Data Cleaning and EDA</span>"
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+    "title": "5  Data Cleaning and EDA",
+    "section": "5.5 EDA Demo 2: Mauna Loa CO2 Data – A Lesson in Data Faithfulness",
+    "text": "5.5 EDA Demo 2: Mauna Loa CO2 Data – A Lesson in Data Faithfulness\nMauna Loa Observatory has been monitoring CO2 concentrations since 1958.\n\nco2_file = \"data/co2_mm_mlo.txt\"\n\nLet’s do some EDA!!\n\n5.5.1 Reading this file into Pandas?\nLet’s instead check out this .txt file. Some questions to keep in mind: Do we trust this file extension? What structure is it?\nLines 71-78 (inclusive) are shown below:\nline number |                            file contents\n\n71          |   #            decimal     average   interpolated    trend    #days\n72          |   #             date                             (season corr)\n73          |   1958   3    1958.208      315.71      315.71      314.62     -1\n74          |   1958   4    1958.292      317.45      317.45      315.29     -1\n75          |   1958   5    1958.375      317.50      317.50      314.71     -1\n76          |   1958   6    1958.458      -99.99      317.10      314.85     -1\n77          |   1958   7    1958.542      315.86      315.86      314.98     -1\n78          |   1958   8    1958.625      314.93      314.93      315.94     -1\nNotice how:\n\nThe values are separated by white space, possibly tabs.\nThe data line up down the rows. For example, the month appears in 7th to 8th position of each line.\nThe 71st and 72nd lines in the file contain column headings split over two lines.\n\nWe can use read_csv to read the data into a pandas DataFrame, and we provide several arguments to specify that the separators are white space, there is no header (we will set our own column names), and to skip the first 72 rows of the file.\n\nco2 = pd.read_csv(\n    co2_file, header = None, skiprows = 72,\n    sep = r'\\s+'       #delimiter for continuous whitespace (stay tuned for regex next lecture))\n)\nco2.head()\n\n\n\n\n\n\n\n\n0\n1\n2\n3\n4\n5\n6\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n3\n1958\n6\n1958.46\n-99.99\n317.10\n314.85\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n\n\n\n\n\nCongratulations! You’ve wrangled the data!\n\n…But our columns aren’t named. We need to do more EDA.\n\n\n5.5.2 Exploring Variable Feature Types\nThe NOAA webpage might have some useful tidbits (in this case it doesn’t).\nUsing this information, we’ll rerun pd.read_csv, but this time with some custom column names.\n\nco2 = pd.read_csv(\n    co2_file, header = None, skiprows = 72,\n    sep = '\\s+', #regex for continuous whitespace (next lecture)\n    names = ['Yr', 'Mo', 'DecDate', 'Avg', 'Int', 'Trend', 'Days']\n)\nco2.head()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n3\n1958\n6\n1958.46\n-99.99\n317.10\n314.85\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n\n\n\n\n\n\n\n5.5.3 Visualizing CO2\nScientific studies tend to have very clean data, right…? Let’s jump right in and make a time series plot of CO2 monthly averages.\n\n\nCode\nsns.lineplot(x='DecDate', y='Avg', data=co2);\n\n\n\n\n\n\n\n\n\nThe code above uses the seaborn plotting library (abbreviated sns). We will cover this in the Visualization lecture, but now you don’t need to worry about how it works!\nYikes! Plotting the data uncovered a problem. The sharp vertical lines suggest that we have some missing values. What happened here?\n\nco2.head()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n3\n1958\n6\n1958.46\n-99.99\n317.10\n314.85\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n\n\n\n\n\n\nco2.tail()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n733\n2019\n4\n2019.29\n413.32\n413.32\n410.49\n26\n\n\n734\n2019\n5\n2019.38\n414.66\n414.66\n411.20\n28\n\n\n735\n2019\n6\n2019.46\n413.92\n413.92\n411.58\n27\n\n\n736\n2019\n7\n2019.54\n411.77\n411.77\n411.43\n23\n\n\n737\n2019\n8\n2019.62\n409.95\n409.95\n411.84\n29\n\n\n\n\n\n\n\nSome data have unusual values like -1 and -99.99.\nLet’s check the description at the top of the file again.\n\n-1 signifies a missing value for the number of days Days the equipment was in operation that month.\n-99.99 denotes a missing monthly average Avg\n\nHow can we fix this? First, let’s explore other aspects of our data. Understanding our data will help us decide what to do with the missing values.\n\n\n\n5.5.4 Sanity Checks: Reasoning about the data\nFirst, we consider the shape of the data. How many rows should we have?\n\nIf chronological order, we should have one record per month.\nData from March 1958 to August 2019.\nWe should have $ 12 (2019-1957) - 2 - 4 = 738 $ records.\n\n\nco2.shape\n\n(738, 7)\n\n\nNice!! The number of rows (i.e. records) match our expectations.\nLet’s now check the quality of each feature.\n\n\n5.5.5 Understanding Missing Value 1: Days\nDays is a time field, so let’s analyze other time fields to see if there is an explanation for missing values of days of operation.\nLet’s start with months, Mo.\nAre we missing any records? The number of months should have 62 or 61 instances (March 1957-August 2019).\n\nco2[\"Mo\"].value_counts().sort_index()\n\nMo\n1     61\n2     61\n3     62\n4     62\n5     62\n6     62\n7     62\n8     62\n9     61\n10    61\n11    61\n12    61\nName: count, dtype: int64\n\n\nAs expected Jan, Feb, Sep, Oct, Nov, and Dec have 61 occurrences and the rest 62.\n\nNext let’s explore days Days itself, which is the number of days that the measurement equipment worked.\n\n\nCode\nsns.displot(co2['Days']);\nplt.title(\"Distribution of days feature\"); # suppresses unneeded plotting output\n\n\n\n\n\n\n\n\n\nIn terms of data quality, a handful of months have averages based on measurements taken on fewer than half the days. In addition, there are nearly 200 missing values–that’s about 27% of the data!\n\nFinally, let’s check the last time feature, year Yr.\nLet’s check to see if there is any connection between missing-ness and the year of the recording.\n\n\nCode\nsns.scatterplot(x=\"Yr\", y=\"Days\", data=co2);\nplt.title(\"Day field by Year\"); # the ; suppresses output\n\n\n\n\n\n\n\n\n\nObservations:\n\nAll of the missing data are in the early years of operation.\nIt appears there may have been problems with equipment in the mid to late 80s.\n\nPotential Next Steps:\n\nConfirm these explanations through documentation about the historical readings.\nMaybe drop the earliest recordings? However, we would want to delay such action until after we have examined the time trends and assess whether there are any potential problems.\n\n\n\n\n5.5.6 Understanding Missing Value 2: Avg\nNext, let’s return to the -99.99 values in Avg to analyze the overall quality of the CO2 measurements. We’ll plot a histogram of the average CO2 measurements\n\n\nCode\n# Histograms of average CO2 measurements\nsns.displot(co2['Avg']);\n\n\n\n\n\n\n\n\n\nThe non-missing values are in the 300-400 range (a regular range of CO2 levels).\nWe also see that there are only a few missing Avg values (&lt;1% of values). Let’s examine all of them:\n\nco2[co2[\"Avg\"] &lt; 0]\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n3\n1958\n6\n1958.46\n-99.99\n317.10\n314.85\n-1\n\n\n7\n1958\n10\n1958.79\n-99.99\n312.66\n315.61\n-1\n\n\n71\n1964\n2\n1964.12\n-99.99\n320.07\n319.61\n-1\n\n\n72\n1964\n3\n1964.21\n-99.99\n320.73\n319.55\n-1\n\n\n73\n1964\n4\n1964.29\n-99.99\n321.77\n319.48\n-1\n\n\n213\n1975\n12\n1975.96\n-99.99\n330.59\n331.60\n0\n\n\n313\n1984\n4\n1984.29\n-99.99\n346.84\n344.27\n2\n\n\n\n\n\n\n\nThere doesn’t seem to be a pattern to these values, other than that most records also were missing Days data.\n\n\n5.5.7 Drop, NaN, or Impute Missing Avg Data?\nHow should we address the invalid Avg data?\n\nDrop records\nSet to NaN\nImpute using some strategy\n\nRemember we want to fix the following plot:\n\n\nCode\nsns.lineplot(x='DecDate', y='Avg', data=co2)\nplt.title(\"CO2 Average By Month\");\n\n\n\n\n\n\n\n\n\nSince we are plotting Avg vs DecDate, we should just focus on dealing with missing values for Avg.\nLet’s consider a few options: 1. Drop those records 2. Replace -99.99 with NaN 3. Substitute it with a likely value for the average CO2?\nWhat do you think are the pros and cons of each possible action?\nLet’s examine each of these three options.\n\n# 1. Drop missing values\nco2_drop = co2[co2['Avg'] &gt; 0]\nco2_drop.head()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n5\n1958\n8\n1958.62\n314.93\n314.93\n315.94\n-1\n\n\n\n\n\n\n\n\n# 2. Replace NaN with -99.99\nco2_NA = co2.replace(-99.99, np.nan)\nco2_NA.head()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n3\n1958\n6\n1958.46\nNaN\n317.10\n314.85\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n\n\n\n\n\nWe’ll also use a third version of the data.\nFirst, we note that the dataset already comes with a substitute value for the -99.99.\nFrom the file description:\n\nThe interpolated column includes average values from the preceding column (average) and interpolated values where data are missing. Interpolated values are computed in two steps…\n\nThe Int feature has values that exactly match those in Avg, except when Avg is -99.99, and then a reasonable estimate is used instead.\nSo, the third version of our data will use the Int feature instead of Avg.\n\n# 3. Use interpolated column which estimates missing Avg values\nco2_impute = co2.copy()\nco2_impute['Avg'] = co2['Int']\nco2_impute.head()\n\n\n\n\n\n\n\n\nYr\nMo\nDecDate\nAvg\nInt\nTrend\nDays\n\n\n\n\n0\n1958\n3\n1958.21\n315.71\n315.71\n314.62\n-1\n\n\n1\n1958\n4\n1958.29\n317.45\n317.45\n315.29\n-1\n\n\n2\n1958\n5\n1958.38\n317.50\n317.50\n314.71\n-1\n\n\n3\n1958\n6\n1958.46\n317.10\n317.10\n314.85\n-1\n\n\n4\n1958\n7\n1958.54\n315.86\n315.86\n314.98\n-1\n\n\n\n\n\n\n\nWhat’s a reasonable estimate?\nTo answer this question, let’s zoom in on a short time period, say the measurements in 1958 (where we know we have two missing values).\n\n\nCode\n# results of plotting data in 1958\n\ndef line_and_points(data, ax, title):\n    # assumes single year, hence Mo\n    ax.plot('Mo', 'Avg', data=data)\n    ax.scatter('Mo', 'Avg', data=data)\n    ax.set_xlim(2, 13)\n    ax.set_title(title)\n    ax.set_xticks(np.arange(3, 13))\n\ndef data_year(data, year):\n    return data[data[\"Yr\"] == 1958]\n    \n# uses matplotlib subplots\n# you may see more next week; focus on output for now\nfig, axes = plt.subplots(ncols = 3, figsize=(12, 4), sharey=True)\n\nyear = 1958\nline_and_points(data_year(co2_drop, year), axes[0], title=\"1. Drop Missing\")\nline_and_points(data_year(co2_NA, year), axes[1], title=\"2. Missing Set to NaN\")\nline_and_points(data_year(co2_impute, year), axes[2], title=\"3. Missing Interpolated\")\n\nfig.suptitle(f\"Monthly Averages for {year}\")\nplt.tight_layout()\n\n\n\n\n\n\n\n\n\nIn the big picture since there are only 7 Avg values missing (&lt;1% of 738 months), any of these approaches would work.\nHowever there is some appeal to option C, Imputing:\n\nShows seasonal trends for CO2\nWe are plotting all months in our data as a line plot\n\nLet’s replot our original figure with option 3:\n\n\nCode\nsns.lineplot(x='DecDate', y='Avg', data=co2_impute)\nplt.title(\"CO2 Average By Month, Imputed\");\n\n\n\n\n\n\n\n\n\nLooks pretty close to what we see on the NOAA website!\n\n\n5.5.8 Presenting the Data: A Discussion on Data Granularity\nFrom the description:\n\nMonthly measurements are averages of average day measurements.\nThe NOAA GML website has datasets for daily/hourly measurements too.\n\nThe data you present depends on your research question.\nHow do CO2 levels vary by season?\n\nYou might want to keep average monthly data.\n\nAre CO2 levels rising over the past 50+ years, consistent with global warming predictions?\n\nYou might be happier with a coarser granularity of average year data!\n\n\n\nCode\nco2_year = co2_impute.groupby('Yr').mean()\nsns.lineplot(x='Yr', y='Avg', data=co2_year)\nplt.title(\"CO2 Average By Year\");\n\n\n\n\n\n\n\n\n\nIndeed, we see a rise by nearly 100 ppm of CO2 since Mauna Loa began recording in 1958.",
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+    "text": "5.6 Summary\nWe went over a lot of content this lecture; let’s summarize the most important points:\n\n5.6.1 Dealing with Missing Values\nThere are a few options we can take to deal with missing data:\n\nDrop missing records\nKeep NaN missing values\nImpute using an interpolated column\n\n\n\n5.6.2 EDA and Data Wrangling\nThere are several ways to approach EDA and Data Wrangling:\n\nExamine the data and metadata: what is the date, size, organization, and structure of the data?\nExamine each field/attribute/dimension individually.\nExamine pairs of related dimensions (e.g. breaking down grades by major).\nAlong the way, we can:\n\nVisualize or summarize the data.\nValidate assumptions about data and its collection process. Pay particular attention to when the data was collected.\nIdentify and address anomalies.\nApply data transformations and corrections (we’ll cover this in the upcoming lecture).\nRecord everything you do! Developing in Jupyter Notebook promotes reproducibility of your own work!",
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+    "text": "4.1 Custom Sorts\nFirst, let’s finish our discussion about sorting. Let’s try to solve a sorting problem using different approaches. Assume we want to find the longest baby names and sort our data accordingly.\nWe’ll start by loading the babynames dataset. Note that this dataset is filtered to only contain data from California.\nCode\n# This code pulls census data and loads it into a DataFrame\n# We won't cover it explicitly in this class, but you are welcome to explore it on your own\nimport pandas as pd\nimport numpy as np\nimport urllib.request\nimport os.path\nimport zipfile\n\ndata_url = \"https://www.ssa.gov/oact/babynames/state/namesbystate.zip\"\nlocal_filename = \"data/babynamesbystate.zip\"\nif not os.path.exists(local_filename): # If the data exists don't download again\n    with urllib.request.urlopen(data_url) as resp, open(local_filename, 'wb') as f:\n        f.write(resp.read())\n\nzf = zipfile.ZipFile(local_filename, 'r')\n\nca_name = 'STATE.CA.TXT'\nfield_names = ['State', 'Sex', 'Year', 'Name', 'Count']\nwith zf.open(ca_name) as fh:\n    babynames = pd.read_csv(fh, header=None, names=field_names)\n\nbabynames.tail(10)\n\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n407418\nCA\nM\n2022\nZach\n5\n\n\n407419\nCA\nM\n2022\nZadkiel\n5\n\n\n407420\nCA\nM\n2022\nZae\n5\n\n\n407421\nCA\nM\n2022\nZai\n5\n\n\n407422\nCA\nM\n2022\nZay\n5\n\n\n407423\nCA\nM\n2022\nZayvier\n5\n\n\n407424\nCA\nM\n2022\nZia\n5\n\n\n407425\nCA\nM\n2022\nZora\n5\n\n\n407426\nCA\nM\n2022\nZuriel\n5\n\n\n407427\nCA\nM\n2022\nZylo\n5",
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+    "text": "4.1.1 Approach 1: Create a Temporary Column\nOne method to do this is to first start by creating a column that contains the lengths of the names.\n\n# Create a Series of the length of each name\nbabyname_lengths = babynames[\"Name\"].str.len()\n\n# Add a column named \"name_lengths\" that includes the length of each name\nbabynames[\"name_lengths\"] = babyname_lengths\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nname_lengths\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n4\n\n\n1\nCA\nF\n1910\nHelen\n239\n5\n\n\n2\nCA\nF\n1910\nDorothy\n220\n7\n\n\n3\nCA\nF\n1910\nMargaret\n163\n8\n\n\n4\nCA\nF\n1910\nFrances\n134\n7\n\n\n\n\n\n\n\nWe can then sort the DataFrame by that column using .sort_values():\n\n# Sort by the temporary column\nbabynames = babynames.sort_values(by=\"name_lengths\", ascending=False)\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nname_lengths\n\n\n\n\n334166\nCA\nM\n1996\nFranciscojavier\n8\n15\n\n\n337301\nCA\nM\n1997\nFranciscojavier\n5\n15\n\n\n339472\nCA\nM\n1998\nFranciscojavier\n6\n15\n\n\n321792\nCA\nM\n1991\nRyanchristopher\n7\n15\n\n\n327358\nCA\nM\n1993\nJohnchristopher\n5\n15\n\n\n\n\n\n\n\nFinally, we can drop the name_length column from babynames to prevent our table from getting cluttered.\n\n# Drop the 'name_length' column\nbabynames = babynames.drop(\"name_lengths\", axis='columns')\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n334166\nCA\nM\n1996\nFranciscojavier\n8\n\n\n337301\nCA\nM\n1997\nFranciscojavier\n5\n\n\n339472\nCA\nM\n1998\nFranciscojavier\n6\n\n\n321792\nCA\nM\n1991\nRyanchristopher\n7\n\n\n327358\nCA\nM\n1993\nJohnchristopher\n5\n\n\n\n\n\n\n\n\n\n4.1.2 Approach 2: Sorting using the key Argument\nAnother way to approach this is to use the key argument of .sort_values(). Here we can specify that we want to sort \"Name\" values by their length.\n\nbabynames.sort_values(\"Name\", key=lambda x: x.str.len(), ascending=False).head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n334166\nCA\nM\n1996\nFranciscojavier\n8\n\n\n327472\nCA\nM\n1993\nRyanchristopher\n5\n\n\n337301\nCA\nM\n1997\nFranciscojavier\n5\n\n\n337477\nCA\nM\n1997\nRyanchristopher\n5\n\n\n312543\nCA\nM\n1987\nFranciscojavier\n5\n\n\n\n\n\n\n\n\n\n4.1.3 Approach 3: Sorting using the map Function\nWe can also use the map function on a Series to solve this. Say we want to sort the babynames table by the number of \"dr\"’s and \"ea\"’s in each \"Name\". We’ll define the function dr_ea_count to help us out.\n\n# First, define a function to count the number of times \"dr\" or \"ea\" appear in each name\ndef dr_ea_count(string):\n    return string.count('dr') + string.count('ea')\n\n# Then, use `map` to apply `dr_ea_count` to each name in the \"Name\" column\nbabynames[\"dr_ea_count\"] = babynames[\"Name\"].map(dr_ea_count)\n\n# Sort the DataFrame by the new \"dr_ea_count\" column so we can see our handiwork\nbabynames = babynames.sort_values(by=\"dr_ea_count\", ascending=False)\nbabynames.head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\ndr_ea_count\n\n\n\n\n115957\nCA\nF\n1990\nDeandrea\n5\n3\n\n\n101976\nCA\nF\n1986\nDeandrea\n6\n3\n\n\n131029\nCA\nF\n1994\nLeandrea\n5\n3\n\n\n108731\nCA\nF\n1988\nDeandrea\n5\n3\n\n\n308131\nCA\nM\n1985\nDeandrea\n6\n3\n\n\n\n\n\n\n\nWe can drop the dr_ea_count once we’re done using it to maintain a neat table.\n\n# Drop the `dr_ea_count` column\nbabynames = babynames.drop(\"dr_ea_count\", axis = 'columns')\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n115957\nCA\nF\n1990\nDeandrea\n5\n\n\n101976\nCA\nF\n1986\nDeandrea\n6\n\n\n131029\nCA\nF\n1994\nLeandrea\n5\n\n\n108731\nCA\nF\n1988\nDeandrea\n5\n\n\n308131\nCA\nM\n1985\nDeandrea\n6",
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+    "text": "4.2 Aggregating Data with .groupby\nUp until this point, we have been working with individual rows of DataFrames. As data scientists, we often wish to investigate trends across a larger subset of our data. For example, we may want to compute some summary statistic (the mean, median, sum, etc.) for a group of rows in our DataFrame. To do this, we’ll use pandas GroupBy objects. Our goal is to group together rows that fall under the same category and perform an operation that aggregates across all rows in the category.\nLet’s say we wanted to aggregate all rows in babynames for a given year.\n\nbabynames.groupby(\"Year\")\n\n&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x28805ee90&gt;\n\n\nWhat does this strange output mean? Calling .groupby (documentation) has generated a GroupBy object. You can imagine this as a set of “mini” sub-DataFrames, where each subframe contains all of the rows from babynames that correspond to a particular year.\nThe diagram below shows a simplified view of babynames to help illustrate this idea.\n\n\n\nWe can’t work with a GroupBy object directly – that is why you saw that strange output earlier rather than a standard view of a DataFrame. To actually manipulate values within these “mini” DataFrames, we’ll need to call an aggregation method. This is a method that tells pandas how to aggregate the values within the GroupBy object. Once the aggregation is applied, pandas will return a normal (now grouped) DataFrame.\nThe first aggregation method we’ll consider is .agg. The .agg method takes in a function as its argument; this function is then applied to each column of a “mini” grouped DataFrame. We end up with a new DataFrame with one aggregated row per subframe. Let’s see this in action by finding the sum of all counts for each year in babynames – this is equivalent to finding the number of babies born in each year.\n\nbabynames[[\"Year\", \"Count\"]].groupby(\"Year\").agg(\"sum\").head(5)\n\n\n\n\n\n\n\n\nCount\n\n\nYear\n\n\n\n\n\n1910\n9163\n\n\n1911\n9983\n\n\n1912\n17946\n\n\n1913\n22094\n\n\n1914\n26926\n\n\n\n\n\n\n\nWe can relate this back to the diagram we used above. Remember that the diagram uses a simplified version of babynames, which is why we see smaller values for the summed counts.\n\n\n\nPerforming an aggregation\n\n\nCalling .agg has condensed each subframe back into a single row. This gives us our final output: a DataFrame that is now indexed by \"Year\", with a single row for each unique year in the original babynames DataFrame.\nThere are many different aggregation functions we can use, all of which are useful in different applications.\n\nbabynames[[\"Year\", \"Count\"]].groupby(\"Year\").agg(\"min\").head(5)\n\n\n\n\n\n\n\n\nCount\n\n\nYear\n\n\n\n\n\n1910\n5\n\n\n1911\n5\n\n\n1912\n5\n\n\n1913\n5\n\n\n1914\n5\n\n\n\n\n\n\n\n\nbabynames[[\"Year\", \"Count\"]].groupby(\"Year\").agg(\"max\").head(5)\n\n\n\n\n\n\n\n\nCount\n\n\nYear\n\n\n\n\n\n1910\n295\n\n\n1911\n390\n\n\n1912\n534\n\n\n1913\n614\n\n\n1914\n773\n\n\n\n\n\n\n\n\n# Same result, but now we explicitly tell pandas to only consider the \"Count\" column when summing\nbabynames.groupby(\"Year\")[[\"Count\"]].agg(\"sum\").head(5)\n\n\n\n\n\n\n\n\nCount\n\n\nYear\n\n\n\n\n\n1910\n9163\n\n\n1911\n9983\n\n\n1912\n17946\n\n\n1913\n22094\n\n\n1914\n26926\n\n\n\n\n\n\n\nThere are many different aggregations that can be applied to the grouped data. The primary requirement is that an aggregation function must:\n\nTake in a Series of data (a single column of the grouped subframe).\nReturn a single value that aggregates this Series.\n\n\n4.2.1 Aggregation Functions\nBecause of this fairly broad requirement, pandas offers many ways of computing an aggregation.\nIn-built Python operations – such as sum, max, and min – are automatically recognized by pandas.\n\n# What is the minimum count for each name in any year?\nbabynames.groupby(\"Name\")[[\"Count\"]].agg(\"min\").head()\n\n\n\n\n\n\n\n\nCount\n\n\nName\n\n\n\n\n\nAadan\n5\n\n\nAadarsh\n6\n\n\nAaden\n10\n\n\nAadhav\n6\n\n\nAadhini\n6\n\n\n\n\n\n\n\n\n# What is the largest single-year count of each name?\nbabynames.groupby(\"Name\")[[\"Count\"]].agg(\"max\").head()\n\n\n\n\n\n\n\n\nCount\n\n\nName\n\n\n\n\n\nAadan\n7\n\n\nAadarsh\n6\n\n\nAaden\n158\n\n\nAadhav\n8\n\n\nAadhini\n6\n\n\n\n\n\n\n\nAs mentioned previously, functions from the NumPy library, such as np.mean, np.max, np.min, and np.sum, are also fair game in pandas.\n\n# What is the average count for each name across all years?\nbabynames.groupby(\"Name\")[[\"Count\"]].agg(\"mean\").head()\n\n\n\n\n\n\n\n\nCount\n\n\nName\n\n\n\n\n\nAadan\n6.000000\n\n\nAadarsh\n6.000000\n\n\nAaden\n46.214286\n\n\nAadhav\n6.750000\n\n\nAadhini\n6.000000\n\n\n\n\n\n\n\npandas also offers a number of in-built functions. Functions that are native to pandas can be referenced using their string name within a call to .agg. Some examples include:\n\n.agg(\"sum\")\n.agg(\"max\")\n.agg(\"min\")\n.agg(\"mean\")\n.agg(\"first\")\n.agg(\"last\")\n\nThe latter two entries in this list – \"first\" and \"last\" – are unique to pandas. They return the first or last entry in a subframe column. Why might this be useful? Consider a case where multiple columns in a group share identical information. To represent this information in the grouped output, we can simply grab the first or last entry, which we know will be identical to all other entries.\nLet’s illustrate this with an example. Say we add a new column to babynames that contains the first letter of each name.\n\n# Imagine we had an additional column, \"First Letter\". We'll explain this code next week\nbabynames[\"First Letter\"] = babynames[\"Name\"].str[0]\n\n# We construct a simplified DataFrame containing just a subset of columns\nbabynames_new = babynames[[\"Name\", \"First Letter\", \"Year\"]]\nbabynames_new.head()\n\n\n\n\n\n\n\n\nName\nFirst Letter\nYear\n\n\n\n\n115957\nDeandrea\nD\n1990\n\n\n101976\nDeandrea\nD\n1986\n\n\n131029\nLeandrea\nL\n1994\n\n\n108731\nDeandrea\nD\n1988\n\n\n308131\nDeandrea\nD\n1985\n\n\n\n\n\n\n\nIf we form groups for each name in the dataset, \"First Letter\" will be the same for all members of the group. This means that if we simply select the first entry for \"First Letter\" in the group, we’ll represent all data in that group.\nWe can use a dictionary to apply different aggregation functions to each column during grouping.\n\n\n\nAggregating using “first”\n\n\n\nbabynames_new.groupby(\"Name\").agg({\"First Letter\":\"first\", \"Year\":\"max\"}).head()\n\n\n\n\n\n\n\n\nFirst Letter\nYear\n\n\nName\n\n\n\n\n\n\nAadan\nA\n2014\n\n\nAadarsh\nA\n2019\n\n\nAaden\nA\n2020\n\n\nAadhav\nA\n2019\n\n\nAadhini\nA\n2022\n\n\n\n\n\n\n\n\n\n4.2.2 Plotting Birth Counts\nLet’s use .agg to find the total number of babies born in each year. Recall that using .agg with .groupby() follows the format: df.groupby(column_name).agg(aggregation_function). The line of code below gives us the total number of babies born in each year.\n\n\nCode\nbabynames.groupby(\"Year\")[[\"Count\"]].agg(sum).head(5)\n# Alternative 1\n# babynames.groupby(\"Year\")[[\"Count\"]].sum()\n# Alternative 2\n# babynames.groupby(\"Year\").sum(numeric_only=True)\n\n\n/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/390646742.py:1: FutureWarning:\n\nThe provided callable &lt;built-in function sum&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n\n\n\n\n\n\n\n\n\n\nCount\n\n\nYear\n\n\n\n\n\n1910\n9163\n\n\n1911\n9983\n\n\n1912\n17946\n\n\n1913\n22094\n\n\n1914\n26926\n\n\n\n\n\n\n\nHere’s an illustration of the process:\n\nPlotting the Dataframe we obtain tells an interesting story.\n\n\nCode\nimport plotly.express as px\npuzzle2 = babynames.groupby(\"Year\")[[\"Count\"]].agg(\"sum\")\npx.line(puzzle2, y = \"Count\")\n\n\n                                                \n\n\nA word of warning: we made an enormous assumption when we decided to use this dataset to estimate birth rate. According to this article from the Legistlative Analyst Office, the true number of babies born in California in 2020 was 421,275. However, our plot shows 362,882 babies —— what happened?\n\n\n4.2.3 Summary of the .groupby() Function\nA groupby operation involves some combination of splitting a DataFrame into grouped subframes, applying a function, and combining the results.\nFor some arbitrary DataFrame df below, the code df.groupby(\"year\").agg(sum) does the following:\n\nSplits the DataFrame into sub-DataFrames with rows belonging to the same year.\nApplies the sum function to each column of each sub-DataFrame.\nCombines the results of sum into a single DataFrame, indexed by year.\n\n\n\n\n4.2.4 Revisiting the .agg() Function\n.agg() can take in any function that aggregates several values into one summary value. Some commonly-used aggregation functions can even be called directly, without explicit use of .agg(). For example, we can call .mean() on .groupby():\nbabynames.groupby(\"Year\").mean().head()\nWe can now put this all into practice. Say we want to find the baby name with sex “F” that has fallen in popularity the most in California. To calculate this, we can first create a metric: “Ratio to Peak” (RTP). The RTP is the ratio of babies born with a given name in 2022 to the maximum number of babies born with the name in any year.\nLet’s start with calculating this for one baby, “Jennifer”.\n\n# We filter by babies with sex \"F\" and sort by \"Year\"\nf_babynames = babynames[babynames[\"Sex\"] == \"F\"]\nf_babynames = f_babynames.sort_values([\"Year\"])\n\n# Determine how many Jennifers were born in CA per year\njenn_counts_series = f_babynames[f_babynames[\"Name\"] == \"Jennifer\"][\"Count\"]\n\n# Determine the max number of Jennifers born in a year and the number born in 2022 \n# to calculate RTP\nmax_jenn = max(f_babynames[f_babynames[\"Name\"] == \"Jennifer\"][\"Count\"])\ncurr_jenn = f_babynames[f_babynames[\"Name\"] == \"Jennifer\"][\"Count\"].iloc[-1]\nrtp = curr_jenn / max_jenn\nrtp\n\n0.018796372629843364\n\n\nBy creating a function to calculate RTP and applying it to our DataFrame by using .groupby(), we can easily compute the RTP for all names at once!\n\ndef ratio_to_peak(series):\n    return series.iloc[-1] / max(series)\n\n#Using .groupby() to apply the function\nrtp_table = f_babynames.groupby(\"Name\")[[\"Year\", \"Count\"]].agg(ratio_to_peak)\nrtp_table.head()\n\n\n\n\n\n\n\n\nYear\nCount\n\n\nName\n\n\n\n\n\n\nAadhini\n1.0\n1.000000\n\n\nAadhira\n1.0\n0.500000\n\n\nAadhya\n1.0\n0.660000\n\n\nAadya\n1.0\n0.586207\n\n\nAahana\n1.0\n0.269231\n\n\n\n\n\n\n\nIn the rows shown above, we can see that every row shown has a Year value of 1.0.\nThis is the “pandas-ification” of logic you saw in Data 8. Much of the logic you’ve learned in Data 8 will serve you well in Data 100.\n\n\n4.2.5 Nuisance Columns\nNote that you must be careful with which columns you apply the .agg() function to. If we were to apply our function to the table as a whole by doing f_babynames.groupby(\"Name\").agg(ratio_to_peak), executing our .agg() call would result in a TypeError.\n\nWe can avoid this issue (and prevent unintentional loss of data) by explicitly selecting column(s) we want to apply our aggregation function to BEFORE calling .agg(),\n\n\n4.2.6 Renaming Columns After Grouping\nBy default, .groupby will not rename any aggregated columns. As we can see in the table above, the aggregated column is still named Count even though it now represents the RTP. For better readability, we can rename Count to Count RTP\n\nrtp_table = rtp_table.rename(columns = {\"Count\": \"Count RTP\"})\nrtp_table\n\n\n\n\n\n\n\n\nYear\nCount RTP\n\n\nName\n\n\n\n\n\n\nAadhini\n1.0\n1.000000\n\n\nAadhira\n1.0\n0.500000\n\n\nAadhya\n1.0\n0.660000\n\n\nAadya\n1.0\n0.586207\n\n\nAahana\n1.0\n0.269231\n\n\n...\n...\n...\n\n\nZyanya\n1.0\n0.466667\n\n\nZyla\n1.0\n1.000000\n\n\nZylah\n1.0\n1.000000\n\n\nZyra\n1.0\n1.000000\n\n\nZyrah\n1.0\n0.833333\n\n\n\n\n13782 rows × 2 columns\n\n\n\n\n\n4.2.7 Some Data Science Payoff\nBy sorting rtp_table, we can see the names whose popularity has decreased the most.\n\nrtp_table = rtp_table.rename(columns = {\"Count\": \"Count RTP\"})\nrtp_table.sort_values(\"Count RTP\").head()\n\n\n\n\n\n\n\n\nYear\nCount RTP\n\n\nName\n\n\n\n\n\n\nDebra\n1.0\n0.001260\n\n\nDebbie\n1.0\n0.002815\n\n\nCarol\n1.0\n0.003180\n\n\nTammy\n1.0\n0.003249\n\n\nSusan\n1.0\n0.003305\n\n\n\n\n\n\n\nTo visualize the above DataFrame, let’s look at the line plot below:\n\n\nCode\nimport plotly.express as px\npx.line(f_babynames[f_babynames[\"Name\"] == \"Debra\"], x = \"Year\", y = \"Count\")\n\n\n                                                \n\n\nWe can get the list of the top 10 names and then plot popularity with the following code:\n\ntop10 = rtp_table.sort_values(\"Count RTP\").head(10).index\npx.line(\n    f_babynames[f_babynames[\"Name\"].isin(top10)], \n    x = \"Year\", \n    y = \"Count\", \n    color = \"Name\"\n)\n\n                                                \n\n\nAs a quick exercise, consider what code would compute the total number of babies with each name.\n\n\nCode\nbabynames.groupby(\"Name\")[[\"Count\"]].agg(\"sum\").head()\n# alternative solution: \n# babynames.groupby(\"Name\")[[\"Count\"]].sum()\n\n\n\n\n\n\n\n\n\nCount\n\n\nName\n\n\n\n\n\nAadan\n18\n\n\nAadarsh\n6\n\n\nAaden\n647\n\n\nAadhav\n27\n\n\nAadhini\n6",
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+    "text": "4.3 .groupby(), Continued\nWe’ll work with the elections DataFrame again.\n\n\nCode\nimport pandas as pd\nimport numpy as np\n\nelections = pd.read_csv(\"data/elections.csv\")\nelections.head(5)\n\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n\n\n\n\n\n\n4.3.1 Raw GroupBy Objects\nThe result of groupby applied to a DataFrame is a DataFrameGroupBy object, not a DataFrame.\n\ngrouped_by_year = elections.groupby(\"Year\")\ntype(grouped_by_year)\n\npandas.core.groupby.generic.DataFrameGroupBy\n\n\nThere are several ways to look into DataFrameGroupBy objects:\n\ngrouped_by_party = elections.groupby(\"Party\")\ngrouped_by_party.groups\n\n{'American': [22, 126], 'American Independent': [115, 119, 124], 'Anti-Masonic': [6], 'Anti-Monopoly': [38], 'Citizens': [127], 'Communist': [89], 'Constitution': [160, 164, 172], 'Constitutional Union': [24], 'Democratic': [2, 4, 8, 10, 13, 14, 17, 20, 28, 29, 34, 37, 39, 45, 47, 52, 55, 57, 64, 70, 74, 77, 81, 83, 86, 91, 94, 97, 100, 105, 108, 111, 114, 116, 118, 123, 129, 134, 137, 140, 144, 151, 158, 162, 168, 176, 178], 'Democratic-Republican': [0, 1], 'Dixiecrat': [103], 'Farmer–Labor': [78], 'Free Soil': [15, 18], 'Green': [149, 155, 156, 165, 170, 177, 181], 'Greenback': [35], 'Independent': [121, 130, 143, 161, 167, 174], 'Liberal Republican': [31], 'Libertarian': [125, 128, 132, 138, 139, 146, 153, 159, 163, 169, 175, 180], 'National Democratic': [50], 'National Republican': [3, 5], 'National Union': [27], 'Natural Law': [148], 'New Alliance': [136], 'Northern Democratic': [26], 'Populist': [48, 61, 141], 'Progressive': [68, 82, 101, 107], 'Prohibition': [41, 44, 49, 51, 54, 59, 63, 67, 73, 75, 99], 'Reform': [150, 154], 'Republican': [21, 23, 30, 32, 33, 36, 40, 43, 46, 53, 56, 60, 65, 69, 72, 79, 80, 84, 87, 90, 96, 98, 104, 106, 109, 112, 113, 117, 120, 122, 131, 133, 135, 142, 145, 152, 157, 166, 171, 173, 179], 'Socialist': [58, 62, 66, 71, 76, 85, 88, 92, 95, 102], 'Southern Democratic': [25], 'States' Rights': [110], 'Taxpayers': [147], 'Union': [93], 'Union Labor': [42], 'Whig': [7, 9, 11, 12, 16, 19]}\n\n\n\ngrouped_by_party.get_group(\"Socialist\")\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n58\n1904\nEugene V. Debs\nSocialist\n402810\nloss\n2.985897\n\n\n62\n1908\nEugene V. Debs\nSocialist\n420852\nloss\n2.850866\n\n\n66\n1912\nEugene V. Debs\nSocialist\n901551\nloss\n6.004354\n\n\n71\n1916\nAllan L. Benson\nSocialist\n590524\nloss\n3.194193\n\n\n76\n1920\nEugene V. Debs\nSocialist\n913693\nloss\n3.428282\n\n\n85\n1928\nNorman Thomas\nSocialist\n267478\nloss\n0.728623\n\n\n88\n1932\nNorman Thomas\nSocialist\n884885\nloss\n2.236211\n\n\n92\n1936\nNorman Thomas\nSocialist\n187910\nloss\n0.412876\n\n\n95\n1940\nNorman Thomas\nSocialist\n116599\nloss\n0.234237\n\n\n102\n1948\nNorman Thomas\nSocialist\n139569\nloss\n0.286312\n\n\n\n\n\n\n\n\n\n4.3.2 Other GroupBy Methods\nThere are many aggregation methods we can use with .agg. Some useful options are:\n\n.mean: creates a new DataFrame with the mean value of each group\n.sum: creates a new DataFrame with the sum of each group\n.max and .min: creates a new DataFrame with the maximum/minimum value of each group\n.first and .last: creates a new DataFrame with the first/last row in each group\n.size: creates a new Series with the number of entries in each group\n.count: creates a new DataFrame with the number of entries, excluding missing values.\n\nLet’s illustrate some examples by creating a DataFrame called df.\n\ndf = pd.DataFrame({'letter':['A','A','B','C','C','C'], \n                   'num':[1,2,3,4,np.nan,4], \n                   'state':[np.nan, 'tx', 'fl', 'hi', np.nan, 'ak']})\ndf\n\n\n\n\n\n\n\n\nletter\nnum\nstate\n\n\n\n\n0\nA\n1.0\nNaN\n\n\n1\nA\n2.0\ntx\n\n\n2\nB\n3.0\nfl\n\n\n3\nC\n4.0\nhi\n\n\n4\nC\nNaN\nNaN\n\n\n5\nC\n4.0\nak\n\n\n\n\n\n\n\nNote the slight difference between .size() and .count(): while .size() returns a Series and counts the number of entries including the missing values, .count() returns a DataFrame and counts the number of entries in each column excluding missing values.\n\ndf.groupby(\"letter\").size()\n\nletter\nA    2\nB    1\nC    3\ndtype: int64\n\n\n\ndf.groupby(\"letter\").count()\n\n\n\n\n\n\n\n\nnum\nstate\n\n\nletter\n\n\n\n\n\n\nA\n2\n1\n\n\nB\n1\n1\n\n\nC\n2\n2\n\n\n\n\n\n\n\nYou might recall that the value_counts() function in the previous note does something similar. It turns out value_counts() and groupby.size() are the same, except value_counts() sorts the resulting Series in descending order automatically.\n\ndf[\"letter\"].value_counts()\n\nletter\nC    3\nA    2\nB    1\nName: count, dtype: int64\n\n\nThese (and other) aggregation functions are so common that pandas allows for writing shorthand. Instead of explicitly stating the use of .agg, we can call the function directly on the GroupBy object.\nFor example, the following are equivalent:\n\nelections.groupby(\"Candidate\").agg(mean)\nelections.groupby(\"Candidate\").mean()\n\nThere are many other methods that pandas supports. You can check them out on the pandas documentation.\n\n\n4.3.3 Filtering by Group\nAnother common use for GroupBy objects is to filter data by group.\ngroupby.filter takes an argument func, where func is a function that:\n\nTakes a DataFrame object as input\nReturns a single True or False.\n\ngroupby.filter applies func to each group/sub-DataFrame:\n\nIf func returns True for a group, then all rows belonging to the group are preserved.\nIf func returns False for a group, then all rows belonging to that group are filtered out.\n\nIn other words, sub-DataFrames that correspond to True are returned in the final result, whereas those with a False value are not. Importantly, groupby.filter is different from groupby.agg in that an entire sub-DataFrame is returned in the final DataFrame, not just a single row. As a result, groupby.filter preserves the original indices and the column we grouped on does NOT become the index!\n\nTo illustrate how this happens, let’s go back to the elections dataset. Say we want to identify “tight” election years – that is, we want to find all rows that correspond to election years where all candidates in that year won a similar portion of the total vote. Specifically, let’s find all rows corresponding to a year where no candidate won more than 45% of the total vote.\nIn other words, we want to:\n\nFind the years where the maximum % in that year is less than 45%\nReturn all DataFrame rows that correspond to these years\n\nFor each year, we need to find the maximum % among all rows for that year. If this maximum % is lower than 45%, we will tell pandas to keep all rows corresponding to that year.\n\nelections.groupby(\"Year\").filter(lambda sf: sf[\"%\"].max() &lt; 45).head(9)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n23\n1860\nAbraham Lincoln\nRepublican\n1855993\nwin\n39.699408\n\n\n24\n1860\nJohn Bell\nConstitutional Union\n590901\nloss\n12.639283\n\n\n25\n1860\nJohn C. Breckinridge\nSouthern Democratic\n848019\nloss\n18.138998\n\n\n26\n1860\nStephen A. Douglas\nNorthern Democratic\n1380202\nloss\n29.522311\n\n\n66\n1912\nEugene V. Debs\nSocialist\n901551\nloss\n6.004354\n\n\n67\n1912\nEugene W. Chafin\nProhibition\n208156\nloss\n1.386325\n\n\n68\n1912\nTheodore Roosevelt\nProgressive\n4122721\nloss\n27.457433\n\n\n69\n1912\nWilliam Taft\nRepublican\n3486242\nloss\n23.218466\n\n\n70\n1912\nWoodrow Wilson\nDemocratic\n6296284\nwin\n41.933422\n\n\n\n\n\n\n\nWhat’s going on here? In this example, we’ve defined our filtering function, func, to be lambda sf: sf[\"%\"].max() &lt; 45. This filtering function will find the maximum \"%\" value among all entries in the grouped sub-DataFrame, which we call sf. If the maximum value is less than 45, then the filter function will return True and all rows in that grouped sub-DataFrame will appear in the final output DataFrame.\nExamine the DataFrame above. Notice how, in this preview of the first 9 rows, all entries from the years 1860 and 1912 appear. This means that in 1860 and 1912, no candidate in that year won more than 45% of the total vote.\nYou may ask: how is the groupby.filter procedure different to the boolean filtering we’ve seen previously? Boolean filtering considers individual rows when applying a boolean condition. For example, the code elections[elections[\"%\"] &lt; 45] will check the \"%\" value of every single row in elections; if it is less than 45, then that row will be kept in the output. groupby.filter, in contrast, applies a boolean condition across all rows in a group. If not all rows in that group satisfy the condition specified by the filter, the entire group will be discarded in the output.\n\n\n4.3.4 Aggregation with lambda Functions\nWhat if we wish to aggregate our DataFrame using a non-standard function – for example, a function of our own design? We can do so by combining .agg with lambda expressions.\nLet’s first consider a puzzle to jog our memory. We will attempt to find the Candidate from each Party with the highest % of votes.\nA naive approach may be to group by the Party column and aggregate by the maximum.\n\nelections.groupby(\"Party\").agg(max).head(10)\n\n/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/4278286395.py:1: FutureWarning:\n\nThe provided callable &lt;built-in function max&gt; is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n\n\n\n\n\n\n\n\n\n\nYear\nCandidate\nPopular vote\nResult\n%\n\n\nParty\n\n\n\n\n\n\n\n\n\nAmerican\n1976\nThomas J. Anderson\n873053\nloss\n21.554001\n\n\nAmerican Independent\n1976\nLester Maddox\n9901118\nloss\n13.571218\n\n\nAnti-Masonic\n1832\nWilliam Wirt\n100715\nloss\n7.821583\n\n\nAnti-Monopoly\n1884\nBenjamin Butler\n134294\nloss\n1.335838\n\n\nCitizens\n1980\nBarry Commoner\n233052\nloss\n0.270182\n\n\nCommunist\n1932\nWilliam Z. Foster\n103307\nloss\n0.261069\n\n\nConstitution\n2016\nMichael Peroutka\n203091\nloss\n0.152398\n\n\nConstitutional Union\n1860\nJohn Bell\n590901\nloss\n12.639283\n\n\nDemocratic\n2020\nWoodrow Wilson\n81268924\nwin\n61.344703\n\n\nDemocratic-Republican\n1824\nJohn Quincy Adams\n151271\nwin\n57.210122\n\n\n\n\n\n\n\nThis approach is clearly wrong – the DataFrame claims that Woodrow Wilson won the presidency in 2020.\nWhy is this happening? Here, the max aggregation function is taken over every column independently. Among Democrats, max is computing:\n\nThe most recent Year a Democratic candidate ran for president (2020)\nThe Candidate with the alphabetically “largest” name (“Woodrow Wilson”)\nThe Result with the alphabetically “largest” outcome (“win”)\n\nInstead, let’s try a different approach. We will:\n\nSort the DataFrame so that rows are in descending order of %\nGroup by Party and select the first row of each sub-DataFrame\n\nWhile it may seem unintuitive, sorting elections by descending order of % is extremely helpful. If we then group by Party, the first row of each GroupBy object will contain information about the Candidate with the highest voter %.\n\nelections_sorted_by_percent = elections.sort_values(\"%\", ascending=False)\nelections_sorted_by_percent.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n114\n1964\nLyndon Johnson\nDemocratic\n43127041\nwin\n61.344703\n\n\n91\n1936\nFranklin Roosevelt\nDemocratic\n27752648\nwin\n60.978107\n\n\n120\n1972\nRichard Nixon\nRepublican\n47168710\nwin\n60.907806\n\n\n79\n1920\nWarren Harding\nRepublican\n16144093\nwin\n60.574501\n\n\n133\n1984\nRonald Reagan\nRepublican\n54455472\nwin\n59.023326\n\n\n\n\n\n\n\n\nelections_sorted_by_percent.groupby(\"Party\").agg(lambda x : x.iloc[0]).head(10)\n\n# Equivalent to the below code\n# elections_sorted_by_percent.groupby(\"Party\").agg('first').head(10)\n\n\n\n\n\n\n\n\nYear\nCandidate\nPopular vote\nResult\n%\n\n\nParty\n\n\n\n\n\n\n\n\n\nAmerican\n1856\nMillard Fillmore\n873053\nloss\n21.554001\n\n\nAmerican Independent\n1968\nGeorge Wallace\n9901118\nloss\n13.571218\n\n\nAnti-Masonic\n1832\nWilliam Wirt\n100715\nloss\n7.821583\n\n\nAnti-Monopoly\n1884\nBenjamin Butler\n134294\nloss\n1.335838\n\n\nCitizens\n1980\nBarry Commoner\n233052\nloss\n0.270182\n\n\nCommunist\n1932\nWilliam Z. Foster\n103307\nloss\n0.261069\n\n\nConstitution\n2008\nChuck Baldwin\n199750\nloss\n0.152398\n\n\nConstitutional Union\n1860\nJohn Bell\n590901\nloss\n12.639283\n\n\nDemocratic\n1964\nLyndon Johnson\n43127041\nwin\n61.344703\n\n\nDemocratic-Republican\n1824\nAndrew Jackson\n151271\nloss\n57.210122\n\n\n\n\n\n\n\nHere’s an illustration of the process:\n\nNotice how our code correctly determines that Lyndon Johnson from the Democratic Party has the highest voter %.\nMore generally, lambda functions are used to design custom aggregation functions that aren’t pre-defined by Python. The input parameter x to the lambda function is a GroupBy object. Therefore, it should make sense why lambda x : x.iloc[0] selects the first row in each groupby object.\nIn fact, there’s a few different ways to approach this problem. Each approach has different tradeoffs in terms of readability, performance, memory consumption, complexity, etc. We’ve given a few examples below.\nNote: Understanding these alternative solutions is not required. They are given to demonstrate the vast number of problem-solving approaches in pandas.\n\n# Using the idxmax function\nbest_per_party = elections.loc[elections.groupby('Party')['%'].idxmax()]\nbest_per_party.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n22\n1856\nMillard Fillmore\nAmerican\n873053\nloss\n21.554001\n\n\n115\n1968\nGeorge Wallace\nAmerican Independent\n9901118\nloss\n13.571218\n\n\n6\n1832\nWilliam Wirt\nAnti-Masonic\n100715\nloss\n7.821583\n\n\n38\n1884\nBenjamin Butler\nAnti-Monopoly\n134294\nloss\n1.335838\n\n\n127\n1980\nBarry Commoner\nCitizens\n233052\nloss\n0.270182\n\n\n\n\n\n\n\n\n# Using the .drop_duplicates function\nbest_per_party2 = elections.sort_values('%').drop_duplicates(['Party'], keep='last')\nbest_per_party2.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n148\n1996\nJohn Hagelin\nNatural Law\n113670\nloss\n0.118219\n\n\n164\n2008\nChuck Baldwin\nConstitution\n199750\nloss\n0.152398\n\n\n110\n1956\nT. Coleman Andrews\nStates' Rights\n107929\nloss\n0.174883\n\n\n147\n1996\nHoward Phillips\nTaxpayers\n184656\nloss\n0.192045\n\n\n136\n1988\nLenora Fulani\nNew Alliance\n217221\nloss\n0.237804",
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+    "text": "4.4 Aggregating Data with Pivot Tables\nWe know now that .groupby gives us the ability to group and aggregate data across our DataFrame. The examples above formed groups using just one column in the DataFrame. It’s possible to group by multiple columns at once by passing in a list of column names to .groupby.\nLet’s consider the babynames dataset again. In this problem, we will find the total number of baby names associated with each sex for each year. To do this, we’ll group by both the \"Year\" and \"Sex\" columns.\n\nbabynames.head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nFirst Letter\n\n\n\n\n115957\nCA\nF\n1990\nDeandrea\n5\nD\n\n\n101976\nCA\nF\n1986\nDeandrea\n6\nD\n\n\n131029\nCA\nF\n1994\nLeandrea\n5\nL\n\n\n108731\nCA\nF\n1988\nDeandrea\n5\nD\n\n\n308131\nCA\nM\n1985\nDeandrea\n6\nD\n\n\n\n\n\n\n\n\n# Find the total number of baby names associated with each sex for each \n# year in the data\nbabynames.groupby([\"Year\", \"Sex\"])[[\"Count\"]].agg(sum).head(6)\n\n/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19214/3186035650.py:3: FutureWarning:\n\nThe provided callable &lt;built-in function sum&gt; is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n\n\n\n\n\n\n\n\n\n\n\nCount\n\n\nYear\nSex\n\n\n\n\n\n1910\nF\n5950\n\n\nM\n3213\n\n\n1911\nF\n6602\n\n\nM\n3381\n\n\n1912\nF\n9804\n\n\nM\n8142\n\n\n\n\n\n\n\nNotice that both \"Year\" and \"Sex\" serve as the index of the DataFrame (they are both rendered in bold). We’ve created a multi-index DataFrame where two different index values, the year and sex, are used to uniquely identify each row.\nThis isn’t the most intuitive way of representing this data – and, because multi-indexed DataFrames have multiple dimensions in their index, they can often be difficult to use.\nAnother strategy to aggregate across two columns is to create a pivot table. You saw these back in Data 8. One set of values is used to create the index of the pivot table; another set is used to define the column names. The values contained in each cell of the table correspond to the aggregated data for each index-column pair.\nHere’s an illustration of the process:\n\nThe best way to understand pivot tables is to see one in action. Let’s return to our original goal of summing the total number of names associated with each combination of year and sex. We’ll call the pandas .pivot_table method to create a new table.\n\n# The `pivot_table` method is used to generate a Pandas pivot table\nimport numpy as np\nbabynames.pivot_table(\n    index = \"Year\",\n    columns = \"Sex\",    \n    values = \"Count\", \n    aggfunc = \"sum\", \n).head(5)\n\n\n\n\n\n\n\nSex\nF\nM\n\n\nYear\n\n\n\n\n\n\n1910\n5950\n3213\n\n\n1911\n6602\n3381\n\n\n1912\n9804\n8142\n\n\n1913\n11860\n10234\n\n\n1914\n13815\n13111\n\n\n\n\n\n\n\nLooks a lot better! Now, our DataFrame is structured with clear index-column combinations. Each entry in the pivot table represents the summed count of names for a given combination of \"Year\" and \"Sex\".\nLet’s take a closer look at the code implemented above.\n\nindex = \"Year\" specifies the column name in the original DataFrame that should be used as the index of the pivot table\ncolumns = \"Sex\" specifies the column name in the original DataFrame that should be used to generate the columns of the pivot table\nvalues = \"Count\" indicates what values from the original DataFrame should be used to populate the entry for each index-column combination\naggfunc = np.sum tells pandas what function to use when aggregating the data specified by values. Here, we are summing the name counts for each pair of \"Year\" and \"Sex\"\n\nWe can even include multiple values in the index or columns of our pivot tables.\n\nbabynames_pivot = babynames.pivot_table(\n    index=\"Year\",     # the rows (turned into index)\n    columns=\"Sex\",    # the column values\n    values=[\"Count\", \"Name\"], \n    aggfunc=\"max\",      # group operation\n)\nbabynames_pivot.head(6)\n\n\n\n\n\n\n\n\nCount\nName\n\n\nSex\nF\nM\nF\nM\n\n\nYear\n\n\n\n\n\n\n\n\n1910\n295\n237\nYvonne\nWilliam\n\n\n1911\n390\n214\nZelma\nWillis\n\n\n1912\n534\n501\nYvonne\nWoodrow\n\n\n1913\n584\n614\nZelma\nYoshio\n\n\n1914\n773\n769\nZelma\nYoshio\n\n\n1915\n998\n1033\nZita\nYukio\n\n\n\n\n\n\n\nNote that each row provides the number of girls and number of boys having that year’s most common name, and also lists the alphabetically largest girl name and boy name. The counts for number of girls/boys in the resulting DataFrame do not correspond to the names listed. For example, in 1910, the most popular girl name is given to 295 girls, but that name was likely not Yvonne.",
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+    "text": "4.5 Joining Tables\nWhen working on data science projects, we’re unlikely to have absolutely all the data we want contained in a single DataFrame – a real-world data scientist needs to grapple with data coming from multiple sources. If we have access to multiple datasets with related information, we can join two or more tables into a single DataFrame.\nTo put this into practice, we’ll revisit the elections dataset.\n\nelections.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n\n\n\n\n\nSay we want to understand the popularity of the names of each presidential candidate in 2022. To do this, we’ll need the combined data of babynames and elections.\nWe’ll start by creating a new column containing the first name of each presidential candidate. This will help us join each name in elections to the corresponding name data in babynames.\n\n# This `str` operation splits each candidate's full name at each \n# blank space, then takes just the candidate's first name\nelections[\"First Name\"] = elections[\"Candidate\"].str.split().str[0]\nelections.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\nFirst Name\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\nAndrew\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\nJohn\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\nAndrew\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\nJohn\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\nAndrew\n\n\n\n\n\n\n\n\n# Here, we'll only consider `babynames` data from 2022\nbabynames_2022 = babynames[babynames[\"Year\"]==2022]\nbabynames_2022.head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nFirst Letter\n\n\n\n\n237964\nCA\nF\n2022\nLeandra\n10\nL\n\n\n404916\nCA\nM\n2022\nLeandro\n99\nL\n\n\n405892\nCA\nM\n2022\nAndreas\n14\nA\n\n\n235927\nCA\nF\n2022\nAndrea\n322\nA\n\n\n405695\nCA\nM\n2022\nDeandre\n18\nD\n\n\n\n\n\n\n\nNow, we’re ready to join the two tables. pd.merge is the pandas method used to join DataFrames together.\n\nmerged = pd.merge(left = elections, right = babynames_2022, \\\n                  left_on = \"First Name\", right_on = \"Name\")\nmerged.head()\n# Notice that pandas automatically specifies `Year_x` and `Year_y` \n# when both merged DataFrames have the same column name to avoid confusion\n\n# Second option\n# merged = elections.merge(right = babynames_2022, \\\n    # left_on = \"First Name\", right_on = \"Name\")\n\n\n\n\n\n\n\n\nYear_x\nCandidate\nParty\nPopular vote\nResult\n%\nFirst Name\nState\nSex\nYear_y\nName\nCount\nFirst Letter\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\nAndrew\nCA\nM\n2022\nAndrew\n741\nA\n\n\n1\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\nAndrew\nCA\nM\n2022\nAndrew\n741\nA\n\n\n2\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\nAndrew\nCA\nM\n2022\nAndrew\n741\nA\n\n\n3\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\nJohn\nCA\nM\n2022\nJohn\n490\nJ\n\n\n4\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\nJohn\nCA\nM\n2022\nJohn\n490\nJ\n\n\n\n\n\n\n\nLet’s take a closer look at the parameters:\n\nleft and right parameters are used to specify the DataFrames to be joined.\nleft_on and right_on parameters are assigned to the string names of the columns to be used when performing the join. These two on parameters tell pandas what values should act as pairing keys to determine which rows to merge across the DataFrames. We’ll talk more about this idea of a pairing key next lecture.",
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+    "text": "4.6 Parting Note\nCongratulations! We finally tackled pandas. Don’t worry if you are still not feeling very comfortable with it—you will have plenty of chances to practice over the next few weeks.\nNext, we will get our hands dirty with some real-world datasets and use our pandas knowledge to conduct some exploratory data analysis.",
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+    "text": "3.1 Conditional Selection\nConditional selection allows us to select a subset of rows in a DataFrame that satisfy some specified condition.\nTo understand how to use conditional selection, we must look at another possible input of the .loc and [] methods – a boolean array, which is simply an array or Series where each element is either True or False. This boolean array must have a length equal to the number of rows in the DataFrame. It will return all rows that correspond to a value of True in the array. We used a very similar technique when performing conditional extraction from a Series in the last lecture.\nTo see this in action, let’s select all even-indexed rows in the first 10 rows of our DataFrame.\n# Ask yourself: why is :9 is the correct slice to select the first 10 rows?\nbabynames_first_10_rows = babynames.loc[:9, :]\n\n# Notice how we have exactly 10 elements in our boolean array argument\nbabynames_first_10_rows[[True, False, True, False, True, False, True, False, True, False]]\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n4\nCA\nF\n1910\nFrances\n134\n\n\n6\nCA\nF\n1910\nEvelyn\n126\n\n\n8\nCA\nF\n1910\nVirginia\n101\nWe can perform a similar operation using .loc.\nbabynames_first_10_rows.loc[[True, False, True, False, True, False, True, False, True, False], :]\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n4\nCA\nF\n1910\nFrances\n134\n\n\n6\nCA\nF\n1910\nEvelyn\n126\n\n\n8\nCA\nF\n1910\nVirginia\n101\nThese techniques worked well in this example, but you can imagine how tedious it might be to list out True and Falsefor every row in a larger DataFrame. To make things easier, we can instead provide a logical condition as an input to .loc or [] that returns a boolean array with the necessary length.\nFor example, to return all names associated with F sex:\n# First, use a logical condition to generate a boolean array\nlogical_operator = (babynames[\"Sex\"] == \"F\")\n\n# Then, use this boolean array to filter the DataFrame\nbabynames[logical_operator].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134\nRecall from the previous lecture that .head() will return only the first few rows in the DataFrame. In reality, babynames[logical operator] contains as many rows as there are entries in the original babynames DataFrame with sex \"F\".\nHere, logical_operator evaluates to a Series of boolean values with length 407428.\nCode\nprint(\"There are a total of {} values in 'logical_operator'\".format(len(logical_operator)))\n\n\nThere are a total of 407428 values in 'logical_operator'\nRows starting at row 0 and ending at row 239536 evaluate to True and are thus returned in the DataFrame. Rows from 239537 onwards evaluate to False and are omitted from the output.\nCode\nprint(\"The 0th item in this 'logical_operator' is: {}\".format(logical_operator.iloc[0]))\nprint(\"The 239536th item in this 'logical_operator' is: {}\".format(logical_operator.iloc[239536]))\nprint(\"The 239537th item in this 'logical_operator' is: {}\".format(logical_operator.iloc[239537]))\n\n\nThe 0th item in this 'logical_operator' is: True\nThe 239536th item in this 'logical_operator' is: True\nThe 239537th item in this 'logical_operator' is: False\nPassing a Series as an argument to babynames[] has the same effect as using a boolean array. In fact, the [] selection operator can take a boolean Series, array, and list as arguments. These three are used interchangeably throughout the course.\nWe can also use .loc to achieve similar results.\nbabynames.loc[babynames[\"Sex\"] == \"F\"].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134\nBoolean conditions can be combined using various bitwise operators, allowing us to filter results by multiple conditions. In the table below, p and q are boolean arrays or Series.\nWhen combining multiple conditions with logical operators, we surround each individual condition with a set of parenthesis (). This imposes an order of operations on pandas evaluating your logic and can avoid code erroring.\nFor example, if we want to return data on all names with sex \"F\" born before the year 2000, we can write:\nbabynames[(babynames[\"Sex\"] == \"F\") & (babynames[\"Year\"] &lt; 2000)].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134\nNote that we’re working with Series, so using and in place of &, or or in place | will error.\n# This line of code will raise a ValueError\n# babynames[(babynames[\"Sex\"] == \"F\") and (babynames[\"Year\"] &lt; 2000)].head()\nIf we want to return data on all names with sex \"F\" or all born before the year 2000, we can write:\nbabynames[(babynames[\"Sex\"] == \"F\") | (babynames[\"Year\"] &lt; 2000)].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134\nBoolean array selection is a useful tool, but can lead to overly verbose code for complex conditions. In the example below, our boolean condition is long enough to extend for several lines of code.\n# Note: The parentheses surrounding the code make it possible to break the code on to multiple lines for readability\n(\n    babynames[(babynames[\"Name\"] == \"Bella\") | \n              (babynames[\"Name\"] == \"Alex\") |\n              (babynames[\"Name\"] == \"Ani\") |\n              (babynames[\"Name\"] == \"Lisa\")]\n).head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n6289\nCA\nF\n1923\nBella\n5\n\n\n7512\nCA\nF\n1925\nBella\n8\n\n\n12368\nCA\nF\n1932\nLisa\n5\n\n\n14741\nCA\nF\n1936\nLisa\n8\n\n\n17084\nCA\nF\n1939\nLisa\n5\nFortunately, pandas provides many alternative methods for constructing boolean filters.\nThe .isin function is one such example. This method evaluates if the values in a Series are contained in a different sequence (list, array, or Series) of values. In the cell below, we achieve equivalent results to the DataFrame above with far more concise code.\nnames = [\"Bella\", \"Alex\", \"Narges\", \"Lisa\"]\nbabynames[\"Name\"].isin(names).head()\n\n0    False\n1    False\n2    False\n3    False\n4    False\nName: Name, dtype: bool\nbabynames[babynames[\"Name\"].isin(names)].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n6289\nCA\nF\n1923\nBella\n5\n\n\n7512\nCA\nF\n1925\nBella\n8\n\n\n12368\nCA\nF\n1932\nLisa\n5\n\n\n14741\nCA\nF\n1936\nLisa\n8\n\n\n17084\nCA\nF\n1939\nLisa\n5\nThe function str.startswith can be used to define a filter based on string values in a Series object. It checks to see if string values in a Series start with a particular character.\n# Identify whether names begin with the letter \"N\"\nbabynames[\"Name\"].str.startswith(\"N\").head()\n\n0    False\n1    False\n2    False\n3    False\n4    False\nName: Name, dtype: bool\n# Extracting names that begin with the letter \"N\"\nbabynames[babynames[\"Name\"].str.startswith(\"N\")].head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n76\nCA\nF\n1910\nNorma\n23\n\n\n83\nCA\nF\n1910\nNellie\n20\n\n\n127\nCA\nF\n1910\nNina\n11\n\n\n198\nCA\nF\n1910\nNora\n6\n\n\n310\nCA\nF\n1911\nNellie\n23",
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+    "text": "3.2 Adding, Removing, and Modifying Columns\nIn many data science tasks, we may need to change the columns contained in our DataFrame in some way. Fortunately, the syntax to do so is fairly straightforward.\nTo add a new column to a DataFrame, we use a syntax similar to that used when accessing an existing column. Specify the name of the new column by writing df[\"column\"], then assign this to a Series or array containing the values that will populate this column.\n\n# Create a Series of the length of each name. \nbabyname_lengths = babynames[\"Name\"].str.len()\n\n# Add a column named \"name_lengths\" that includes the length of each name\nbabynames[\"name_lengths\"] = babyname_lengths\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nname_lengths\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n4\n\n\n1\nCA\nF\n1910\nHelen\n239\n5\n\n\n2\nCA\nF\n1910\nDorothy\n220\n7\n\n\n3\nCA\nF\n1910\nMargaret\n163\n8\n\n\n4\nCA\nF\n1910\nFrances\n134\n7\n\n\n\n\n\n\n\nIf we need to later modify an existing column, we can do so by referencing this column again with the syntax df[\"column\"], then re-assigning it to a new Series or array of the appropriate length.\n\n# Modify the “name_lengths” column to be one less than its original value\nbabynames[\"name_lengths\"] = babynames[\"name_lengths\"] - 1\nbabynames.head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nname_lengths\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n3\n\n\n1\nCA\nF\n1910\nHelen\n239\n4\n\n\n2\nCA\nF\n1910\nDorothy\n220\n6\n\n\n3\nCA\nF\n1910\nMargaret\n163\n7\n\n\n4\nCA\nF\n1910\nFrances\n134\n6\n\n\n\n\n\n\n\nWe can rename a column using the .rename() method. It takes in a dictionary that maps old column names to their new ones.\n\n# Rename “name_lengths” to “Length”\nbabynames = babynames.rename(columns={\"name_lengths\":\"Length\"})\nbabynames.head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\nLength\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n3\n\n\n1\nCA\nF\n1910\nHelen\n239\n4\n\n\n2\nCA\nF\n1910\nDorothy\n220\n6\n\n\n3\nCA\nF\n1910\nMargaret\n163\n7\n\n\n4\nCA\nF\n1910\nFrances\n134\n6\n\n\n\n\n\n\n\nIf we want to remove a column or row of a DataFrame, we can call the .drop (documentation) method. Use the axis parameter to specify whether a column or row should be dropped. Unless otherwise specified, pandas will assume that we are dropping a row by default.\n\n# Drop our new \"Length\" column from the DataFrame\nbabynames = babynames.drop(\"Length\", axis=\"columns\")\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134\n\n\n\n\n\n\n\nNotice that we re-assigned babynames to the result of babynames.drop(...). This is a subtle but important point: pandas table operations do not occur in-place. Calling df.drop(...) will output a copy of df with the row/column of interest removed without modifying the original df table.\nIn other words, if we simply call:\n\n# This creates a copy of `babynames` and removes the column \"Name\"...\nbabynames.drop(\"Name\", axis=\"columns\")\n\n# ...but the original `babynames` is unchanged! \n# Notice that the \"Name\" column is still present\nbabynames.head(5)\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n0\nCA\nF\n1910\nMary\n295\n\n\n1\nCA\nF\n1910\nHelen\n239\n\n\n2\nCA\nF\n1910\nDorothy\n220\n\n\n3\nCA\nF\n1910\nMargaret\n163\n\n\n4\nCA\nF\n1910\nFrances\n134",
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+    "text": "3.3 Useful Utility Functions\npandas contains an extensive library of functions that can help shorten the process of setting and getting information from its data structures. In the following section, we will give overviews of each of the main utility functions that will help us in Data 100.\nDiscussing all functionality offered by pandas could take an entire semester! We will walk you through the most commonly-used functions and encourage you to explore and experiment on your own.\n\nNumPy and built-in function support\n.shape\n.size\n.describe()\n.sample()\n.value_counts()\n.unique()\n.sort_values()\n\nThe pandas documentation will be a valuable resource in Data 100 and beyond.\n\n3.3.1 NumPy\npandas is designed to work well with NumPy, the framework for array computations you encountered in Data 8. Just about any NumPy function can be applied to pandas DataFrames and Series.\n\n# Pull out the number of babies named Yash each year\nyash_count = babynames[babynames[\"Name\"] == \"Yash\"][\"Count\"]\nyash_count.head()\n\n331824     8\n334114     9\n336390    11\n338773    12\n341387    10\nName: Count, dtype: int64\n\n\n\n# Average number of babies named Yash each year\nnp.mean(yash_count)\n\n17.142857142857142\n\n\n\n# Max number of babies named Yash born in any one year\nnp.max(yash_count)\n\n29\n\n\n\n\n3.3.2 .shape and .size\n.shape and .size are attributes of Series and DataFrames that measure the “amount” of data stored in the structure. Calling .shape returns a tuple containing the number of rows and columns present in the DataFrame or Series. .size is used to find the total number of elements in a structure, equivalent to the number of rows times the number of columns.\nMany functions strictly require the dimensions of the arguments along certain axes to match. Calling these dimension-finding functions is much faster than counting all of the items by hand.\n\n# Return the shape of the DataFrame, in the format (num_rows, num_columns)\nbabynames.shape\n\n(407428, 5)\n\n\n\n# Return the size of the DataFrame, equal to num_rows * num_columns\nbabynames.size\n\n2037140\n\n\n\n\n3.3.3 .describe()\nIf many statistics are required from a DataFrame (minimum value, maximum value, mean value, etc.), then .describe() (documentation) can be used to compute all of them at once.\n\nbabynames.describe()\n\n\n\n\n\n\n\n\nYear\nCount\n\n\n\n\ncount\n407428.000000\n407428.000000\n\n\nmean\n1985.733609\n79.543456\n\n\nstd\n27.007660\n293.698654\n\n\nmin\n1910.000000\n5.000000\n\n\n25%\n1969.000000\n7.000000\n\n\n50%\n1992.000000\n13.000000\n\n\n75%\n2008.000000\n38.000000\n\n\nmax\n2022.000000\n8260.000000\n\n\n\n\n\n\n\nA different set of statistics will be reported if .describe() is called on a Series.\n\nbabynames[\"Sex\"].describe()\n\ncount     407428\nunique         2\ntop            F\nfreq      239537\nName: Sex, dtype: object\n\n\n\n\n3.3.4 .sample()\nAs we will see later in the semester, random processes are at the heart of many data science techniques (for example, train-test splits, bootstrapping, and cross-validation). .sample() (documentation) lets us quickly select random entries (a row if called from a DataFrame, or a value if called from a Series).\nBy default, .sample() selects entries without replacement. Pass in the argument replace=True to sample with replacement.\n\n# Sample a single row\nbabynames.sample()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n218266\nCA\nF\n2017\nCalliope\n40\n\n\n\n\n\n\n\nNaturally, this can be chained with other methods and operators (iloc, etc.).\n\n# Sample 5 random rows, and select all columns after column 2\nbabynames.sample(5).iloc[:, 2:]\n\n\n\n\n\n\n\n\nYear\nName\nCount\n\n\n\n\n89270\n1982\nSuzette\n29\n\n\n290263\n1975\nLloyd\n61\n\n\n320664\n1991\nMalik\n26\n\n\n106120\n1988\nAnnette\n113\n\n\n257630\n1945\nSammy\n46\n\n\n\n\n\n\n\n\n# Randomly sample 4 names from the year 2000, with replacement, and select all columns after column 2\nbabynames[babynames[\"Year\"] == 2000].sample(4, replace = True).iloc[:, 2:]\n\n\n\n\n\n\n\n\nYear\nName\nCount\n\n\n\n\n342774\n2000\nAlvin\n111\n\n\n150041\n2000\nKacey\n30\n\n\n344634\n2000\nAnwar\n5\n\n\n343348\n2000\nLeopoldo\n19\n\n\n\n\n\n\n\n\n\n3.3.5 .value_counts()\nThe Series.value_counts() (documentation) method counts the number of occurrence of each unique value in a Series. In other words, it counts the number of times each unique value appears. This is often useful for determining the most or least common entries in a Series.\nIn the example below, we can determine the name with the most years in which at least one person has taken that name by counting the number of times each name appears in the \"Name\" column of babynames. Note that the return value is also a Series.\n\nbabynames[\"Name\"].value_counts().head()\n\nName\nJean         223\nFrancis      221\nGuadalupe    218\nJessie       217\nMarion       214\nName: count, dtype: int64\n\n\n\n\n3.3.6 .unique()\nIf we have a Series with many repeated values, then .unique() (documentation) can be used to identify only the unique values. Here we return an array of all the names in babynames.\n\nbabynames[\"Name\"].unique()\n\narray(['Mary', 'Helen', 'Dorothy', ..., 'Zae', 'Zai', 'Zayvier'],\n      dtype=object)\n\n\n\n\n3.3.7 .sort_values()\nOrdering a DataFrame can be useful for isolating extreme values. For example, the first 5 entries of a row sorted in descending order (that is, from highest to lowest) are the largest 5 values. .sort_values (documentation) allows us to order a DataFrame or Series by a specified column. We can choose to either receive the rows in ascending order (default) or descending order.\n\n# Sort the \"Count\" column from highest to lowest\nbabynames.sort_values(by=\"Count\", ascending=False).head()\n\n\n\n\n\n\n\n\nState\nSex\nYear\nName\nCount\n\n\n\n\n268041\nCA\nM\n1957\nMichael\n8260\n\n\n267017\nCA\nM\n1956\nMichael\n8258\n\n\n317387\nCA\nM\n1990\nMichael\n8246\n\n\n281850\nCA\nM\n1969\nMichael\n8245\n\n\n283146\nCA\nM\n1970\nMichael\n8196\n\n\n\n\n\n\n\nUnlike when calling .value_counts() on a DataFrame, we do not need to explicitly specify the column used for sorting when calling .value_counts() on a Series. We can still specify the ordering paradigm – that is, whether values are sorted in ascending or descending order.\n\n# Sort the \"Name\" Series alphabetically\nbabynames[\"Name\"].sort_values(ascending=True).head()\n\n366001      Aadan\n384005      Aadan\n369120      Aadan\n398211    Aadarsh\n370306      Aaden\nName: Name, dtype: object",
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+    "text": "3.4 Parting Note\nManipulating DataFrames is not a skill that is mastered in just one day. Due to the flexibility of pandas, there are many different ways to get from point A to point B. We recommend trying multiple different ways to solve the same problem to gain even more practice and reach that point of mastery sooner.\nNext, we will start digging deeper into the mechanics behind grouping data.",
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+    "text": "2.1 Tabular Data\nData scientists work with data stored in a variety of formats. This class focuses primarily on tabular data — data that is stored in a table.\nTabular data is one of the most common systems that data scientists use to organize data. This is in large part due to the simplicity and flexibility of tables. Tables allow us to represent each observation, or instance of collecting data from an individual, as its own row. We can record each observation’s distinct characteristics, or features, in separate columns.\nTo see this in action, we’ll explore the elections dataset, which stores information about political candidates who ran for president of the United States in previous years.\nIn the elections dataset, each row (blue box) represents one instance of a candidate running for president in a particular year. For example, the first row represents Andrew Jackson running for president in the year 1824. Each column (yellow box) represents one characteristic piece of information about each presidential candidate. For example, the column named “Result” stores whether or not the candidate won the election.\nYour work in Data 8 helped you grow very familiar with using and interpreting data stored in a tabular format. Back then, you used the Table class of the datascience library, a special programming library created specifically for Data 8 students.\nIn Data 100, we will be working with the programming library pandas, which is generally accepted in the data science community as the industry- and academia-standard tool for manipulating tabular data (as well as the inspiration for Petey, our panda bear mascot).\nUsing pandas, we can",
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+    "text": "Arrange data in a tabular format.\nExtract useful information filtered by specific conditions.\nOperate on data to gain new insights.\nApply NumPy functions to our data (our friends from Data 8).\nPerform vectorized computations to speed up our analysis (Lab 1).",
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+    "text": "2.2 Series, DataFrames, and Indices\nTo begin our work in pandas, we must first import the library into our Python environment. This will allow us to use pandas data structures and methods in our code.\n\n# `pd` is the conventional alias for Pandas, as `np` is for NumPy\nimport pandas as pd\n\nThere are three fundamental data structures in pandas:\n\nSeries: 1D labeled array data; best thought of as columnar data.\nDataFrame: 2D tabular data with rows and columns.\nIndex: A sequence of row/column labels.\n\nDataFrames, Series, and Indices can be represented visually in the following diagram, which considers the first few rows of the elections dataset.\n\n\n\nNotice how the DataFrame is a two-dimensional object — it contains both rows and columns. The Series above is a singular column of this DataFrame, namely the Result column. Both contain an Index, or a shared list of row labels (the integers from 0 to 4, inclusive).\n\n2.2.1 Series\nA Series represents a column of a DataFrame; more generally, it can be any 1-dimensional array-like object. It contains both:\n\nA sequence of values of the same type.\nA sequence of data labels called the index.\n\nIn the cell below, we create a Series named s.\n\ns = pd.Series([\"welcome\", \"to\", \"data 100\"])\ns\n\n0     welcome\n1          to\n2    data 100\ndtype: object\n\n\n\n # Accessing data values within the Series\n s.values\n\narray(['welcome', 'to', 'data 100'], dtype=object)\n\n\n\n # Accessing the Index of the Series\n s.index\n\nRangeIndex(start=0, stop=3, step=1)\n\n\nBy default, the index of a Series is a sequential list of integers beginning from 0. Optionally, a manually specified list of desired indices can be passed to the index argument.\n\ns = pd.Series([-1, 10, 2], index = [\"a\", \"b\", \"c\"])\ns\n\na    -1\nb    10\nc     2\ndtype: int64\n\n\n\ns.index\n\nIndex(['a', 'b', 'c'], dtype='object')\n\n\nIndices can also be changed after initialization.\n\ns.index = [\"first\", \"second\", \"third\"]\ns\n\nfirst     -1\nsecond    10\nthird      2\ndtype: int64\n\n\n\ns.index\n\nIndex(['first', 'second', 'third'], dtype='object')\n\n\n\n2.2.1.1 Selection in Series\nMuch like when working with NumPy arrays, we can select a single value or a set of values from a Series. To do so, there are three primary methods:\n\nA single label.\nA list of labels.\nA filtering condition.\n\nTo demonstrate this, let’s define a new Series s.\n\ns = pd.Series([4, -2, 0, 6], index = [\"a\", \"b\", \"c\", \"d\"])\ns\n\na    4\nb   -2\nc    0\nd    6\ndtype: int64\n\n\n\n2.2.1.1.1 A Single Label\n\n# We return the value stored at the index label \"a\"\ns[\"a\"] \n\n4\n\n\n\n\n2.2.1.1.2 A List of Labels\n\n# We return a Series of the values stored at the index labels \"a\" and \"c\"\ns[[\"a\", \"c\"]] \n\na    4\nc    0\ndtype: int64\n\n\n\n\n2.2.1.1.3 A Filtering Condition\nPerhaps the most interesting (and useful) method of selecting data from a Series is by using a filtering condition.\nFirst, we apply a boolean operation to the Series. This creates a new Series of boolean values.\n\n# Filter condition: select all elements greater than 0\ns &gt; 0 \n\na     True\nb    False\nc    False\nd     True\ndtype: bool\n\n\nWe then use this boolean condition to index into our original Series. pandas will select only the entries in the original Series that satisfy the condition.\n\ns[s &gt; 0] \n\na    4\nd    6\ndtype: int64\n\n\n\n\n\n\n2.2.2 DataFrames\nTypically, we will work with Series using the perspective that they are columns in a DataFrame. We can think of a DataFrame as a collection of Series that all share the same Index.\nIn Data 8, you encountered the Table class of the datascience library, which represented tabular data. In Data 100, we’ll be using the DataFrame class of the pandas library.\n\n2.2.2.1 Creating a DataFrame\nThere are many ways to create a DataFrame. Here, we will cover the most popular approaches:\n\nFrom a CSV file.\nUsing a list and column name(s).\nFrom a dictionary.\nFrom a Series.\n\nMore generally, the syntax for creating a DataFrame is:\n pandas.DataFrame(data, index, columns)\n\n2.2.2.1.1 From a CSV file\nIn Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a DataFrame by passing the data path as an argument to the following pandas function.   pd.read_csv(\"filename.csv\")\nWith our new understanding of pandas in hand, let’s return to the elections dataset from before. Now, we can recognize that it is represented as a pandas DataFrame.\n\nelections = pd.read_csv(\"data/elections.csv\")\nelections\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n1457226\nloss\n1.073699\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\nwin\n51.311515\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\nloss\n46.858542\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\nloss\n1.177979\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 6 columns\n\n\n\nThis code stores our DataFrame object in the elections variable. Upon inspection, our elections DataFrame has 182 rows and 6 columns (Year, Candidate, Party, Popular Vote, Result, %). Each row represents a single record — in our example, a presidential candidate from some particular year. Each column represents a single attribute or feature of the record.\n\n\n2.2.2.1.2 Using a List and Column Name(s)\nWe’ll now explore creating a DataFrame with data of our own.\nConsider the following examples. The first code cell creates a DataFrame with a single column Numbers.\n\ndf_list = pd.DataFrame([1, 2, 3], columns=[\"Numbers\"])\ndf_list\n\n\n\n\n\n\n\n\nNumbers\n\n\n\n\n0\n1\n\n\n1\n2\n\n\n2\n3\n\n\n\n\n\n\n\nThe second creates a DataFrame with the columns Numbers and Description. Notice how a 2D list of values is required to initialize the second DataFrame — each nested list represents a single row of data.\n\ndf_list = pd.DataFrame([[1, \"one\"], [2, \"two\"]], columns = [\"Number\", \"Description\"])\ndf_list\n\n\n\n\n\n\n\n\nNumber\nDescription\n\n\n\n\n0\n1\none\n\n\n1\n2\ntwo\n\n\n\n\n\n\n\n\n\n2.2.2.1.3 From a Dictionary\nA third (and more common) way to create a DataFrame is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.\nBelow are two ways of implementing this approach. The first is based on specifying the columns of the DataFrame, whereas the second is based on specifying the rows of the DataFrame.\n\ndf_dict = pd.DataFrame({\n    \"Fruit\": [\"Strawberry\", \"Orange\"], \n    \"Price\": [5.49, 3.99]\n})\ndf_dict\n\n\n\n\n\n\n\n\nFruit\nPrice\n\n\n\n\n0\nStrawberry\n5.49\n\n\n1\nOrange\n3.99\n\n\n\n\n\n\n\n\ndf_dict = pd.DataFrame(\n    [\n        {\"Fruit\":\"Strawberry\", \"Price\":5.49}, \n        {\"Fruit\": \"Orange\", \"Price\":3.99}\n    ]\n)\ndf_dict\n\n\n\n\n\n\n\n\nFruit\nPrice\n\n\n\n\n0\nStrawberry\n5.49\n\n\n1\nOrange\n3.99\n\n\n\n\n\n\n\n\n\n2.2.2.1.4 From a Series\nEarlier, we explained how a Series was synonymous to a column in a DataFrame. It follows, then, that a DataFrame is equivalent to a collection of Series, which all share the same Index.\nIn fact, we can initialize a DataFrame by merging two or more Series. Consider the Series s_a and s_b.\n\n# Notice how our indices, or row labels, are the same\n\ns_a = pd.Series([\"a1\", \"a2\", \"a3\"], index = [\"r1\", \"r2\", \"r3\"])\ns_b = pd.Series([\"b1\", \"b2\", \"b3\"], index = [\"r1\", \"r2\", \"r3\"])\n\nWe can turn individual Series into a DataFrame using two common methods (shown below):\n\npd.DataFrame(s_a)\n\n\n\n\n\n\n\n\n0\n\n\n\n\nr1\na1\n\n\nr2\na2\n\n\nr3\na3\n\n\n\n\n\n\n\n\ns_b.to_frame()\n\n\n\n\n\n\n\n\n0\n\n\n\n\nr1\nb1\n\n\nr2\nb2\n\n\nr3\nb3\n\n\n\n\n\n\n\nTo merge the two Series and specify their column names, we use the following syntax:\n\npd.DataFrame({\n    \"A-column\": s_a, \n    \"B-column\": s_b\n})\n\n\n\n\n\n\n\n\nA-column\nB-column\n\n\n\n\nr1\na1\nb1\n\n\nr2\na2\nb2\n\n\nr3\na3\nb3\n\n\n\n\n\n\n\n\n\n\n\n2.2.3 Indices\nOn a more technical note, an index doesn’t have to be an integer, nor does it have to be unique. For example, we can set the index of the elections DataFrame to be the name of presidential candidates.\n\n# Creating a DataFrame from a CSV file and specifying the index column\nelections = pd.read_csv(\"data/elections.csv\", index_col = \"Candidate\")\nelections\n\n\n\n\n\n\n\n\nYear\nParty\nPopular vote\nResult\n%\n\n\nCandidate\n\n\n\n\n\n\n\n\n\nAndrew Jackson\n1824\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\nJohn Quincy Adams\n1824\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\nAndrew Jackson\n1828\nDemocratic\n642806\nwin\n56.203927\n\n\nJohn Quincy Adams\n1828\nNational Republican\n500897\nloss\n43.796073\n\n\nAndrew Jackson\n1832\nDemocratic\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n\n\nJill Stein\n2016\nGreen\n1457226\nloss\n1.073699\n\n\nJoseph Biden\n2020\nDemocratic\n81268924\nwin\n51.311515\n\n\nDonald Trump\n2020\nRepublican\n74216154\nloss\n46.858542\n\n\nJo Jorgensen\n2020\nLibertarian\n1865724\nloss\n1.177979\n\n\nHoward Hawkins\n2020\nGreen\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 5 columns\n\n\n\nWe can also select a new column and set it as the index of the DataFrame. For example, we can set the index of the elections DataFrame to represent the candidate’s party.\n\nelections.reset_index(inplace = True) # Resetting the index so we can set it again\n# This sets the index to the \"Party\" column\nelections.set_index(\"Party\")\n\n\n\n\n\n\n\n\nCandidate\nYear\nPopular vote\nResult\n%\n\n\nParty\n\n\n\n\n\n\n\n\n\nDemocratic-Republican\nAndrew Jackson\n1824\n151271\nloss\n57.210122\n\n\nDemocratic-Republican\nJohn Quincy Adams\n1824\n113142\nwin\n42.789878\n\n\nDemocratic\nAndrew Jackson\n1828\n642806\nwin\n56.203927\n\n\nNational Republican\nJohn Quincy Adams\n1828\n500897\nloss\n43.796073\n\n\nDemocratic\nAndrew Jackson\n1832\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n\n\nGreen\nJill Stein\n2016\n1457226\nloss\n1.073699\n\n\nDemocratic\nJoseph Biden\n2020\n81268924\nwin\n51.311515\n\n\nRepublican\nDonald Trump\n2020\n74216154\nloss\n46.858542\n\n\nLibertarian\nJo Jorgensen\n2020\n1865724\nloss\n1.177979\n\n\nGreen\nHoward Hawkins\n2020\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 5 columns\n\n\n\nAnd, if we’d like, we can revert the index back to the default list of integers.\n\n# This resets the index to be the default list of integer\nelections.reset_index(inplace=True) \nelections.index\n\nRangeIndex(start=0, stop=182, step=1)\n\n\nIt is also important to note that the row labels that constitute an index don’t have to be unique. While index values can be unique and numeric, acting as a row number, they can also be named and non-unique.\nHere we see unique and numeric index values.\n\n\n\nHowever, here the index values are not unique.",
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+    "text": "2.3 DataFrame Attributes: Index, Columns, and Shape\nOn the other hand, column names in a DataFrame are almost always unique. Looking back to the elections dataset, it wouldn’t make sense to have two columns named \"Candidate\". Sometimes, you’ll want to extract these different values, in particular, the list of row and column labels.\nFor index/row labels, use DataFrame.index:\n\nelections.set_index(\"Party\", inplace = True)\nelections.index\n\nIndex(['Democratic-Republican', 'Democratic-Republican', 'Democratic',\n       'National Republican', 'Democratic', 'National Republican',\n       'Anti-Masonic', 'Whig', 'Democratic', 'Whig',\n       ...\n       'Constitution', 'Republican', 'Independent', 'Libertarian',\n       'Democratic', 'Green', 'Democratic', 'Republican', 'Libertarian',\n       'Green'],\n      dtype='object', name='Party', length=182)\n\n\nFor column labels, use DataFrame.columns:\n\nelections.columns\n\nIndex(['index', 'Candidate', 'Year', 'Popular vote', 'Result', '%'], dtype='object')\n\n\nAnd for the shape of the DataFrame, we can use DataFrame.shape to get the number of rows followed by the number of columns:\n\nelections.shape\n\n(182, 6)",
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+    "text": "2.4 Slicing in DataFrames\nNow that we’ve learned more about DataFrames, let’s dive deeper into their capabilities.\nThe API (Application Programming Interface) for the DataFrame class is enormous. In this section, we’ll discuss several methods of the DataFrame API that allow us to extract subsets of data.\nThe simplest way to manipulate a DataFrame is to extract a subset of rows and columns, known as slicing.\nCommon ways we may want to extract data are grabbing:\n\nThe first or last n rows in the DataFrame.\nData with a certain label.\nData at a certain position.\n\nWe will do so with four primary methods of the DataFrame class:\n\n.head and .tail\n.loc\n.iloc\n[]\n\n\n2.4.1 Extracting data with .head and .tail\nThe simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the DataFrame.\nTo extract the first n rows of a DataFrame df, we use the syntax df.head(n).\n\n\nCode\nelections = pd.read_csv(\"data/elections.csv\")\n\n\n\n# Extract the first 5 rows of the DataFrame\nelections.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n\n\n\n\n\nSimilarly, calling df.tail(n) allows us to extract the last n rows of the DataFrame.\n\n# Extract the last 5 rows of the DataFrame\nelections.tail(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n177\n2016\nJill Stein\nGreen\n1457226\nloss\n1.073699\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\nwin\n51.311515\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\nloss\n46.858542\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\nloss\n1.177979\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\nloss\n0.255731\n\n\n\n\n\n\n\n\n\n2.4.2 Label-based Extraction: Indexing with .loc\nFor the more complex task of extracting data with specific column or index labels, we can use .loc. The .loc accessor allows us to specify the labels of rows and columns we wish to extract. The labels (commonly referred to as the indices) are the bold text on the far left of a DataFrame, while the column labels are the column names found at the top of a DataFrame.\n\n\n\nTo grab data with .loc, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the .loc function; the column labels are the second.\nArguments to .loc can be:\n\nA single value.\nA slice.\nA list.\n\nFor example, to select a single value, we can select the row labeled 0 and the column labeled Candidate from the elections DataFrame.\n\nelections.loc[0, 'Candidate']\n\n'Andrew Jackson'\n\n\nKeep in mind that passing in just one argument as a single value will produce a Series. Below, we’ve extracted a subset of the \"Popular vote\" column as a Series.\n\nelections.loc[[87, 25, 179], \"Popular vote\"]\n\n87     15761254\n25       848019\n179    74216154\nName: Popular vote, dtype: int64\n\n\nNote that if we pass \"Popular vote\" as a list, the output will be a DataFrame.\n\nelections.loc[[87, 25, 179], [\"Popular vote\"]]\n\n\n\n\n\n\n\n\nPopular vote\n\n\n\n\n87\n15761254\n\n\n25\n848019\n\n\n179\n74216154\n\n\n\n\n\n\n\nTo select multiple rows and columns, we can use Python slice notation. Here, we select the rows from labels 0 to 3 and the columns from labels \"Year\" to \"Popular vote\". Notice that unlike Python slicing, .loc is inclusive of the right upper bound.\n\nelections.loc[0:3, 'Year':'Popular vote']\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nSuppose that instead, we want to extract all column values for the first four rows in the elections DataFrame. The shorthand : is useful for this.\n\nelections.loc[0:3, :]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\nWe can use the same shorthand to extract all rows.\n\nelections.loc[:, [\"Year\", \"Candidate\", \"Result\"]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nResult\n\n\n\n\n0\n1824\nAndrew Jackson\nloss\n\n\n1\n1824\nJohn Quincy Adams\nwin\n\n\n2\n1828\nAndrew Jackson\nwin\n\n\n3\n1828\nJohn Quincy Adams\nloss\n\n\n4\n1832\nAndrew Jackson\nwin\n\n\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nloss\n\n\n178\n2020\nJoseph Biden\nwin\n\n\n179\n2020\nDonald Trump\nloss\n\n\n180\n2020\nJo Jorgensen\nloss\n\n\n181\n2020\nHoward Hawkins\nloss\n\n\n\n\n182 rows × 3 columns\n\n\n\nThere are a couple of things we should note. Firstly, unlike conventional Python, pandas allows us to slice string values (in our example, the column labels). Secondly, slicing with .loc is inclusive. Notice how our resulting DataFrame includes every row and column between and including the slice labels we specified.\nEquivalently, we can use a list to obtain multiple rows and columns in our elections DataFrame.\n\nelections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nLastly, we can interchange list and slicing notation.\n\nelections.loc[[0, 1, 2, 3], :]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\n\n\n2.4.3 Integer-based Extraction: Indexing with .iloc\nSlicing with .iloc works similarly to .loc. However, .iloc uses the index positions of rows and columns rather than the labels (think to yourself: loc uses lables; iloc uses indices). The arguments to the .iloc function also behave similarly — single values, lists, indices, and any combination of these are permitted.\nLet’s begin reproducing our results from above. We’ll begin by selecting the first presidential candidate in our elections DataFrame:\n\n# elections.loc[0, \"Candidate\"] - Previous approach\nelections.iloc[0, 1]\n\n'Andrew Jackson'\n\n\nNotice how the first argument to both .loc and .iloc are the same. This is because the row with a label of 0 is conveniently in the \\(0^{\\text{th}}\\) (equivalently, the first position) of the elections DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.\nAnd, as before, if we were to pass in only one single value argument, our result would be a Series.\n\nelections.iloc[[1,2,3],1]\n\n1    John Quincy Adams\n2       Andrew Jackson\n3    John Quincy Adams\nName: Candidate, dtype: object\n\n\nHowever, when we select the first four rows and columns using .iloc, we notice something.\n\n# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach\nelections.iloc[0:4, 0:4]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nSlicing is no longer inclusive in .iloc — it’s exclusive. In other words, the right end of a slice is not included when using .iloc. This is one of the subtleties of pandas syntax; you will get used to it with practice.\nList behavior works just as expected.\n\n#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach\nelections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nAnd just like with .loc, we can use a colon with .iloc to extract all rows or columns.\n\nelections.iloc[:, 0:3]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n\n\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n\n\n178\n2020\nJoseph Biden\nDemocratic\n\n\n179\n2020\nDonald Trump\nRepublican\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n\n\n181\n2020\nHoward Hawkins\nGreen\n\n\n\n\n182 rows × 3 columns\n\n\n\nThis discussion begs the question: when should we use .loc vs. .iloc? In most cases, .loc is generally safer to use. You can imagine .iloc may return incorrect values when applied to a dataset where the ordering of data can change. However, .iloc can still be useful — for example, if you are looking at a DataFrame of sorted movie earnings and want to get the median earnings for a given year, you can use .iloc to index into the middle.\nOverall, it is important to remember that:\n\n.loc performances label-based extraction.\n.iloc performs integer-based extraction.\n\n\n\n2.4.4 Context-dependent Extraction: Indexing with []\nThe [] selection operator is the most baffling of all, yet the most commonly used. It only takes a single argument, which may be one of the following:\n\nA slice of row numbers.\nA list of column labels.\nA single-column label.\n\nThat is, [] is context-dependent. Let’s see some examples.\n\n2.4.4.1 A slice of row numbers\nSay we wanted the first four rows of our elections DataFrame.\n\nelections[0:4]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\n\n\n2.4.4.2 A list of column labels\nSuppose we now want the first four columns.\n\nelections[[\"Year\", \"Candidate\", \"Party\", \"Popular vote\"]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\n\n\n...\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n1457226\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\n\n\n\n\n182 rows × 4 columns\n\n\n\n\n\n2.4.4.3 A single-column label\nLastly, [] allows us to extract only the \"Candidate\" column.\n\nelections[\"Candidate\"]\n\n0         Andrew Jackson\n1      John Quincy Adams\n2         Andrew Jackson\n3      John Quincy Adams\n4         Andrew Jackson\n             ...        \n177           Jill Stein\n178         Joseph Biden\n179         Donald Trump\n180         Jo Jorgensen\n181       Howard Hawkins\nName: Candidate, Length: 182, dtype: object\n\n\nThe output is a Series! In this course, we’ll become very comfortable with [], especially for selecting columns. In practice, [] is much more common than .loc, especially since it is far more concise.",
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+    "section": "2.5 Parting Note",
+    "text": "2.5 Parting Note\nThe pandas library is enormous and contains many useful functions. Here is a link to its documentation. We certainly don’t expect you to memorize each and every method of the library, and we will give you a reference sheet for exams.\nThe introductory Data 100 pandas lectures will provide a high-level view of the key data structures and methods that will form the foundation of your pandas knowledge. A goal of this course is to help you build your familiarity with the real-world programming practice of … Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. Being able to search for, read, and implement documentation is an important life skill for any data scientist.\nWith that, we will move on to Pandas II!",
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+    "text": "1.1 Data Science Lifecycle\nThe data science lifecycle is a high-level overview of the data science workflow. It’s a cycle of stages that a data scientist should explore as they conduct a thorough analysis of a data-driven problem.\nThere are many variations of the key ideas present in the data science lifecycle. In Data 100, we visualize the stages of the lifecycle using a flow diagram. Notice how there are two entry points.",
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+    "text": "1.1.1 Ask a Question\nWhether by curiosity or necessity, data scientists constantly ask questions. For example, in the business world, data scientists may be interested in predicting the profit generated by a certain investment. In the field of medicine, they may ask whether some patients are more likely than others to benefit from a treatment.\nPosing questions is one of the primary ways the data science lifecycle begins. It helps to fully define the question. Here are some things you should ask yourself before framing a question.\n\nWhat do we want to know?\n\nA question that is too ambiguous may lead to confusion.\n\nWhat problems are we trying to solve?\n\nThe goal of asking a question should be clear in order to justify your efforts to stakeholders.\n\nWhat are the hypotheses we want to test?\n\nThis gives a clear perspective from which to analyze final results.\n\nWhat are the metrics for our success?\n\nThis establishes a clear point to know when to conclude the project.\n\n\n\n\n\n\n\n1.1.2 Obtain Data\nThe second entry point to the lifecycle is by obtaining data. A careful analysis of any problem requires the use of data. Data may be readily available to us, or we may have to embark on a process to collect it. When doing so, it is crucial to ask the following:\n\nWhat data do we have, and what data do we need?\n\nDefine the units of the data (people, cities, points in time, etc.) and what features to measure.\n\nHow will we sample more data?\n\nScrape the web, collect manually, run experiments, etc.\n\nIs our data representative of the population we want to study?\n\nIf our data is not representative of our population of interest, then we can come to incorrect conclusions.\n\n\nKey procedures: data acquisition, data cleaning\n\n\n\n\n\n1.1.3 Understand the Data\nRaw data itself is not inherently useful. It’s impossible to discern all the patterns and relationships between variables without carefully investigating them. Therefore, translating pure data into actionable insights is a key job of a data scientist. For example, we may choose to ask:\n\nHow is our data organized, and what does it contain?\n\nKnowing what the data says about the world helps us better understand the world.\n\nDo we have relevant data?\n\nIf the data we have collected is not useful to the question at hand, then we must collect more data.\n\nWhat are the biases, anomalies, or other issues with the data?\n\nThese can lead to many false conclusions if ignored, so data scientists must always be aware of these issues.\n\nHow do we transform the data to enable effective analysis?\n\nData is not always easy to interpret at first glance, so a data scientist should strive to reveal the hidden insights.\n\n\nKey procedures: exploratory data analysis, data visualization.\n\n\n\n\n\n1.1.4 Understand the World\nAfter observing the patterns in our data, we can begin answering our questions. This may require that we predict a quantity (machine learning) or measure the effect of some treatment (inference).\nFrom here, we may choose to report our results, or possibly conduct more analysis. We may not be satisfied with our findings, or our initial exploration may have brought up new questions that require new data.\n\nWhat does the data say about the world?\n\nGiven our models, the data will lead us to certain conclusions about the real world.\n\n\nDoes it answer our questions or accurately solve the problem?\n\nIf our model and data can not accomplish our goals, then we must reform our question, model, or both.\n\n\nHow robust are our conclusions and can we trust the predictions?\n\nInaccurate models can lead to false conclusions.\n\n\nKey procedures: model creation, prediction, inference.",
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+    "text": "1.2 Conclusion\nThe data science lifecycle is meant to be a set of general guidelines rather than a hard set of requirements. In our journey exploring the lifecycle, we’ll cover both the underlying theory and technologies used in data science. By the end of the course, we hope that you start to see yourself as a data scientist.\nWith that, we’ll begin by introducing one of the most important tools in exploratory data analysis: pandas.",
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@@ -401,14 +401,17 @@ <h2 data-number="7.4" class="anchored" data-anchor-id="variable-types-should-inf
 <h2 data-number="7.5" class="anchored" data-anchor-id="qualitative-variables-bar-plots"><span class="header-section-number">7.5</span> Qualitative Variables: Bar Plots</h2>
 <p>A <strong>bar plot</strong> is one of the most common ways of displaying the <strong>distribution</strong> of a <strong>qualitative</strong> (categorical) variable. The length of a bar plot encodes the frequency of a category; the width encodes no useful information. The color <em>could</em> indicate a sub-category, but this is not necessarily the case.</p>
 <p>Let’s contextualize this in an example. We will use the World Bank dataset (<code>wb</code>) in our analysis.</p>
-<div id="c8f76b49" class="cell" data-execution_count="1">
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 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> pd.read_csv(<span class="st">"data/world_bank.csv"</span>, index_col<span class="op">=</span><span class="dv">0</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>wb.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> warnings </span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>warnings.filterwarnings(<span class="st">"ignore"</span>, <span class="st">"use_inf_as_na"</span>) <span class="co"># Supresses distracting deprecation warnings</span></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> pd.read_csv(<span class="st">"data/world_bank.csv"</span>, index_col<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>wb.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -572,7 +575,7 @@ <h2 data-number="7.5" class="anchored" data-anchor-id="qualitative-variables-bar
 <p>We can visualize the distribution of the <code>Continent</code> column using a bar plot. There are a few ways to do this.</p>
 <section id="plotting-in-pandas" class="level3" data-number="7.5.1">
 <h3 data-number="7.5.1" class="anchored" data-anchor-id="plotting-in-pandas"><span class="header-section-number">7.5.1</span> Plotting in Pandas</h3>
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 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>wb[<span class="st">'Continent'</span>].value_counts().plot(kind<span class="op">=</span><span class="st">'bar'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -587,7 +590,7 @@ <h3 data-number="7.5.1" class="anchored" data-anchor-id="plotting-in-pandas"><sp
 </section>
 <section id="plotting-in-matplotlib" class="level3" data-number="7.5.2">
 <h3 data-number="7.5.2" class="anchored" data-anchor-id="plotting-in-matplotlib"><span class="header-section-number">7.5.2</span> Plotting in Matplotlib</h3>
-<div id="129bb6dd" class="cell" data-execution_count="3">
+<div id="51988baa" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt <span class="co"># matplotlib is typically given the alias plt</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>continent <span class="op">=</span> wb[<span class="st">'Continent'</span>].value_counts()</span>
@@ -607,7 +610,7 @@ <h3 data-number="7.5.2" class="anchored" data-anchor-id="plotting-in-matplotlib"
 </section>
 <section id="plotting-in-seaborn" class="level3" data-number="7.5.3">
 <h3 data-number="7.5.3" class="anchored" data-anchor-id="plotting-in-seaborn"><span class="header-section-number">7.5.3</span> Plotting in <code>Seaborn</code></h3>
-<div id="7a0adeb4" class="cell" data-execution_count="4">
+<div id="76bc3611" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns <span class="co"># seaborn is typically given the alias sns</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>sns.countplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Continent'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -630,7 +633,7 @@ <h3 data-number="7.5.3" class="anchored" data-anchor-id="plotting-in-seaborn"><s
 <section id="distributions-of-quantitative-variables" class="level2" data-number="7.6">
 <h2 data-number="7.6" class="anchored" data-anchor-id="distributions-of-quantitative-variables"><span class="header-section-number">7.6</span> Distributions of Quantitative Variables</h2>
 <p>Revisiting our example with the <code>wb</code> DataFrame, let’s plot the distribution of <code>Gross national income per capita</code>.</p>
-<div id="6d5fe5d6" class="cell" data-execution_count="5">
+<div id="0e007ca4" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>wb.head(<span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -796,7 +799,7 @@ <h2 data-number="7.6" class="anchored" data-anchor-id="distributions-of-quantita
 </div>
 <p>How should we define our categories for this variable? In the previous example, these were a few unique values of the <code>Continent</code> column. If we use similar logic here, our categories are the different numerical values contained in the <code>Gross national income per capita</code> column.</p>
 <p>Under this assumption, let’s plot this distribution using the <code>seaborn.countplot</code> function.</p>
-<div id="24e73350" class="cell" data-execution_count="6">
+<div id="59db197e" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>sns.countplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
@@ -818,7 +821,7 @@ <h2 data-number="7.6" class="anchored" data-anchor-id="distributions-of-quantita
 <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-plots"><span class="header-section-number">7.6.1</span> Box Plots and Violin Plots</h3>
 <p>Box plots and violin plots are two very similar kinds of visualizations. Both display the distribution of a variable using information about <strong>quartiles</strong>.</p>
 <p>In a box plot, the width of the box at any point does not encode meaning. In a violin plot, the width of the plot indicates the density of the distribution at each possible value.</p>
-<div id="b954b631" class="cell" data-execution_count="7">
+<div id="83a616d6" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>sns.boxplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
@@ -828,7 +831,7 @@ <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-pl
 </div>
 </div>
 </div>
-<div id="4d0aeb4f" class="cell" data-execution_count="8">
+<div id="79111e0b" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>sns.violinplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
@@ -845,7 +848,7 @@ <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-pl
 <li>The third quartile (Q3) represents the 75th percentile – 75% of the data is smaller than or equal to the third quartile.</li>
 </ul>
 <p>This means that the middle 50% of the data lies between the first and third quartiles. This is demonstrated in the histogram below. The three quartiles are marked with red vertical bars.</p>
-<div id="46f6d09a" class="cell" data-execution_count="9">
+<div id="21195e75" class="cell" data-execution_count="9">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>gdp <span class="op">=</span> wb[<span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>]</span>
@@ -861,27 +864,17 @@ <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-pl
 <span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a>sns.histplot(wb_quartiles, x<span class="op">=</span><span class="st">"Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016"</span>, hue<span class="op">=</span><span class="st">"category"</span>)</span>
 <span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>sns.rugplot([q1, q2, q3], c<span class="op">=</span><span class="st">"firebrick"</span>, lw<span class="op">=</span><span class="dv">6</span>, height<span class="op">=</span><span class="fl">0.1</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-10-output-2.png" width="585" height="429" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-10-output-1.png" width="585" height="429" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>In a box plot, the lower extent of the box lies at Q1, while the upper extent of the box lies at Q3. The horizontal line in the middle of the box corresponds to Q2 (equivalently, the median).</p>
-<div id="0e21301d" class="cell" data-execution_count="10">
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>sns.boxplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="6a859c7d" class="cell" data-execution_count="10">
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>sns.boxplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
@@ -896,8 +889,8 @@ <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-pl
 <img src="images/box_plot_diagram.png" width="600">
 </center>
 <p>A violin plot displays quartile information, albeit a bit more subtly through smoothed density curves. Look closely at the center vertical bar of the violin plot below; the three quartiles and “whiskers” are still present!</p>
-<div id="97b635bd" class="cell" data-execution_count="11">
-<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>sns.violinplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="6602d5ec" class="cell" data-execution_count="11">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>sns.violinplot(data<span class="op">=</span>wb, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
@@ -911,8 +904,8 @@ <h3 data-number="7.6.1" class="anchored" data-anchor-id="box-plots-and-violin-pl
 <h3 data-number="7.6.2" class="anchored" data-anchor-id="side-by-side-box-and-violin-plots"><span class="header-section-number">7.6.2</span> Side-by-Side Box and Violin Plots</h3>
 <p>Plotting side-by-side box or violin plots allows us to compare distributions across different categories. In other words, they enable us to plot both a qualitative variable and a quantitative continuous variable in one visualization.</p>
 <p>With <code>seaborn</code>, we can easily create side-by-side plots by specifying both an x and y column.</p>
-<div id="2aa902a8" class="cell" data-execution_count="12">
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>sns.boxplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Continent"</span>, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="70f3e0b5" class="cell" data-execution_count="12">
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>sns.boxplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Continent"</span>, y<span class="op">=</span><span class="st">'Gross domestic product: </span><span class="sc">% g</span><span class="st">rowth : 2016'</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
@@ -928,15 +921,15 @@ <h3 data-number="7.6.3" class="anchored" data-anchor-id="histograms"><span class
 <section id="plotting-histograms" class="level4" data-number="7.6.3.1">
 <h4 data-number="7.6.3.1" class="anchored" data-anchor-id="plotting-histograms"><span class="header-section-number">7.6.3.1</span> Plotting Histograms</h4>
 <p>Below, we plot a histogram using matplotlib and seaborn. Which graph do you prefer?</p>
-<div id="77949ce0" class="cell" data-execution_count="13">
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The `edgecolor` argument controls the color of the bin edges</span></span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>gni <span class="op">=</span> wb[<span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>]</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>plt.hist(gni, density<span class="op">=</span><span class="va">True</span>, edgecolor<span class="op">=</span><span class="st">"white"</span>)</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Add labels</span></span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
-<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="94be05d5" class="cell" data-execution_count="13">
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The `edgecolor` argument controls the color of the bin edges</span></span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>gni <span class="op">=</span> wb[<span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>]</span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>plt.hist(gni, density<span class="op">=</span><span class="va">True</span>, edgecolor<span class="op">=</span><span class="st">"white"</span>)</span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Add labels</span></span>
+<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
+<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
@@ -945,19 +938,13 @@ <h4 data-number="7.6.3.1" class="anchored" data-anchor-id="plotting-histograms">
 </div>
 </div>
 </div>
-<div id="7c5cf664" class="cell" data-execution_count="14">
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>, stat<span class="op">=</span><span class="st">"density"</span>)</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="9abf19a2" class="cell" data-execution_count="14">
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>, stat<span class="op">=</span><span class="st">"density"</span>)</span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-15-output-2.png" width="623" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-15-output-1.png" width="623" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -968,38 +955,32 @@ <h4 data-number="7.6.3.2" class="anchored" data-anchor-id="overlaid-histograms">
 <p>We can overlay histograms (or density curves) to compare distributions across qualitative categories.</p>
 <p>The <code>hue</code> parameter of <code>sns.histplot</code> specifies the column that should be used to determine the color of each category. <code>hue</code> can be used in many <code>seaborn</code> plotting functions.</p>
 <p>Notice that the resulting plot includes a legend describing which color corresponds to each hemisphere – a legend should always be included if color is used to encode information in a visualization!</p>
-<div id="0d243c8b" class="cell" data-execution_count="15">
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a new variable to store the hemisphere in which each country is located</span></span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>north <span class="op">=</span> [<span class="st">"Asia"</span>, <span class="st">"Europe"</span>, <span class="st">"N. America"</span>]</span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>south <span class="op">=</span> [<span class="st">"Africa"</span>, <span class="st">"Oceania"</span>, <span class="st">"S. America"</span>]</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>wb.loc[wb[<span class="st">"Continent"</span>].isin(north), <span class="st">"Hemisphere"</span>] <span class="op">=</span> <span class="st">"Northern"</span></span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>wb.loc[wb[<span class="st">"Continent"</span>].isin(south), <span class="st">"Hemisphere"</span>] <span class="op">=</span> <span class="st">"Southern"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div>
-<div id="8da409a5" class="cell" data-execution_count="16">
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>, hue<span class="op">=</span><span class="st">"Hemisphere"</span>, stat<span class="op">=</span><span class="st">"density"</span>)</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="1ff93216" class="cell" data-execution_count="15">
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a new variable to store the hemisphere in which each country is located</span></span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>north <span class="op">=</span> [<span class="st">"Asia"</span>, <span class="st">"Europe"</span>, <span class="st">"N. America"</span>]</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>south <span class="op">=</span> [<span class="st">"Africa"</span>, <span class="st">"Oceania"</span>, <span class="st">"S. America"</span>]</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>wb.loc[wb[<span class="st">"Continent"</span>].isin(north), <span class="st">"Hemisphere"</span>] <span class="op">=</span> <span class="st">"Northern"</span></span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>wb.loc[wb[<span class="st">"Continent"</span>].isin(south), <span class="st">"Hemisphere"</span>] <span class="op">=</span> <span class="st">"Southern"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div id="ce72338d" class="cell" data-execution_count="16">
+<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"Gross national income per capita, Atlas method: $: 2016"</span>, hue<span class="op">=</span><span class="st">"Hemisphere"</span>, stat<span class="op">=</span><span class="st">"density"</span>)</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-17-output-2.png" width="576" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-17-output-1.png" width="576" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Again, each bin of a histogram is scaled such that its <strong>area</strong> is proportional to the <strong>percentage</strong> of all datapoints that it contains.</p>
-<div id="01d950ea" class="cell" data-execution_count="17">
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>densities, bins, _ <span class="op">=</span> plt.hist(gni, density<span class="op">=</span><span class="va">True</span>, edgecolor<span class="op">=</span><span class="st">"white"</span>, bins<span class="op">=</span><span class="dv">5</span>)</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"First bin has width </span><span class="sc">{</span>bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> and height </span><span class="sc">{</span>densities[<span class="dv">0</span>]<span class="sc">}</span><span class="ss">"</span>)</span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"This corresponds to </span><span class="sc">{</span>bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> * </span><span class="sc">{</span>densities[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> = </span><span class="sc">{</span>(bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>])<span class="op">*</span>densities[<span class="dv">0</span>]<span class="op">*</span><span class="dv">100</span><span class="sc">}</span><span class="ss">% of the data"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="e14dcb75" class="cell" data-execution_count="17">
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>densities, bins, _ <span class="op">=</span> plt.hist(gni, density<span class="op">=</span><span class="va">True</span>, edgecolor<span class="op">=</span><span class="st">"white"</span>, bins<span class="op">=</span><span class="dv">5</span>)</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"First bin has width </span><span class="sc">{</span>bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> and height </span><span class="sc">{</span>densities[<span class="dv">0</span>]<span class="sc">}</span><span class="ss">"</span>)</span>
+<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"This corresponds to </span><span class="sc">{</span>bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> * </span><span class="sc">{</span>densities[<span class="dv">0</span>]<span class="sc">}</span><span class="ss"> = </span><span class="sc">{</span>(bins[<span class="dv">1</span>]<span class="op">-</span>bins[<span class="dv">0</span>])<span class="op">*</span>densities[<span class="dv">0</span>]<span class="op">*</span><span class="dv">100</span><span class="sc">}</span><span class="ss">% of the data"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>First bin has width 16410.0 and height 4.7741589911386953e-05
 This corresponds to 16410.0 * 4.7741589911386953e-05 = 78.343949044586% of the data</code></pre>
@@ -1034,22 +1015,16 @@ <h4 data-number="7.6.3.3" class="anchored" data-anchor-id="evaluating-histograms
 <section id="skewness-and-tails" class="level5" data-number="7.6.3.3.1">
 <h5 data-number="7.6.3.3.1" class="anchored" data-anchor-id="skewness-and-tails"><span class="header-section-number">7.6.3.3.1</span> Skewness and Tails</h5>
 <p>The skew of a histogram describes the direction in which its “tail” extends. - A distribution with a long right tail is <strong>skewed right</strong> (such as <code>Gross national income per capita</code>). In a right-skewed distribution, the few large outliers “pull” the mean to the <strong>right</strong> of the median.</p>
-<div id="e351204b" class="cell" data-execution_count="18">
-<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>, stat <span class="op">=</span> <span class="st">'density'</span>)<span class="op">;</span></span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">'Distribution with a long right tail'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="b135f2ed" class="cell" data-execution_count="18">
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>, stat <span class="op">=</span> <span class="st">'density'</span>)<span class="op">;</span></span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">'Distribution with a long right tail'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
 <pre><code>Text(0.5, 1.0, 'Distribution with a long right tail')</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-19-output-3.png" width="623" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-19-output-2.png" width="623" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1058,22 +1033,16 @@ <h5 data-number="7.6.3.3.1" class="anchored" data-anchor-id="skewness-and-tails"
 <li>A distribution with a long left tail is <strong>skewed left</strong> (such as <code>Access to an improved water source</code>). In a left-skewed distribution, the few small outliers “pull” the mean to the <strong>left</strong> of the median.</li>
 </ul>
 <p>In the case where a distribution has equal-sized right and left tails, it is <strong>symmetric</strong>. The mean is approximately <strong>equal</strong> to the median. Think of mean as the balancing point of the distribution.</p>
-<div id="a539c3d1" class="cell" data-execution_count="19">
-<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Access to an improved water source: </span><span class="sc">% o</span><span class="st">f population: 2015'</span>, stat <span class="op">=</span> <span class="st">'density'</span>)<span class="op">;</span></span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">'Distribution with a long left tail'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="85e5011a" class="cell" data-execution_count="19">
+<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>sns.histplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">'Access to an improved water source: </span><span class="sc">% o</span><span class="st">f population: 2015'</span>, stat <span class="op">=</span> <span class="st">'density'</span>)<span class="op">;</span></span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">'Distribution with a long left tail'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="19">
 <pre><code>Text(0.5, 1.0, 'Distribution with a long left tail')</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-20-output-3.png" width="597" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-20-output-2.png" width="597" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1087,60 +1056,42 @@ <h5 data-number="7.6.3.3.2" class="anchored" data-anchor-id="outliers"><span cla
 <h5 data-number="7.6.3.3.3" class="anchored" data-anchor-id="modes"><span class="header-section-number">7.6.3.3.3</span> Modes</h5>
 <p>In Data 100, we describe a “mode” of a histogram as a peak in the distribution. Often, however, it is difficult to determine what counts as its own “peak.” For example, the number of peaks in the distribution of HIV rates across different countries varies depending on the number of histogram bins we plot.</p>
 <p>If we set the number of bins to 5, the distribution appears unimodal.</p>
-<div id="c0062e82" class="cell" data-execution_count="20">
-<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename the very long column name for convenience</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> wb.rename(columns<span class="op">=</span>{<span class="st">'Antiretroviral therapy coverage: </span><span class="sc">% o</span><span class="st">f people living with HIV: 2015'</span>:<span class="st">"HIV rate"</span>})</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a><span class="co"># With 5 bins, it seems that there is only one peak</span></span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">5</span>)</span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"5 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="b8aa7889" class="cell" data-execution_count="20">
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename the very long column name for convenience</span></span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> wb.rename(columns<span class="op">=</span>{<span class="st">'Antiretroviral therapy coverage: </span><span class="sc">% o</span><span class="st">f people living with HIV: 2015'</span>:<span class="st">"HIV rate"</span>})</span>
+<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a><span class="co"># With 5 bins, it seems that there is only one peak</span></span>
+<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">5</span>)</span>
+<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"5 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-21-output-2.png" width="614" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-21-output-1.png" width="614" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
-<div id="22058b31" class="cell" data-execution_count="21">
-<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># With 10 bins, there seem to be two peaks</span></span>
-<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">10</span>)</span>
-<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"10 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="a15d171c" class="cell" data-execution_count="21">
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># With 10 bins, there seem to be two peaks</span></span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x<span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">10</span>)</span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"10 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-22-output-2.png" width="606" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-22-output-1.png" width="606" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
-<div id="883d65e4" class="cell" data-execution_count="22">
-<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># And with 20 bins, it becomes hard to say what counts as a "peak"!</span></span>
-<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x <span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">20</span>)</span>
-<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"20 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="4d774efc" class="cell" data-execution_count="22">
+<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># And with 20 bins, it becomes hard to say what counts as a "peak"!</span></span>
+<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>sns.histplot(data<span class="op">=</span>wb, x <span class="op">=</span><span class="st">"HIV rate"</span>, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">20</span>)</span>
+<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"20 histogram bins"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_1_files/figure-html/cell-23-output-2.png" width="606" height="449" class="figure-img"></p>
+<p><img src="visualization_1_files/figure-html/cell-23-output-1.png" width="606" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
diff --git a/docs/visualization_1/visualization_1_files/figure-html/cell-10-output-1.png b/docs/visualization_1/visualization_1_files/figure-html/cell-10-output-1.png
new file mode 100644
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diff --git a/docs/visualization_2/visualization_2.html b/docs/visualization_2/visualization_2.html
index a953a36b..45ed0656 100644
--- a/docs/visualization_2/visualization_2.html
+++ b/docs/visualization_2/visualization_2.html
@@ -348,18 +348,21 @@ <h3 data-number="8.1.1" class="anchored" data-anchor-id="kde-theory"><span class
 <p>A <strong>kernel density estimate (KDE)</strong> is a smooth, continuous function that approximates a curve. It allows us to represent general trends in a distribution without focusing on the details, which is useful for analyzing the broad structure of a dataset.</p>
 <p>More formally, a KDE attempts to approximate the underlying <strong>probability distribution</strong> from which our dataset was drawn. You may have encountered the idea of a probability distribution in your other classes; if not, we’ll discuss it at length in the next lecture. For now, you can think of a probability distribution as a description of how likely it is for us to sample a particular value in our dataset.</p>
 <p>A KDE curve estimates the probability density function of a random variable. Consider the example below, where we have used <code>sns.displot</code> to plot both a histogram (containing the data points we actually collected) and a KDE curve (representing the <em>approximated</em> probability distribution from which this data was drawn) using data from the World Bank dataset (<code>wb</code>).</p>
-<div id="78cc81dc" class="cell" data-execution_count="1">
+<div id="fc90308c" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> pd.read_csv(<span class="st">"data/world_bank.csv"</span>, index_col<span class="op">=</span><span class="dv">0</span>)</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> wb.rename(columns<span class="op">=</span>{<span class="st">'Antiretroviral therapy coverage: </span><span class="sc">% o</span><span class="st">f people living with HIV: 2015'</span>:<span class="st">"HIV rate"</span>,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>:<span class="st">'gni'</span>})</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>wb.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> warnings </span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>warnings.filterwarnings(<span class="st">"ignore"</span>, <span class="st">"use_inf_as_na"</span>) <span class="co"># Supresses distracting deprecation warnings</span></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> pd.read_csv(<span class="st">"data/world_bank.csv"</span>, index_col<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>wb <span class="op">=</span> wb.rename(columns<span class="op">=</span>{<span class="st">'Antiretroviral therapy coverage: </span><span class="sc">% o</span><span class="st">f people living with HIV: 2015'</span>:<span class="st">"HIV rate"</span>,</span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'Gross national income per capita, Atlas method: $: 2016'</span>:<span class="st">'gni'</span>})</span>
+<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>wb.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display" data-execution_count="1">
 <div>
@@ -520,7 +523,7 @@ <h3 data-number="8.1.1" class="anchored" data-anchor-id="kde-theory"><span class
 </div>
 </div>
 </div>
-<div id="be8cbc40" class="cell" data-execution_count="2">
+<div id="def7dda3" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -528,16 +531,10 @@ <h3 data-number="8.1.1" class="anchored" data-anchor-id="kde-theory"><span class
 <span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>                       kde <span class="op">=</span> <span class="va">True</span>, stat <span class="op">=</span> <span class="st">"density"</span>)</span>
 <span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of HIV rates"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-3-output-2.png" width="469" height="488" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-3-output-1.png" width="469" height="488" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -555,52 +552,40 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="constructing-a-kde"><sp
 </ol>
 <p>We’ll explain what a “kernel” is momentarily.</p>
 <p>To make things simpler, let’s construct a KDE for a small, artificially generated dataset of 5 datapoints: <span class="math inline">\([2.2, 2.8, 3.7, 5.3, 5.7]\)</span>. In the plot below, each vertical bar represents one data point.</p>
-<div id="45df044f" class="cell" data-execution_count="3">
+<div id="8de2d43b" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> [<span class="fl">2.2</span>, <span class="fl">2.8</span>, <span class="fl">3.7</span>, <span class="fl">5.3</span>, <span class="fl">5.7</span>]</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>sns.rugplot(data, height<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> [<span class="fl">2.2</span>, <span class="fl">2.8</span>, <span class="fl">3.7</span>, <span class="fl">5.3</span>, <span class="fl">5.7</span>]</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>sns.rugplot(data, height<span class="op">=</span><span class="fl">0.3</span>)</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-4-output-2.png" width="597" height="434" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-4-output-1.png" width="597" height="434" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Our goal is to create the following KDE curve, which was generated automatically by <code>sns.kdeplot</code>.</p>
-<div id="56f8230e" class="cell" data-execution_count="4">
+<div id="d1c321d1" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>sns.kdeplot(data)</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>sns.kdeplot(data)</span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-5-output-2.png" width="597" height="434" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-5-output-1.png" width="597" height="434" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -611,78 +596,72 @@ <h4 data-number="8.1.2.1" class="anchored" data-anchor-id="step-1-place-a-kernel
 <p>A <strong>kernel</strong> is a density curve. It is the mathematical function that attempts to capture the randomness of each data point in our sampled data. To explain what this means, consider just <em>one</em> of the datapoints in our dataset: <span class="math inline">\(2.2\)</span>. We obtained this datapoint by randomly sampling some information out in the real world (you can imagine <span class="math inline">\(2.2\)</span> as representing a single measurement taken in an experiment, for example). If we were to sample a new datapoint, we may obtain a slightly different value. It could be higher than <span class="math inline">\(2.2\)</span>; it could also be lower than <span class="math inline">\(2.2\)</span>. We make the assumption that any future sampled datapoints will likely be similar in value to the data we’ve already drawn. This means that our <em>kernel</em> – our description of the probability of randomly sampling any new value – will be greatest at the datapoint we’ve already drawn but still have non-zero probability above and below it. The area under any kernel should integrate to 1, representing the total probability of drawing a new datapoint.</p>
 <p>A <strong>bandwidth value</strong>, usually denoted by <span class="math inline">\(\alpha\)</span>, represents the width of the kernel. A large value of <span class="math inline">\(\alpha\)</span> will result in a wide, short kernel function, while a small value with result in a narrow, tall kernel.</p>
 <p>Below, we place a <strong>Gaussian kernel</strong>, plotted in orange, over the datapoint <span class="math inline">\(2.2\)</span>. A Gaussian kernel is simply the normal distribution, which you may have called a bell curve in Data 8.</p>
-<div id="38bc9655" class="cell" data-execution_count="5">
+<div id="1d748f44" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> gaussian_kernel(x, z, a):</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    <span class="co"># We'll discuss where this mathematical formulation came from later</span></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (<span class="dv">1</span><span class="op">/</span>np.sqrt(<span class="dv">2</span><span class="op">*</span>np.pi<span class="op">*</span>a<span class="op">**</span><span class="dv">2</span>)) <span class="op">*</span> np.exp((<span class="op">-</span>(x <span class="op">-</span> z)<span class="op">**</span><span class="dv">2</span> <span class="op">/</span> (<span class="dv">2</span> <span class="op">*</span> a<span class="op">**</span><span class="dv">2</span>)))</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot our datapoint</span></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>sns.rugplot([<span class="fl">2.2</span>], height<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the kernel</span></span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>x <span class="op">=</span> np.linspace(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>, <span class="dv">1000</span>)</span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>plt.plot(x, gaussian_kernel(x, <span class="fl">2.2</span>, <span class="dv">1</span>))</span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> gaussian_kernel(x, z, a):</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    <span class="co"># We'll discuss where this mathematical formulation came from later</span></span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (<span class="dv">1</span><span class="op">/</span>np.sqrt(<span class="dv">2</span><span class="op">*</span>np.pi<span class="op">*</span>a<span class="op">**</span><span class="dv">2</span>)) <span class="op">*</span> np.exp((<span class="op">-</span>(x <span class="op">-</span> z)<span class="op">**</span><span class="dv">2</span> <span class="op">/</span> (<span class="dv">2</span> <span class="op">*</span> a<span class="op">**</span><span class="dv">2</span>)))</span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot our datapoint</span></span>
+<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>sns.rugplot([<span class="fl">2.2</span>], height<span class="op">=</span><span class="fl">0.3</span>)</span>
+<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the kernel</span></span>
+<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>x <span class="op">=</span> np.linspace(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>, <span class="dv">1000</span>)</span>
+<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>plt.plot(x, gaussian_kernel(x, <span class="fl">2.2</span>, <span class="dv">1</span>))</span>
+<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-6-output-2.png" width="597" height="434" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-6-output-1.png" width="597" height="434" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>To begin creating our KDE, we place a kernel on <em>each</em> datapoint in our dataset. For our dataset of 5 points, we will have 5 kernels.</p>
-<div id="180decc2" class="cell" data-execution_count="6">
+<div id="fce2ec32" class="cell" data-execution_count="6">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You will work with the functions below in Lab 4</span></span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_kde(kernel, pts, a):</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Takes in a kernel, set of points, and alpha</span></span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Returns the KDE as a function</span></span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> f(x):</span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>        output <span class="op">=</span> <span class="dv">0</span></span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>        <span class="cf">for</span> pt <span class="kw">in</span> pts:</span>
-<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>            output <span class="op">+=</span> kernel(x, pt, a)</span>
-<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> output <span class="op">/</span> <span class="bu">len</span>(pts) <span class="co"># Normalization factor</span></span>
-<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> f</span>
-<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_kde(kernel, pts, a):</span>
-<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Calls create_kde and plots the corresponding KDE</span></span>
-<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a>    f <span class="op">=</span> create_kde(kernel, pts, a)</span>
-<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> np.linspace(<span class="bu">min</span>(pts) <span class="op">-</span> <span class="dv">5</span>, <span class="bu">max</span>(pts) <span class="op">+</span> <span class="dv">5</span>, <span class="dv">1000</span>)</span>
-<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> [f(xi) <span class="cf">for</span> xi <span class="kw">in</span> x]</span>
-<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a>    plt.plot(x, y)<span class="op">;</span></span>
-<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_separate_kernels(kernel, pts, a, norm<span class="op">=</span><span class="va">False</span>):</span>
-<span id="cb10-20"><a href="#cb10-20" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Plots individual kernels, which are then summed to create the KDE</span></span>
-<span id="cb10-21"><a href="#cb10-21" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> np.linspace(<span class="bu">min</span>(pts) <span class="op">-</span> <span class="dv">5</span>, <span class="bu">max</span>(pts) <span class="op">+</span> <span class="dv">5</span>, <span class="dv">1000</span>)</span>
-<span id="cb10-22"><a href="#cb10-22" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> pt <span class="kw">in</span> pts:</span>
-<span id="cb10-23"><a href="#cb10-23" aria-hidden="true" tabindex="-1"></a>        y <span class="op">=</span> kernel(x, pt, a)</span>
-<span id="cb10-24"><a href="#cb10-24" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> norm:</span>
-<span id="cb10-25"><a href="#cb10-25" aria-hidden="true" tabindex="-1"></a>            y <span class="op">/=</span> <span class="bu">len</span>(pts)</span>
-<span id="cb10-26"><a href="#cb10-26" aria-hidden="true" tabindex="-1"></a>        plt.plot(x, y)</span>
-<span id="cb10-27"><a href="#cb10-27" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb10-28"><a href="#cb10-28" aria-hidden="true" tabindex="-1"></a>    plt.show()<span class="op">;</span></span>
-<span id="cb10-29"><a href="#cb10-29" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb10-30"><a href="#cb10-30" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb10-31"><a href="#cb10-31" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
-<span id="cb10-32"><a href="#cb10-32" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb10-33"><a href="#cb10-33" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb10-34"><a href="#cb10-34" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-35"><a href="#cb10-35" aria-hidden="true" tabindex="-1"></a>plot_separate_kernels(gaussian_kernel, data, a <span class="op">=</span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># You will work with the functions below in Lab 4</span></span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_kde(kernel, pts, a):</span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Takes in a kernel, set of points, and alpha</span></span>
+<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Returns the KDE as a function</span></span>
+<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> f(x):</span>
+<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>        output <span class="op">=</span> <span class="dv">0</span></span>
+<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>        <span class="cf">for</span> pt <span class="kw">in</span> pts:</span>
+<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>            output <span class="op">+=</span> kernel(x, pt, a)</span>
+<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> output <span class="op">/</span> <span class="bu">len</span>(pts) <span class="co"># Normalization factor</span></span>
+<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> f</span>
+<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_kde(kernel, pts, a):</span>
+<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Calls create_kde and plots the corresponding KDE</span></span>
+<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>    f <span class="op">=</span> create_kde(kernel, pts, a)</span>
+<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> np.linspace(<span class="bu">min</span>(pts) <span class="op">-</span> <span class="dv">5</span>, <span class="bu">max</span>(pts) <span class="op">+</span> <span class="dv">5</span>, <span class="dv">1000</span>)</span>
+<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> [f(xi) <span class="cf">for</span> xi <span class="kw">in</span> x]</span>
+<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a>    plt.plot(x, y)<span class="op">;</span></span>
+<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> plot_separate_kernels(kernel, pts, a, norm<span class="op">=</span><span class="va">False</span>):</span>
+<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Plots individual kernels, which are then summed to create the KDE</span></span>
+<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> np.linspace(<span class="bu">min</span>(pts) <span class="op">-</span> <span class="dv">5</span>, <span class="bu">max</span>(pts) <span class="op">+</span> <span class="dv">5</span>, <span class="dv">1000</span>)</span>
+<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> pt <span class="kw">in</span> pts:</span>
+<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a>        y <span class="op">=</span> kernel(x, pt, a)</span>
+<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> norm:</span>
+<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a>            y <span class="op">/=</span> <span class="bu">len</span>(pts)</span>
+<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a>        plt.plot(x, y)</span>
+<span id="cb6-27"><a href="#cb6-27" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a>    plt.show()<span class="op">;</span></span>
+<span id="cb6-29"><a href="#cb6-29" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb6-30"><a href="#cb6-30" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb6-31"><a href="#cb6-31" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
+<span id="cb6-32"><a href="#cb6-32" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb6-33"><a href="#cb6-33" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb6-34"><a href="#cb6-34" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb6-35"><a href="#cb6-35" aria-hidden="true" tabindex="-1"></a>plot_separate_kernels(gaussian_kernel, data, a <span class="op">=</span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div>
@@ -697,16 +676,16 @@ <h4 data-number="8.1.2.1" class="anchored" data-anchor-id="step-1-place-a-kernel
 <h4 data-number="8.1.2.2" class="anchored" data-anchor-id="step-2-normalize-kernels-to-have-a-total-area-of-1"><span class="header-section-number">8.1.2.2</span> Step 2: Normalize Kernels to Have a Total Area of 1</h4>
 <p>Above, we said that <em>each</em> kernel has an area of 1. Earlier, we also said that our goal is to construct a KDE curve using these kernels with a <em>total</em> area of 1. If we were to directly sum the kernels as they are, we would produce a KDE curve with an integrated area of (5 kernels) <span class="math inline">\(\times\)</span> (area of 1 each) = 5. To avoid this, we will <strong>normalize</strong> each of our kernels. This involves multiplying each kernel by <span class="math inline">\(\frac{1}{\#\:\text{datapoints}}\)</span>.</p>
 <p>In the cell below, we multiply each of our 5 kernels by <span class="math inline">\(\frac{1}{5}\)</span> to apply normalization.</p>
-<div id="2f52f9f9" class="cell" data-execution_count="7">
+<div id="9ddf4528" class="cell" data-execution_count="7">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a><span class="co"># The `norm` argument specifies whether or not to normalize the kernels</span></span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>plot_separate_kernels(gaussian_kernel, data, a <span class="op">=</span> <span class="dv">1</span>, norm <span class="op">=</span> <span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="co"># The `norm` argument specifies whether or not to normalize the kernels</span></span>
+<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>plot_separate_kernels(gaussian_kernel, data, a <span class="op">=</span> <span class="dv">1</span>, norm <span class="op">=</span> <span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div>
@@ -720,15 +699,16 @@ <h4 data-number="8.1.2.2" class="anchored" data-anchor-id="step-2-normalize-kern
 <section id="step-3-sum-the-normalized-kernels" class="level4" data-number="8.1.2.3">
 <h4 data-number="8.1.2.3" class="anchored" data-anchor-id="step-3-sum-the-normalized-kernels"><span class="header-section-number">8.1.2.3</span> Step 3: Sum the Normalized Kernels</h4>
 <p>Our KDE curve is the sum of the normalized kernels. Notice that the final curve is identical to the plot generated by <code>sns.kdeplot</code> we saw earlier!</p>
-<div id="e0ea61ea" class="cell" data-execution_count="8">
+<div id="4ab3c3bb" class="cell" data-execution_count="8">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>plot_kde(gaussian_kernel, data, a <span class="op">=</span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>sns.kdeplot(data, bw_method<span class="op">=</span><span class="fl">0.65</span>)</span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>sns.histplot(data, stat<span class="op">=</span><span class="st">"density"</span>, bins<span class="op">=</span><span class="dv">2</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div>
@@ -738,6 +718,25 @@ <h4 data-number="8.1.2.3" class="anchored" data-anchor-id="step-3-sum-the-normal
 </div>
 </div>
 </div>
+<p>An alternative method to generate the above KDE is shown below, this time using <code>sns.histplot</code>’s arguments.</p>
+<div id="654182cf" class="cell" data-execution_count="9">
+<details class="code-fold">
+<summary>Code</summary>
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>plt.xlim(<span class="op">-</span><span class="dv">3</span>, <span class="dv">10</span>)</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>plt.ylim(<span class="dv">0</span>, <span class="fl">0.5</span>)</span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Data"</span>)</span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Density"</span>)</span>
+<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>sns.histplot(data, bins<span class="op">=</span><span class="dv">2</span>, kde<span class="op">=</span><span class="va">True</span>, stat<span class="op">=</span><span class="st">"density"</span>, kde_kws<span class="op">=</span><span class="bu">dict</span>(cut<span class="op">=</span><span class="dv">3</span>, bw_method<span class="op">=</span><span class="fl">0.65</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</details>
+<div class="cell-output cell-output-display">
+<div>
+<figure class="figure">
+<p><img src="visualization_2_files/figure-html/cell-10-output-1.png" width="597" height="434" class="figure-img"></p>
+</figure>
+</div>
+</div>
+</div>
 </section>
 </section>
 <section id="kernel-functions-and-bandwidths" class="level3" data-number="8.1.3">
@@ -811,13 +810,13 @@ <h4 data-number="8.1.3.1" class="anchored" data-anchor-id="gaussian-kernel"><spa
 <thead>
 <tr class="header">
 <th>Gaussian Kernel, <span class="math inline">\(\alpha\)</span> = 2</th>
-<th>Gaussian Kernel, <span class="math inline">\(\alpha\)</span> = 10</th>
+<th>Gaussian Kernel, <span class="math inline">\(\alpha\)</span> = 5</th>
 </tr>
 </thead>
 <tbody>
 <tr class="odd">
 <td><img src="images/gaussian_2.png" alt="gaussian_2" width="345"></td>
-<td><img src="images/gaussian_10.png" alt="gaussian_10" width="345"></td>
+<td><img src="images/gaussian_5.png" alt="gaussian_5" width="345"></td>
 </tr>
 </tbody>
 </table>
@@ -830,21 +829,21 @@ <h4 data-number="8.1.3.2" class="anchored" data-anchor-id="boxcar-kernel"><span
         0, &amp; \text{else }
     \end{cases}\]</span></p>
 <p>The boxcar kernel is seldom used in practice – we include it here to demonstrate that a kernel function can take whatever form you would like, provided it integrates to 1 and does not output negative values.</p>
-<div id="6dd4b744" class="cell" data-execution_count="9">
+<div id="cd1d9e5f" class="cell" data-execution_count="10">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> boxcar_kernel(alpha, x, z):</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (((x<span class="op">-</span>z)<span class="op">&gt;=-</span>alpha<span class="op">/</span><span class="dv">2</span>)<span class="op">&amp;</span>((x<span class="op">-</span>z)<span class="op">&lt;=</span>alpha<span class="op">/</span><span class="dv">2</span>))<span class="op">/</span>alpha</span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>xs <span class="op">=</span> np.linspace(<span class="op">-</span><span class="dv">5</span>, <span class="dv">5</span>, <span class="dv">200</span>)</span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>alpha<span class="op">=</span><span class="dv">1</span></span>
-<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>kde_curve <span class="op">=</span> [boxcar_kernel(alpha, x, <span class="dv">0</span>) <span class="cf">for</span> x <span class="kw">in</span> xs]</span>
-<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>plt.plot(xs, kde_curve)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> boxcar_kernel(alpha, x, z):</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (((x<span class="op">-</span>z)<span class="op">&gt;=-</span>alpha<span class="op">/</span><span class="dv">2</span>)<span class="op">&amp;</span>((x<span class="op">-</span>z)<span class="op">&lt;=</span>alpha<span class="op">/</span><span class="dv">2</span>))<span class="op">/</span>alpha</span>
+<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>xs <span class="op">=</span> np.linspace(<span class="op">-</span><span class="dv">5</span>, <span class="dv">5</span>, <span class="dv">200</span>)</span>
+<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>alpha<span class="op">=</span><span class="dv">1</span></span>
+<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>kde_curve <span class="op">=</span> [boxcar_kernel(alpha, x, <span class="dv">0</span>) <span class="cf">for</span> x <span class="kw">in</span> xs]</span>
+<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>plt.plot(xs, kde_curve)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-10-output-1.png" width="571" height="411" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-11-output-1.png" width="571" height="411" class="figure-img"></p>
 <figcaption>The Boxcar kernel centered at 0 with bandwidth <span class="math inline">\(\alpha\)</span> = 1.</figcaption>
 </figure>
 </div>
@@ -877,62 +876,44 @@ <h2 data-number="8.2" class="anchored" data-anchor-id="diving-deeper-into-displo
 <p>As we saw earlier, we can use <code>seaborn</code>’s <code>displot</code> function to plot various distributions. In particular, <code>displot</code> allows you to specify the <code>kind</code> of plot and is a wrapper for <code>histplot</code>, <code>kdeplot</code>, and <code>ecdfplot</code>.</p>
 <p>Below, we can see a couple of examples of how <code>sns.displot</code> can be used to plot various distributions.</p>
 <p>First, we can plot a histogram by setting <code>kind</code> to <code>"hist"</code>. Note that here we’ve specified <code>stat = density</code> to normalize the histogram such that the area under the histogram is equal to 1.</p>
-<div id="32a2f5be" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="10">
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">"hist"</span>, </span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>            stat<span class="op">=</span><span class="st">"density"</span>) <span class="co"># default: stat=count and density integrates to 1</span></span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="c9039ddd" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="11">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">"hist"</span>, </span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>            stat<span class="op">=</span><span class="st">"density"</span>) <span class="co"># default: stat=count and density integrates to 1</span></span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-11-output-2.png" width="476" height="488" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-12-output-1.png" width="476" height="488" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Now, what if we want to generate a KDE plot? We can set <code>kind</code> = to <code>"kde"</code>!</p>
-<div id="98e1ba44" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="11">
-<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">'kde'</span>)</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="9c7bc6ba" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="12">
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
+<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">'kde'</span>)</span>
+<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-12-output-2.png" width="470" height="487" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-13-output-1.png" width="470" height="487" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>And finally, if we want to generate an Empirical Cumulative Distribution Function (ECDF), we can specify <code>kind = "ecdf"</code>.</p>
-<div id="f1704ec4" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="12">
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">'ecdf'</span>)</span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Cumulative Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="b1e0c6a4" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="13">
+<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>sns.displot(data<span class="op">=</span>wb, </span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>            x<span class="op">=</span><span class="st">"gni"</span>, </span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>            kind<span class="op">=</span><span class="st">'ecdf'</span>)</span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Cumulative Distribution of gross national income per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-13-output-2.png" width="507" height="483" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-14-output-1.png" width="507" height="483" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -945,32 +926,32 @@ <h2 data-number="8.3" class="anchored" data-anchor-id="relationships-between-qua
 <h4 data-number="8.3.0.1" class="anchored" data-anchor-id="scatter-plots"><span class="header-section-number">8.3.0.1</span> Scatter Plots</h4>
 <p><strong>Scatter plots</strong> are one of the most useful tools in representing the relationship between <strong>pairs</strong> of quantitative variables. They are particularly important in gauging the strength, or correlation, of the relationship between variables. Knowledge of these relationships can then motivate decisions in our modeling process.</p>
 <p>In <code>matplotlib</code>, we use the function <code>plt.scatter</code> to generate a scatter plot. Notice that, unlike our examples of plotting single-variable distributions, now we specify sequences of values to be plotted along the x-axis <em>and</em> the y-axis.</p>
-<div id="327ecb18" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="13">
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>plt.scatter(wb[<span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>], <span class="op">\</span></span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>            wb[<span class="st">'Adult literacy rate: Female: % ages 15 and older: 2005-14'</span>])</span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"</span><span class="sc">% g</span><span class="st">rowth per capita"</span>)</span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Female adult literacy rate"</span>)</span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="6bcff4f8" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="14">
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>plt.scatter(wb[<span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>], <span class="op">\</span></span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>            wb[<span class="st">'Adult literacy rate: Female: % ages 15 and older: 2005-14'</span>])</span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"</span><span class="sc">% g</span><span class="st">rowth per capita"</span>)</span>
+<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Female adult literacy rate"</span>)</span>
+<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-14-output-1.png" width="593" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-15-output-1.png" width="593" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>In <code>seaborn</code>, we call the function <code>sns.scatterplot</code>. We use the <code>x</code> and <code>y</code> parameters to indicate the values to be plotted along the x and y axes, respectively. By using the <code>hue</code> parameter, we can specify a third variable to be used for coloring each scatter point.</p>
-<div id="ab23fabb" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="14">
-<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>sns.scatterplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>               y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, </span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>               hue <span class="op">=</span> <span class="st">"Continent"</span>)</span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="c2359397" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="15">
+<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>sns.scatterplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>               y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, </span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>               hue <span class="op">=</span> <span class="st">"Continent"</span>)</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-15-output-1.png" width="593" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-16-output-1.png" width="593" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -984,30 +965,30 @@ <h5 data-number="8.3.0.1.1" class="anchored" data-anchor-id="overplotting"><span
 <li><strong>Jittering</strong> is the process of adding a small amount of random noise to all x and y values to slightly shift the position of each datapoint. By randomly shifting all the data by some small distance, we can discern individual points more clearly without modifying the major trends of the original dataset.</li>
 </ul>
 <p>In the cell below, we first jitter the data using <code>np.random.uniform</code>, then re-plot it with smaller markers. The resulting plot is much easier to interpret.</p>
-<div id="df476ae9" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="15">
-<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting a seed ensures that we produce the same plot each time</span></span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="co"># This means that the course notes will not change each time you access them</span></span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">150</span>)</span>
-<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a><span class="co"># This call to np.random.uniform generates random numbers between -1 and 1</span></span>
-<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a><span class="co"># We add these random numbers to the original x data to jitter it slightly</span></span>
-<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>x_noise <span class="op">=</span> np.random.uniform(<span class="op">-</span><span class="dv">1</span>, <span class="dv">1</span>, <span class="bu">len</span>(wb))</span>
-<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>jittered_x <span class="op">=</span> wb[<span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>] <span class="op">+</span> x_noise</span>
-<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Repeat for y data</span></span>
-<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>y_noise <span class="op">=</span> np.random.uniform(<span class="op">-</span><span class="dv">5</span>, <span class="dv">5</span>, <span class="bu">len</span>(wb))</span>
-<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a>jittered_y <span class="op">=</span> wb[<span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>] <span class="op">+</span> y_noise</span>
-<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting the size parameter `s` changes the size of each point</span></span>
-<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a>plt.scatter(jittered_x, jittered_y, s<span class="op">=</span><span class="dv">15</span>)</span>
-<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"</span><span class="sc">% g</span><span class="st">rowth per capita (jittered)"</span>)</span>
-<span id="cb22-18"><a href="#cb22-18" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Female adult literacy rate (jittered)"</span>)</span>
-<span id="cb22-19"><a href="#cb22-19" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="1871832c" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="16">
+<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting a seed ensures that we produce the same plot each time</span></span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="co"># This means that the course notes will not change each time you access them</span></span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>np.random.seed(<span class="dv">150</span>)</span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="co"># This call to np.random.uniform generates random numbers between -1 and 1</span></span>
+<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="co"># We add these random numbers to the original x data to jitter it slightly</span></span>
+<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>x_noise <span class="op">=</span> np.random.uniform(<span class="op">-</span><span class="dv">1</span>, <span class="dv">1</span>, <span class="bu">len</span>(wb))</span>
+<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>jittered_x <span class="op">=</span> wb[<span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>] <span class="op">+</span> x_noise</span>
+<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Repeat for y data</span></span>
+<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>y_noise <span class="op">=</span> np.random.uniform(<span class="op">-</span><span class="dv">5</span>, <span class="dv">5</span>, <span class="bu">len</span>(wb))</span>
+<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>jittered_y <span class="op">=</span> wb[<span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>] <span class="op">+</span> y_noise</span>
+<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting the size parameter `s` changes the size of each point</span></span>
+<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>plt.scatter(jittered_x, jittered_y, s<span class="op">=</span><span class="dv">15</span>)</span>
+<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"</span><span class="sc">% g</span><span class="st">rowth per capita (jittered)"</span>)</span>
+<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Female adult literacy rate (jittered)"</span>)</span>
+<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-16-output-1.png" width="593" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-17-output-1.png" width="593" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1018,41 +999,31 @@ <h5 data-number="8.3.0.1.1" class="anchored" data-anchor-id="overplotting"><span
 <h4 data-number="8.3.0.2" class="anchored" data-anchor-id="lmplot-and-jointplot"><span class="header-section-number">8.3.0.2</span> <code>lmplot</code> and <code>jointplot</code></h4>
 <p><code>seaborn</code> also includes several built-in functions for creating more sophisticated scatter plots. Two of the most commonly used examples are <code>sns.lmplot</code> and <code>sns.jointplot</code>.</p>
 <p><code>sns.lmplot</code> plots both a scatter plot <em>and</em> a linear regression line, all in one function call. We’ll discuss linear regression in a few lectures.</p>
-<div id="f76da774" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="16">
-<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>sns.lmplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>           y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>)</span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="a062bce6" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="17">
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>sns.lmplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>           y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>)</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-17-output-1.png" width="469" height="488" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-18-output-1.png" width="469" height="488" class="figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
 <p><code>sns.jointplot</code> creates a visualization with three components: a scatter plot, a histogram of the distribution of x values, and a histogram of the distribution of y values.</p>
-<div id="9125b947" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="17">
-<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>sns.jointplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>           y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>)</span>
-<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a><span class="co"># plt.suptitle allows us to shift the title up so it does not overlap with the histogram</span></span>
-<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)</span>
-<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="fab6dc3f" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>sns.jointplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>           y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>)</span>
+<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a><span class="co"># plt.suptitle allows us to shift the title up so it does not overlap with the histogram</span></span>
+<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)</span>
+<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-18-output-2.png" width="570" height="567" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-19-output-1.png" width="570" height="567" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1063,28 +1034,18 @@ <h4 data-number="8.3.0.3" class="anchored" data-anchor-id="hex-plots"><span clas
 <p>For datasets with a very large number of datapoints, jittering is unlikely to fully resolve the issue of overplotting. In these cases, we can attempt to visualize our data by its <em>density</em>, rather than displaying each individual datapoint.</p>
 <p><strong>Hex plots</strong> can be thought of as two-dimensional histograms that show the joint distribution between two variables. This is particularly useful when working with very dense data. In a hex plot, the x-y plane is binned into hexagons. Hexagons that are darker in color indicate a greater density of data – that is, there are more data points that lie in the region enclosed by the hexagon.</p>
 <p>We can generate a hex plot using <code>sns.jointplot</code> modified with the <code>kind</code> parameter.</p>
-<div id="5d4a8847" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>sns.jointplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>              y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, <span class="op">\</span></span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>              kind <span class="op">=</span> <span class="st">"hex"</span>)</span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a><span class="co"># plt.suptitle allows us to shift the title up so it does not overlap with the histogram</span></span>
-<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)</span>
-<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="880e12df" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="19">
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>sns.jointplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>              y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, <span class="op">\</span></span>
+<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>              kind <span class="op">=</span> <span class="st">"hex"</span>)</span>
+<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="co"># plt.suptitle allows us to shift the title up so it does not overlap with the histogram</span></span>
+<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)</span>
+<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-19-output-2.png" width="570" height="567" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-20-output-1.png" width="570" height="567" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1094,26 +1055,16 @@ <h4 data-number="8.3.0.3" class="anchored" data-anchor-id="hex-plots"><span clas
 <h4 data-number="8.3.0.4" class="anchored" data-anchor-id="contour-plots"><span class="header-section-number">8.3.0.4</span> Contour Plots</h4>
 <p><strong>Contour plots</strong> are an alternative way of plotting the joint distribution of two variables. You can think of them as the 2-dimensional versions of KDE plots. A contour plot can be interpreted in a similar way to a <a href="https://gisgeography.com/contour-lines-topographic-map/">topographic map</a>. Each contour line represents an area that has the same <em>density</em> of datapoints throughout the region. Contours marked with darker colors contain more datapoints (a higher density) in that region.</p>
 <p><code>sns.kdeplot</code> will generate a contour plot if we specify both x and y data.</p>
-<div id="fc819d62" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="19">
-<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>sns.kdeplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>            y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, <span class="op">\</span></span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>            fill <span class="op">=</span> <span class="va">True</span>)</span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-</code></pre>
-</div>
+<div id="8b97b6b0" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="20">
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>sns.kdeplot(data <span class="op">=</span> wb, x <span class="op">=</span> <span class="st">"per capita: </span><span class="sc">% g</span><span class="st">rowth: 2016"</span>, <span class="op">\</span></span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>            y <span class="op">=</span> <span class="st">"Adult literacy rate: Female: % ages 15 and older: 2005-14"</span>, <span class="op">\</span></span>
+<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>            fill <span class="op">=</span> <span class="va">True</span>)</span>
+<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Female adult literacy against </span><span class="sc">% g</span><span class="st">rowth"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-20-output-2.png" width="596" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-21-output-1.png" width="596" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1126,25 +1077,25 @@ <h2 data-number="8.4" class="anchored" data-anchor-id="transformations"><span cl
 <p>Much of this was done to uncover insights in data, which will prove necessary when we begin building models of data later in the course. A strong graphical correlation between two variables hints at an underlying relationship that we may want to study in greater detail. However, relying on visual relationships alone is limiting - not all plots show association. The presence of outliers and other statistical anomalies makes it hard to interpret data.</p>
 <p><strong>Transformations</strong> are the process of manipulating data to find significant relationships between variables. These are often found by applying mathematical functions to variables that “transform” their range of possible values and highlight some previously hidden associations between data.</p>
 <p>To see why we may want to transform data, consider the following plot of adult literacy rates against gross national income.</p>
-<div id="e04f3192" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="20">
+<div id="358087d5" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="21">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Some data cleaning to help with the next example</span></span>
-<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(index<span class="op">=</span>wb.index)</span>
-<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'lit'</span>] <span class="op">=</span> wb[<span class="st">'Adult literacy rate: Female: % ages 15 and older: 2005-14'</span>] <span class="op">\</span></span>
-<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>            <span class="op">+</span> wb[<span class="st">"Adult literacy rate: Male: % ages 15 and older: 2005-14"</span>]</span>
-<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'inc'</span>] <span class="op">=</span> wb[<span class="st">'gni'</span>]</span>
-<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>df.dropna(inplace<span class="op">=</span><span class="va">True</span>)</span>
-<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>plt.scatter(df[<span class="st">"inc"</span>], df[<span class="st">"lit"</span>])</span>
-<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
-<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
-<span id="cb30-11"><a href="#cb30-11" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Adult literacy rate against GNI per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Some data cleaning to help with the next example</span></span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(index<span class="op">=</span>wb.index)</span>
+<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'lit'</span>] <span class="op">=</span> wb[<span class="st">'Adult literacy rate: Female: % ages 15 and older: 2005-14'</span>] <span class="op">\</span></span>
+<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>            <span class="op">+</span> wb[<span class="st">"Adult literacy rate: Male: % ages 15 and older: 2005-14"</span>]</span>
+<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>df[<span class="st">'inc'</span>] <span class="op">=</span> wb[<span class="st">'gni'</span>]</span>
+<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>df.dropna(inplace<span class="op">=</span><span class="va">True</span>)</span>
+<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a>plt.scatter(df[<span class="st">"inc"</span>], df[<span class="st">"lit"</span>])</span>
+<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
+<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
+<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Adult literacy rate against GNI per capita"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-21-output-1.png" width="593" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-22-output-1.png" width="593" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1170,17 +1121,17 @@ <h3 data-number="8.4.1" class="anchored" data-anchor-id="linearization-and-apply
 </ul>
 <p>One function that produces this result is the <strong>log transformation</strong>. When we take the logarithm of a large number, the original number will decrease in magnitude dramatically. Conversely, when we take the logarithm of a small number, the original number does not change its value by as significant of an amount (to illustrate this, consider the difference between <span class="math inline">\(\log{(100)} = 4.61\)</span> and <span class="math inline">\(\log{(10)} = 2.3\)</span>).</p>
 <p>In Data 100 (and most upper-division STEM classes), <span class="math inline">\(\log\)</span> is used to refer to the natural logarithm with base <span class="math inline">\(e\)</span>.</p>
-<div id="963eb56e" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="21">
-<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># np.log takes the logarithm of an array or Series</span></span>
-<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>])</span>
-<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
-<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
-<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Adult literacy rate against Log(GNI per capita)"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="7e63e6dd" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="22">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="co"># np.log takes the logarithm of an array or Series</span></span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>])</span>
+<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
+<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
+<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>plt.title(<span class="st">"Adult literacy rate against Log(GNI per capita)"</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-22-output-1.png" width="593" height="449" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-23-output-1.png" width="593" height="449" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1193,18 +1144,18 @@ <h3 data-number="8.4.1" class="anchored" data-anchor-id="linearization-and-apply
 <li>Not substantially alter the scaling of small values of y (we do not want to drastically modify the lower end of the y axis, which is already distributed evenly on the vertical scale).</li>
 </ul>
 <p>In this case, it is helpful to apply a <strong>power transformation</strong> – that is, raise our y values to a power. Let’s try raising our adult literacy rate values to the power of 4. Large values raised to the power of 4 will increase in magnitude proportionally much more than small values raised to the power of 4 (consider the difference between <span class="math inline">\(2^4 = 16\)</span> and <span class="math inline">\(200^4 = 1600000000\)</span>).</p>
-<div id="477b0af3" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="22">
-<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Apply a log transformation to the x values and a power transformation to the y values</span></span>
-<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>)</span>
-<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
-<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate (4th power)"</span>)</span>
-<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Adult literacy rate (4th power) against Log(GNI per capita)"</span>)</span>
-<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="44cf91d7" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="23">
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Apply a log transformation to the x values and a power transformation to the y values</span></span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>)</span>
+<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
+<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate (4th power)"</span>)</span>
+<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>plt.suptitle(<span class="st">"Adult literacy rate (4th power) against Log(GNI per capita)"</span>)</span>
+<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a>plt.subplots_adjust(top<span class="op">=</span><span class="fl">0.9</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-23-output-1.png" width="589" height="477" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-24-output-1.png" width="589" height="477" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1214,25 +1165,25 @@ <h3 data-number="8.4.1" class="anchored" data-anchor-id="linearization-and-apply
 <p><span class="math display">\[y^4 = m(\log{x}) + b\]</span></p>
 <p>Where <span class="math inline">\(m\)</span> represents the slope of the linear fit, while <span class="math inline">\(b\)</span> represents the intercept.</p>
 <p>The cell below computes <span class="math inline">\(m\)</span> and <span class="math inline">\(b\)</span> for our transformed data. We’ll discuss how this code was generated in a future lecture.</p>
-<div id="6ca512a8" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="23">
+<div id="a4ceed1c" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="24">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The code below fits a linear regression model. We'll discuss it at length in a future lecture</span></span>
-<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.linear_model <span class="im">import</span> LinearRegression</span>
-<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> LinearRegression()</span>
-<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>model.fit(np.log(df[[<span class="st">"inc"</span>]]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>)</span>
-<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>m, b <span class="op">=</span> model.coef_[<span class="dv">0</span>], model.intercept_</span>
-<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"The slope, m, of the transformed data is: </span><span class="sc">{</span>m<span class="sc">}</span><span class="ss">"</span>)</span>
-<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"The intercept, b, of the transformed data is: </span><span class="sc">{</span>b<span class="sc">}</span><span class="ss">"</span>)</span>
-<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.sort_values(<span class="st">"inc"</span>)</span>
-<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>, label<span class="op">=</span><span class="st">"Transformed data"</span>)</span>
-<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a>plt.plot(np.log(df[<span class="st">"inc"</span>]), m<span class="op">*</span>np.log(df[<span class="st">"inc"</span>])<span class="op">+</span>b, c<span class="op">=</span><span class="st">"red"</span>, label<span class="op">=</span><span class="st">"Linear regression"</span>)</span>
-<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
-<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate (4th power)"</span>)</span>
-<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a>plt.legend()<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># The code below fits a linear regression model. We'll discuss it at length in a future lecture</span></span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.linear_model <span class="im">import</span> LinearRegression</span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> LinearRegression()</span>
+<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>model.fit(np.log(df[[<span class="st">"inc"</span>]]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>)</span>
+<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>m, b <span class="op">=</span> model.coef_[<span class="dv">0</span>], model.intercept_</span>
+<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"The slope, m, of the transformed data is: </span><span class="sc">{</span>m<span class="sc">}</span><span class="ss">"</span>)</span>
+<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"The intercept, b, of the transformed data is: </span><span class="sc">{</span>b<span class="sc">}</span><span class="ss">"</span>)</span>
+<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> df.sort_values(<span class="st">"inc"</span>)</span>
+<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a>plt.scatter(np.log(df[<span class="st">"inc"</span>]), df[<span class="st">"lit"</span>]<span class="op">**</span><span class="dv">4</span>, label<span class="op">=</span><span class="st">"Transformed data"</span>)</span>
+<span id="cb24-13"><a href="#cb24-13" aria-hidden="true" tabindex="-1"></a>plt.plot(np.log(df[<span class="st">"inc"</span>]), m<span class="op">*</span>np.log(df[<span class="st">"inc"</span>])<span class="op">+</span>b, c<span class="op">=</span><span class="st">"red"</span>, label<span class="op">=</span><span class="st">"Linear regression"</span>)</span>
+<span id="cb24-14"><a href="#cb24-14" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Log(gross national income per capita)"</span>)</span>
+<span id="cb24-15"><a href="#cb24-15" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate (4th power)"</span>)</span>
+<span id="cb24-16"><a href="#cb24-16" aria-hidden="true" tabindex="-1"></a>plt.legend()<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
 <pre><code>The slope, m, of the transformed data is: 336400693.43172693
@@ -1241,7 +1192,7 @@ <h3 data-number="8.4.1" class="anchored" data-anchor-id="linearization-and-apply
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-24-output-2.png" width="597" height="443" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-25-output-2.png" width="597" height="443" class="figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1252,20 +1203,20 @@ <h3 data-number="8.4.1" class="anchored" data-anchor-id="linearization-and-apply
 <p>By rearranging the equation, we find a relationship between the untransformed variables <span class="math inline">\(x\)</span> and <span class="math inline">\(y\)</span>.</p>
 <p><span class="math display">\[y = [m(\log{x}) + b]^{(1/4)}\]</span></p>
 <p>When we plug in the values for <span class="math inline">\(m\)</span> and <span class="math inline">\(b\)</span> computed above, something interesting happens.</p>
-<div id="7249d486" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="24">
+<div id="810546c9" class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="25">
 <details class="code-fold">
 <summary>Code</summary>
-<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Now, plug the values for m and b into the relationship between the untransformed x and y</span></span>
-<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(df[<span class="st">"inc"</span>], df[<span class="st">"lit"</span>], label<span class="op">=</span><span class="st">"Untransformed data"</span>)</span>
-<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>plt.plot(df[<span class="st">"inc"</span>], (m<span class="op">*</span>np.log(df[<span class="st">"inc"</span>])<span class="op">+</span>b)<span class="op">**</span>(<span class="dv">1</span><span class="op">/</span><span class="dv">4</span>), c<span class="op">=</span><span class="st">"red"</span>, label<span class="op">=</span><span class="st">"Modeled relationship"</span>)</span>
-<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
-<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
-<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a>plt.legend()<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Now, plug the values for m and b into the relationship between the untransformed x and y</span></span>
+<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>plt.scatter(df[<span class="st">"inc"</span>], df[<span class="st">"lit"</span>], label<span class="op">=</span><span class="st">"Untransformed data"</span>)</span>
+<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>plt.plot(df[<span class="st">"inc"</span>], (m<span class="op">*</span>np.log(df[<span class="st">"inc"</span>])<span class="op">+</span>b)<span class="op">**</span>(<span class="dv">1</span><span class="op">/</span><span class="dv">4</span>), c<span class="op">=</span><span class="st">"red"</span>, label<span class="op">=</span><span class="st">"Modeled relationship"</span>)</span>
+<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>plt.xlabel(<span class="st">"Gross national income per capita"</span>)</span>
+<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>plt.ylabel(<span class="st">"Adult literacy rate"</span>)</span>
+<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>plt.legend()<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="visualization_2_files/figure-html/cell-25-output-1.png" width="593" height="429" class="figure-img"></p>
+<p><img src="visualization_2_files/figure-html/cell-26-output-1.png" width="593" height="429" class="figure-img"></p>
 </figure>
 </div>
 </div>
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diff --git a/docs/visualization_2/visualization_2_files/figure-pdf/cell-13-output-2.pdf b/docs/visualization_2/visualization_2_files/figure-pdf/cell-13-output-2.pdf
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diff --git a/docs/visualization_2/visualization_2_files/figure-pdf/cell-23-output-1.pdf b/docs/visualization_2/visualization_2_files/figure-pdf/cell-23-output-1.pdf
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  []
 
 
@@ -1725,7 +1725,7 @@ Package longtable Warning: Column widths have changed
 
 
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  []
 
 
@@ -1920,7 +1920,7 @@ Package longtable Warning: Column widths have changed
 
 
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-[]\TU/lmtt/m/n/10.95 /var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94434/874729699.py:1: UserWarning:[] 
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  []
 
 
@@ -2585,9 +2585,9 @@ Overfull \hbox (300.57729pt too wide) in paragraph at lines 8505--8505
 l.8568 ...n your own character classes with \d, \w
                                                   , \s, 
 Here is how much of TeX's memory you used:
- 39473 strings out of 476173
- 811414 string characters out of 5784306
- 1946609 words of memory out of 5000000
+ 39470 strings out of 476173
+ 811232 string characters out of 5783897
+ 1522843 words of memory out of 5000000
  61465 multiletter control sequences out of 15000+600000
  564564 words of font info for 94 fonts, out of 8000000 for 9000
  36 hyphenation exceptions out of 8191
diff --git a/index.pdf b/index.pdf
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diff --git a/index.tex b/index.tex
index cd8963c3..841d5827 100644
--- a/index.tex
+++ b/index.tex
@@ -264,7 +264,7 @@ \section*{About the Course Notes}\label{about-the-course-notes}
 
 \chapter{Introduction}\label{introduction}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -318,7 +318,7 @@ \chapter{Introduction}\label{introduction}
 allowing you to take data and produce useful insights on the world's
 most challenging and ambiguous problems.
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -336,7 +336,7 @@ \chapter{Introduction}\label{introduction}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -366,7 +366,7 @@ \chapter{Introduction}\label{introduction}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 To ensure that you can get the most out of the course content, please
 make sure that you are familiar with:
@@ -597,7 +597,7 @@ \section{Conclusion}\label{conclusion}
 
 \chapter{Pandas I}\label{pandas-i}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -1937,7 +1937,7 @@ \section{Parting Note}\label{parting-note}
 
 \chapter{Pandas II}\label{pandas-ii}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -2708,7 +2708,7 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-77005 & CA & F & 1977 & Loraine & 14 \\
+229638 & CA & F & 2020 & Mayra & 26 \\
 \end{longtable}
 
 Naturally, this can be chained with other methods and operators
@@ -2728,11 +2728,11 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-247145 & 1927 & Mitchell & 7 \\
-249999 & 1932 & Monte & 11 \\
-170778 & 2005 & Camden & 9 \\
-244662 & 1923 & Aurelio & 11 \\
-29677 & 1951 & Willa & 8 \\
+189843 & 2010 & Skylar & 104 \\
+201612 & 2012 & Yu & 5 \\
+192014 & 2010 & Sofiya & 9 \\
+266117 & 1955 & Chris & 252 \\
+94028 & 1984 & Wendy & 469 \\
 \end{longtable}
 
 \begin{Shaded}
@@ -2749,10 +2749,10 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-344459 & 2000 & Javin & 6 \\
-151412 & 2000 & Aislynn & 8 \\
-151153 & 2000 & Karisma & 10 \\
-152149 & 2000 & Makiya & 6 \\
+344275 & 2000 & Kenan & 7 \\
+152244 & 2000 & Sherrie & 6 \\
+343016 & 2000 & Darrell & 40 \\
+150175 & 2000 & Iman & 25 \\
 \end{longtable}
 
 \subsection{\texorpdfstring{\texttt{.value\_counts()}}{.value\_counts()}}\label{value_counts}
@@ -2874,7 +2874,7 @@ \section{Parting Note}\label{parting-note-1}
 
 \chapter{Pandas III}\label{pandas-iii}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -3144,7 +3144,7 @@ \section{\texorpdfstring{Aggregating Data with
 \end{Shaded}
 
 \begin{verbatim}
-<pandas.core.groupby.generic.DataFrameGroupBy object at 0x124a39310>
+<pandas.core.groupby.generic.DataFrameGroupBy object at 0x167cf1d50>
 \end{verbatim}
 
 What does this strange output mean? Calling \texttt{.groupby}
@@ -3484,7 +3484,7 @@ \subsection{Plotting Birth Counts}\label{plotting-birth-counts}
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94369/390646742.py:1: FutureWarning:
+/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19239/390646742.py:1: FutureWarning:
 
 The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
 \end{verbatim}
@@ -4129,7 +4129,7 @@ \subsection{\texorpdfstring{Aggregation with \texttt{lambda}
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94369/4278286395.py:1: FutureWarning:
+/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19239/4278286395.py:1: FutureWarning:
 
 The provided callable <built-in function max> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
 \end{verbatim}
@@ -4358,7 +4358,7 @@ \section{Aggregating Data with Pivot
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94369/3186035650.py:3: FutureWarning:
+/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19239/3186035650.py:3: FutureWarning:
 
 The provided callable <built-in function sum> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
 \end{verbatim}
@@ -4685,7 +4685,7 @@ \chapter{Data Cleaning and EDA}\label{data-cleaning-and-eda}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -5529,7 +5529,7 @@ \subsubsection{\texorpdfstring{Temporality with \texttt{pandas}'
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_94434/874729699.py:1: UserWarning:
+/var/folders/gr/vb80r2qs5td4rqbnv4dn2klh0000gn/T/ipykernel_19298/874729699.py:1: UserWarning:
 
 Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
 \end{verbatim}
@@ -7356,7 +7356,7 @@ \subsection{EDA and Data Wrangling}\label{eda-and-data-wrangling}
 
 \chapter{Regular Expressions}\label{regular-expressions}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -8575,7 +8575,7 @@ \section{Limitations of Regular
 
 \chapter{Visualization I}\label{visualization-i}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -8739,6 +8739,9 @@ \section{Qualitative Variables: Bar
 \begin{Highlighting}[]
 \ImportTok{import}\NormalTok{ pandas }\ImportTok{as}\NormalTok{ pd}
 \ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np}
+\ImportTok{import}\NormalTok{ warnings }
+
+\NormalTok{warnings.filterwarnings(}\StringTok{"ignore"}\NormalTok{, }\StringTok{"use\_inf\_as\_na"}\NormalTok{) }\CommentTok{\# Supresses distracting deprecation warnings}
 
 \NormalTok{wb }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{"data/world\_bank.csv"}\NormalTok{, index\_col}\OperatorTok{=}\DecValTok{0}\NormalTok{)}
 \NormalTok{wb.head()}
@@ -9039,17 +9042,7 @@ \subsection{Box Plots and Violin
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-10-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-10-output-1.pdf}
 
 In a box plot, the lower extent of the box lies at Q1, while the upper
 extent of the box lies at Q3. The horizontal line in the middle of the
@@ -9139,13 +9132,7 @@ \subsubsection{Plotting Histograms}\label{plotting-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-15-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-15-output-1.pdf}
 
 \subsubsection{Overlaid Histograms}\label{overlaid-histograms}
 
@@ -9177,13 +9164,7 @@ \subsubsection{Overlaid Histograms}\label{overlaid-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-17-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-17-output-1.pdf}
 
 Again, each bin of a histogram is scaled such that its \textbf{area} is
 proportional to the \textbf{percentage} of all datapoints that it
@@ -9258,17 +9239,11 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
 \begin{verbatim}
 Text(0.5, 1.0, 'Distribution with a long right tail')
 \end{verbatim}
 
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-19-output-3.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-19-output-2.pdf}
 
 \begin{itemize}
 \tightlist
@@ -9290,17 +9265,11 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
 \begin{verbatim}
 Text(0.5, 1.0, 'Distribution with a long left tail')
 \end{verbatim}
 
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-20-output-3.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-20-output-2.pdf}
 
 \paragraph{Outliers}\label{outliers}
 
@@ -9332,13 +9301,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-21-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-21-output-1.pdf}
 
 \begin{Shaded}
 \begin{Highlighting}[]
@@ -9349,13 +9312,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-22-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-22-output-1.pdf}
 
 \begin{Shaded}
 \begin{Highlighting}[]
@@ -9366,13 +9323,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-23-output-2.pdf}
+\includegraphics{visualization_1/visualization_1_files/figure-pdf/cell-23-output-1.pdf}
 
 In part, it is these ambiguities that motivate us to consider using
 Kernel Density Estimation (KDE), which we will explore more in the next
@@ -9382,7 +9333,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}
 
 \chapter{Visualization II}\label{visualization-ii}
 
-\begin{tcolorbox}[enhanced jigsaw, coltitle=black, toprule=.15mm, rightrule=.15mm, colback=white, bottomtitle=1mm, arc=.35mm, titlerule=0mm, colbacktitle=quarto-callout-note-color!10!white, breakable, toptitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, left=2mm, colframe=quarto-callout-note-color-frame, opacityback=0, opacitybacktitle=0.6, leftrule=.75mm]
+\begin{tcolorbox}[enhanced jigsaw, coltitle=black, colback=white, breakable, opacityback=0, titlerule=0mm, arc=.35mm, bottomtitle=1mm, colframe=quarto-callout-note-color-frame, left=2mm, toptitle=1mm, leftrule=.75mm, opacitybacktitle=0.6, colbacktitle=quarto-callout-note-color!10!white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, bottomrule=.15mm, rightrule=.15mm, toprule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -9432,6 +9383,9 @@ \subsection{KDE Theory}\label{kde-theory}
 \ImportTok{import}\NormalTok{ numpy }\ImportTok{as}\NormalTok{ np}
 \ImportTok{import}\NormalTok{ matplotlib.pyplot }\ImportTok{as}\NormalTok{ plt}
 \ImportTok{import}\NormalTok{ seaborn }\ImportTok{as}\NormalTok{ sns}
+\ImportTok{import}\NormalTok{ warnings }
+
+\NormalTok{warnings.filterwarnings(}\StringTok{"ignore"}\NormalTok{, }\StringTok{"use\_inf\_as\_na"}\NormalTok{) }\CommentTok{\# Supresses distracting deprecation warnings}
 
 \NormalTok{wb }\OperatorTok{=}\NormalTok{ pd.read\_csv(}\StringTok{"data/world\_bank.csv"}\NormalTok{, index\_col}\OperatorTok{=}\DecValTok{0}\NormalTok{)}
 \NormalTok{wb }\OperatorTok{=}\NormalTok{ wb.rename(columns}\OperatorTok{=}\NormalTok{\{}\StringTok{\textquotesingle{}Antiretroviral therapy coverage: }\SpecialCharTok{\% o}\StringTok{f people living with HIV: 2015\textquotesingle{}}\NormalTok{:}\StringTok{"HIV rate"}\NormalTok{,}
@@ -9495,13 +9449,7 @@ \subsection{KDE Theory}\label{kde-theory}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-3-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-3-output-1.pdf}
 
 Notice that the smooth KDE curve is higher when the histogram bins are
 taller. You can think of the height of the KDE curve as representing how
@@ -9551,13 +9499,7 @@ \subsection{Constructing a KDE}\label{constructing-a-kde}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-4-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-4-output-1.pdf}
 
 Our goal is to create the following KDE curve, which was generated
 automatically by \texttt{sns.kdeplot}.
@@ -9572,13 +9514,7 @@ \subsection{Constructing a KDE}\label{constructing-a-kde}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-5-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-5-output-1.pdf}
 
 \subsubsection{Step 1: Place a Kernel at Each Data
 Point}\label{step-1-place-a-kernel-at-each-data-point}
@@ -9631,13 +9567,7 @@ \subsubsection{Step 1: Place a Kernel at Each Data
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-6-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-6-output-1.pdf}
 
 To begin creating our KDE, we place a kernel on \emph{each} datapoint in
 our dataset. For our dataset of 5 points, we will have 5 kernels.
@@ -9726,12 +9656,29 @@ \subsubsection{Step 3: Sum the Normalized
 \NormalTok{plt.xlabel(}\StringTok{"Data"}\NormalTok{)}
 \NormalTok{plt.ylabel(}\StringTok{"Density"}\NormalTok{)}
 
-\NormalTok{plot\_kde(gaussian\_kernel, data, a }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
+\NormalTok{sns.kdeplot(data, bw\_method}\OperatorTok{=}\FloatTok{0.65}\NormalTok{)}
+\NormalTok{sns.histplot(data, stat}\OperatorTok{=}\StringTok{"density"}\NormalTok{, bins}\OperatorTok{=}\DecValTok{2}\NormalTok{)}\OperatorTok{;}
 \end{Highlighting}
 \end{Shaded}
 
 \includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-9-output-1.pdf}
 
+An alternative method to generate the above KDE is shown below, this
+time using \texttt{sns.histplot}'s arguments.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{plt.xlim(}\OperatorTok{{-}}\DecValTok{3}\NormalTok{, }\DecValTok{10}\NormalTok{)}
+\NormalTok{plt.ylim(}\DecValTok{0}\NormalTok{, }\FloatTok{0.5}\NormalTok{)}
+\NormalTok{plt.xlabel(}\StringTok{"Data"}\NormalTok{)}
+\NormalTok{plt.ylabel(}\StringTok{"Density"}\NormalTok{)}
+
+\NormalTok{sns.histplot(data, bins}\OperatorTok{=}\DecValTok{2}\NormalTok{, kde}\OperatorTok{=}\VariableTok{True}\NormalTok{, stat}\OperatorTok{=}\StringTok{"density"}\NormalTok{, kde\_kws}\OperatorTok{=}\BuiltInTok{dict}\NormalTok{(cut}\OperatorTok{=}\DecValTok{3}\NormalTok{, bw\_method}\OperatorTok{=}\FloatTok{0.65}\NormalTok{))}
+\end{Highlighting}
+\end{Shaded}
+
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-10-output-1.pdf}
+
 \subsection{Kernel Functions and
 Bandwidths}\label{kernel-functions-and-bandwidths}
 
@@ -9858,7 +9805,7 @@ \subsubsection{Gaussian Kernel}\label{gaussian-kernel}
 \begin{minipage}[b]{\linewidth}\raggedright
 Gaussian Kernel, \(\alpha\) = 2
 \end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
-Gaussian Kernel, \(\alpha\) = 10
+Gaussian Kernel, \(\alpha\) = 5
 \end{minipage} \\
 \midrule\noalign{}
 \endhead
@@ -9898,7 +9845,7 @@ \subsubsection{Boxcar Kernel}\label{boxcar-kernel}
 
 \begin{figure}[H]
 
-{\centering \includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-10-output-1.pdf}
+{\centering \includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-11-output-1.pdf}
 
 }
 
@@ -9952,13 +9899,7 @@ \section{\texorpdfstring{Diving Deeper into
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-11-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-12-output-1.pdf}
 
 Now, what if we want to generate a KDE plot? We can set \texttt{kind} =
 to \texttt{"kde"}!
@@ -9972,13 +9913,7 @@ \section{\texorpdfstring{Diving Deeper into
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-12-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-13-output-1.pdf}
 
 And finally, if we want to generate an Empirical Cumulative Distribution
 Function (ECDF), we can specify \texttt{kind\ =\ "ecdf"}.
@@ -9992,13 +9927,7 @@ \section{\texorpdfstring{Diving Deeper into
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-13-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-14-output-1.pdf}
 
 \section{Relationships Between Quantitative
 Variables}\label{relationships-between-quantitative-variables}
@@ -10031,7 +9960,7 @@ \subsubsection{Scatter Plots}\label{scatter-plots}
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-14-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-15-output-1.pdf}
 
 In \texttt{seaborn}, we call the function \texttt{sns.scatterplot}. We
 use the \texttt{x} and \texttt{y} parameters to indicate the values to
@@ -10049,7 +9978,7 @@ \subsubsection{Scatter Plots}\label{scatter-plots}
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-15-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-16-output-1.pdf}
 
 \paragraph{Overplotting}\label{overplotting}
 
@@ -10106,7 +10035,7 @@ \subsubsection{Scatter Plots}\label{scatter-plots}
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-16-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-17-output-1.pdf}
 
 \subsubsection{\texorpdfstring{\texttt{lmplot} and
 \texttt{jointplot}}{lmplot and jointplot}}\label{lmplot-and-jointplot}
@@ -10128,7 +10057,7 @@ \subsubsection{\texorpdfstring{\texttt{lmplot} and
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-17-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-18-output-1.pdf}
 
 \texttt{sns.jointplot} creates a visualization with three components: a
 scatter plot, a histogram of the distribution of x values, and a
@@ -10145,17 +10074,7 @@ \subsubsection{\texorpdfstring{\texttt{lmplot} and
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-18-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-19-output-1.pdf}
 
 \subsubsection{Hex plots}\label{hex-plots}
 
@@ -10186,17 +10105,7 @@ \subsubsection{Hex plots}\label{hex-plots}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-19-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-20-output-1.pdf}
 
 \subsubsection{Contour Plots}\label{contour-plots}
 
@@ -10222,17 +10131,7 @@ \subsubsection{Contour Plots}\label{contour-plots}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{verbatim}
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-
-/Users/xiaoruiliu/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning:
-
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-\end{verbatim}
-
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-20-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-21-output-1.pdf}
 
 \section{Transformations}\label{transformations}
 
@@ -10272,7 +10171,7 @@ \section{Transformations}\label{transformations}
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-21-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-22-output-1.pdf}
 
 This plot is difficult to interpret for two reasons:
 
@@ -10358,7 +10257,7 @@ \subsection{Linearization and Applying
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-22-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-23-output-1.pdf}
 
 After taking the logarithm of our x values, our plot appears much more
 balanced in its horizontal scale. We no longer have many datapoints
@@ -10403,7 +10302,7 @@ \subsection{Linearization and Applying
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-23-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-24-output-1.pdf}
 
 Our scatter plot is looking a lot better! Now, we are plotting the log
 of our original x values on the horizontal axis, and the 4th power of
@@ -10452,7 +10351,7 @@ \subsection{Linearization and Applying
 The intercept, b, of the transformed data is: -1802204836.0479977
 \end{verbatim}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-24-output-2.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-25-output-2.pdf}
 
 What if we want to understand the \emph{underlying} relationship between
 our original variables, before they were transformed? We can simply
@@ -10482,7 +10381,7 @@ \subsection{Linearization and Applying
 \end{Highlighting}
 \end{Shaded}
 
-\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-25-output-1.pdf}
+\includegraphics{visualization_2/visualization_2_files/figure-pdf/cell-26-output-1.pdf}
 
 We have found a relationship between our original variables -- gross
 national income and adult literacy rate!
diff --git a/visualization_1/visualization_1.ipynb b/visualization_1/visualization_1.ipynb
new file mode 100644
index 00000000..457a73ef
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "title: Visualization I\n",
+        "format:\n",
+        "  html:\n",
+        "    toc: true\n",
+        "    toc-depth: 5\n",
+        "    toc-location: right\n",
+        "    code-fold: false\n",
+        "    theme:\n",
+        "      - cosmo\n",
+        "      - cerulean\n",
+        "    callout-icon: false\n",
+        "jupyter:\n",
+        "  jupytext:\n",
+        "    text_representation:\n",
+        "      extension: .qmd\n",
+        "      format_name: quarto\n",
+        "      format_version: '1.0'\n",
+        "      jupytext_version: 1.16.1\n",
+        "  kernelspec:\n",
+        "    display_name: Python 3 (ipykernel)\n",
+        "    language: python\n",
+        "    name: python3\n",
+        "---\n",
+        "\n",
+        "::: {.callout-note collapse=\"false\"}\n",
+        "## Learning Outcomes\n",
+        "- Understand the theories behind effective visualizations and start to generate plots of our own with `matplotlib` and `seaborn`.\n",
+        "- Analyze histograms and identify the skewness, potential outliers, and the mode.\n",
+        "- Use `boxplot` and `violinplot` to compare two distributions.\n",
+        ":::\n",
+        "\n",
+        "In our journey of the data science lifecycle, we have begun to explore the vast world of exploratory data analysis. More recently, we learned how to pre-process data using various data manipulation techniques. As we work towards understanding our data, there is one key component missing in our arsenal — the ability to visualize and discern relationships in existing data.\n",
+        "\n",
+        "These next two lectures will introduce you to various examples of data visualizations and their underlying theory. In doing so, we'll motivate their importance in real-world examples with the use of plotting libraries.\n",
+        "\n",
+        "## Visualizations in Data 8 and Data 100 (so far)\n",
+        "\n",
+        "You've likely encountered several forms of data visualizations in your studies. You may remember two such examples from Data 8: line plots, scatter plots, and histograms. Each of these served a unique purpose. For example, line plots displayed how numerical quantities changed over time, while histograms were useful in understanding a variable's distribution. \n",
+        "\n",
+        "| Line Chart | Scatter Plot | \n",
+        "| --- | --- | \n",
+        "| <img src=\"images/line_chart_viz.png\" alt='bad_distro' width='300'> |  <img src=\"images/scatter.png\" alt='bad_distro' width='300'> | \n",
+        "\n",
+        "| Histogram | \n",
+        "| --- |\n",
+        "| <img src=\"images/histogram_viz.png\" alt='bad_distro' width='400'> |\n",
+        "\n",
+        "## Goals of Visualization\n",
+        "\n",
+        "Visualizations are useful for a number of reasons. In Data 100, we consider two areas in particular:\n",
+        "\n",
+        "1. To broaden your understanding of the data. Summarizing trends visually before in-depth analysis is a key part of exploratory data analysis. Creating these graphs is a lightweight, iterative and flexible process that helps us investigate relationships between variables.\n",
+        "2. To communicate results/conclusions to others. These visualizations are highly editorial, selective, and fine-tuned to achieve a communications goal, so be thoughtful and careful about its clarity, accessibility, and necessary context.\n",
+        "\n",
+        "Altogether, these goals emphasize the fact that visualizations aren't a matter of making \"pretty\" pictures; we need to do a lot of thinking about what stylistic choices communicate ideas most effectively.\n",
+        "\n",
+        "This course note will focus on the first half of visualization topics in Data 100. The goal here is to understand how to choose the \"right\" plot depending on different variable types and, secondly, how to generate these plots using code.\n",
+        "\n",
+        "## An Overview of Distributions\n",
+        "\n",
+        "A distribution describes both the set of values that a single variable can take and the frequency of unique values in a single variable. For example, if we're interested in the distribution of students across Data 100 discussion sections, the set of possible values is a list of discussion sections (10-11am, 11-12pm, etc.), and the frequency that each of those values occur is the number of students enrolled in each section. In other words, the we're interested in how a variable is distributed across it's possible values. Therefore, distributions must satisfy two properties:\n",
+        "\n",
+        "   1. The total frequency of all categories must sum to 100%\n",
+        "   2. Total count should sum to the total number of datapoints if we're using raw counts.\n",
+        "   \n",
+        "| Not a Valid Distribution | Valid Distribution | \n",
+        "| --- | --- | \n",
+        "| <img src=\"images/bad_distro.png\" alt='bad_distro' width='350'> | <img src=\"images/good_distro.png\" alt='good_distro' width='300'> | \n",
+        "| This is not a valid distribution since individuals can be associated with more than one category and the bar values demonstrate values in minutes and not probability. | This example satisfies the two properties of distributions, so it is a valid distribution. | \n",
+        "\n",
+        "## Variable Types Should Inform Plot Choice\n",
+        "\n",
+        "Different plots are more or less suited for displaying particular types of variables, laid out in the diagram below:\n",
+        "\n",
+        "<center><img src = \"images/variable_types_vis_1.png\" width = \"700\"></img></a></center>\n",
+        "\n",
+        "The first step of any visualization is to identify the type(s) of variables we're working with. From here, we can select an appropriate plot type: \n",
+        "\n",
+        "## Qualitative Variables: Bar Plots\n",
+        "\n",
+        "A **bar plot** is one of the most common ways of displaying the **distribution** of a **qualitative** (categorical) variable. The length of a bar plot encodes the frequency of a category; the width encodes no useful information. The color *could* indicate a sub-category, but this is not necessarily the case. \n",
+        "\n",
+        "Let's contextualize this in an example. We will use the World Bank dataset (`wb`) in our analysis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Continent</th>\n",
+              "      <th>Country</th>\n",
+              "      <th>Primary completion rate: Male: % of relevant age group: 2015</th>\n",
+              "      <th>Primary completion rate: Female: % of relevant age group: 2015</th>\n",
+              "      <th>Lower secondary completion rate: Male: % of relevant age group: 2015</th>\n",
+              "      <th>Lower secondary completion rate: Female: % of relevant age group: 2015</th>\n",
+              "      <th>Youth literacy rate: Male: % of ages 15-24: 2005-14</th>\n",
+              "      <th>Youth literacy rate: Female: % of ages 15-24: 2005-14</th>\n",
+              "      <th>Adult literacy rate: Male: % ages 15 and older: 2005-14</th>\n",
+              "      <th>Adult literacy rate: Female: % ages 15 and older: 2005-14</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Access to improved sanitation facilities: % of population: 1990</th>\n",
+              "      <th>Access to improved sanitation facilities: % of population: 2015</th>\n",
+              "      <th>Child immunization rate: Measles: % of children ages 12-23 months: 2015</th>\n",
+              "      <th>Child immunization rate: DTP3: % of children ages 12-23 months: 2015</th>\n",
+              "      <th>Children with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016</th>\n",
+              "      <th>Children with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016</th>\n",
+              "      <th>Children sleeping under treated bed nets: % of children under age 5: 2009-2016</th>\n",
+              "      <th>Children with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016</th>\n",
+              "      <th>Tuberculosis: Treatment success rate: % of new cases: 2014</th>\n",
+              "      <th>Tuberculosis: Cases detection rate: % of new estimated cases: 2015</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>106.0</td>\n",
+              "      <td>105.0</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>83.0</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>80.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>82.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>25.9</td>\n",
+              "      <td>28.3</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>64.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Benin</td>\n",
+              "      <td>83.0</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>72.7</td>\n",
+              "      <td>25.9</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>61.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Botswana</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>101.0</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>63.0</td>\n",
+              "      <td>97.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>62.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Burundi</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>53.8</td>\n",
+              "      <td>25.4</td>\n",
+              "      <td>91.0</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 47 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Continent   Country  \\\n",
+              "0    Africa   Algeria   \n",
+              "1    Africa    Angola   \n",
+              "2    Africa     Benin   \n",
+              "3    Africa  Botswana   \n",
+              "5    Africa   Burundi   \n",
+              "\n",
+              "   Primary completion rate: Male: % of relevant age group: 2015  \\\n",
+              "0                                              106.0              \n",
+              "1                                                NaN              \n",
+              "2                                               83.0              \n",
+              "3                                               98.0              \n",
+              "5                                               58.0              \n",
+              "\n",
+              "   Primary completion rate: Female: % of relevant age group: 2015  \\\n",
+              "0                                              105.0                \n",
+              "1                                                NaN                \n",
+              "2                                               73.0                \n",
+              "3                                              101.0                \n",
+              "5                                               66.0                \n",
+              "\n",
+              "   Lower secondary completion rate: Male: % of relevant age group: 2015  \\\n",
+              "0                                               68.0                      \n",
+              "1                                                NaN                      \n",
+              "2                                               50.0                      \n",
+              "3                                               86.0                      \n",
+              "5                                               35.0                      \n",
+              "\n",
+              "   Lower secondary completion rate: Female: % of relevant age group: 2015  \\\n",
+              "0                                               85.0                        \n",
+              "1                                                NaN                        \n",
+              "2                                               37.0                        \n",
+              "3                                               87.0                        \n",
+              "5                                               30.0                        \n",
+              "\n",
+              "   Youth literacy rate: Male: % of ages 15-24: 2005-14  \\\n",
+              "0                                               96.0     \n",
+              "1                                               79.0     \n",
+              "2                                               55.0     \n",
+              "3                                               96.0     \n",
+              "5                                               90.0     \n",
+              "\n",
+              "   Youth literacy rate: Female: % of ages 15-24: 2005-14  \\\n",
+              "0                                               92.0       \n",
+              "1                                               67.0       \n",
+              "2                                               31.0       \n",
+              "3                                               99.0       \n",
+              "5                                               88.0       \n",
+              "\n",
+              "   Adult literacy rate: Male: % ages 15 and older: 2005-14  \\\n",
+              "0                                               83.0         \n",
+              "1                                               82.0         \n",
+              "2                                               41.0         \n",
+              "3                                               87.0         \n",
+              "5                                               89.0         \n",
+              "\n",
+              "   Adult literacy rate: Female: % ages 15 and older: 2005-14  ...  \\\n",
+              "0                                               68.0          ...   \n",
+              "1                                               60.0          ...   \n",
+              "2                                               18.0          ...   \n",
+              "3                                               89.0          ...   \n",
+              "5                                               85.0          ...   \n",
+              "\n",
+              "   Access to improved sanitation facilities: % of population: 1990  \\\n",
+              "0                                               80.0                 \n",
+              "1                                               22.0                 \n",
+              "2                                                7.0                 \n",
+              "3                                               39.0                 \n",
+              "5                                               42.0                 \n",
+              "\n",
+              "   Access to improved sanitation facilities: % of population: 2015  \\\n",
+              "0                                               88.0                 \n",
+              "1                                               52.0                 \n",
+              "2                                               20.0                 \n",
+              "3                                               63.0                 \n",
+              "5                                               48.0                 \n",
+              "\n",
+              "   Child immunization rate: Measles: % of children ages 12-23 months: 2015  \\\n",
+              "0                                               95.0                         \n",
+              "1                                               55.0                         \n",
+              "2                                               75.0                         \n",
+              "3                                               97.0                         \n",
+              "5                                               93.0                         \n",
+              "\n",
+              "   Child immunization rate: DTP3: % of children ages 12-23 months: 2015  \\\n",
+              "0                                               95.0                      \n",
+              "1                                               64.0                      \n",
+              "2                                               79.0                      \n",
+              "3                                               95.0                      \n",
+              "5                                               94.0                      \n",
+              "\n",
+              "   Children with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016  \\\n",
+              "0                                               66.0                                                                   \n",
+              "1                                                NaN                                                                   \n",
+              "2                                               23.0                                                                   \n",
+              "3                                                NaN                                                                   \n",
+              "5                                               55.0                                                                   \n",
+              "\n",
+              "   Children with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016  \\\n",
+              "0                                               42.0                                                                                 \n",
+              "1                                                NaN                                                                                 \n",
+              "2                                               33.0                                                                                 \n",
+              "3                                                NaN                                                                                 \n",
+              "5                                               43.0                                                                                 \n",
+              "\n",
+              "   Children sleeping under treated bed nets: % of children under age 5: 2009-2016  \\\n",
+              "0                                                NaN                                \n",
+              "1                                               25.9                                \n",
+              "2                                               72.7                                \n",
+              "3                                                NaN                                \n",
+              "5                                               53.8                                \n",
+              "\n",
+              "   Children with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016  \\\n",
+              "0                                                NaN                                                   \n",
+              "1                                               28.3                                                   \n",
+              "2                                               25.9                                                   \n",
+              "3                                                NaN                                                   \n",
+              "5                                               25.4                                                   \n",
+              "\n",
+              "   Tuberculosis: Treatment success rate: % of new cases: 2014  \\\n",
+              "0                                               88.0            \n",
+              "1                                               34.0            \n",
+              "2                                               89.0            \n",
+              "3                                               77.0            \n",
+              "5                                               91.0            \n",
+              "\n",
+              "   Tuberculosis: Cases detection rate: % of new estimated cases: 2015  \n",
+              "0                                               80.0                   \n",
+              "1                                               64.0                   \n",
+              "2                                               61.0                   \n",
+              "3                                               62.0                   \n",
+              "5                                               51.0                   \n",
+              "\n",
+              "[5 rows x 47 columns]"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#| code-fold: true\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "wb = pd.read_csv(\"data/world_bank.csv\", index_col=0)\n",
+        "wb = wb.replace([np.inf, -np.inf], np.nan)\n",
+        "wb.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can visualize the distribution of the `Continent` column using a bar plot. There are a few ways to do this.\n",
+        "\n",
+        "### Plotting in Pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "wb['Continent'].value_counts().plot(kind='bar');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Recall that `.value_counts()` returns a `Series` with the total count of each unique value. We call `.plot(kind='bar')` on this result to visualize these counts as a bar plot.\n",
+        "\n",
+        "Plotting methods in `pandas` are the least preferred and not supported in Data 100, as their functionality is limited. Instead, future examples will focus on other libraries built specifically for visualizing data. The most well-known library here is `matplotlib`.\n",
+        "\n",
+        "### Plotting in Matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt # matplotlib is typically given the alias plt\n",
+        "\n",
+        "continent = wb['Continent'].value_counts()\n",
+        "plt.bar(continent.index, continent)\n",
+        "plt.xlabel('Continent')\n",
+        "plt.ylabel('Count');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "While more code is required to achieve the same result, `matplotlib` is often used over `pandas` for its ability to plot more complex visualizations, some of which are discussed shortly.\n",
+        "\n",
+        "However, note how we needed to label the axes with `plt.xlabel` and `plt.ylabel`, as `matplotlib` does not support automatic axis labeling. To get around these inconveniences, we can use a more efficient plotting library: `seaborn`.\n",
+        "\n",
+        "### Plotting in `Seaborn`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import seaborn as sns # seaborn is typically given the alias sns\n",
+        "sns.countplot(data = wb, x = 'Continent');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In contrast to `matplotlib`, the general structure of a `seaborn` call involves passing in an entire `DataFrame`, and then specifying what column(s) to plot. `seaborn.countplot` both counts and visualizes the number of unique values in a given column. This column is specified by the `x` argument to `sns.countplot`, while the `DataFrame` is specified by the `data` argument. \n",
+        "\n",
+        "For the vast majority of visualizations, `seaborn` is far more concise and aesthetically pleasing than `matplotlib`. However, the color scheme of this particular bar plot is arbitrary - it encodes no additional information about the categories themselves. This is not always true; color may signify meaningful detail in other visualizations. We'll explore this more in-depth during the next lecture.\n",
+        "\n",
+        "By now, you'll have noticed that each of these plotting libraries have a very different syntax. As with `pandas`, we'll teach you the important methods in `matplotlib` and `seaborn`, but you'll learn more through documentation.\n",
+        "\n",
+        "1. [Matplotlib Documentation](https://matplotlib.org/stable/index.html)\n",
+        "2. [Seaborn Documentation](https://seaborn.pydata.org/)\n",
+        "\n",
+        "## Distributions of Quantitative Variables\n",
+        "\n",
+        "Revisiting our example with the `wb` DataFrame, let's plot the distribution of `Gross national income per capita`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Continent</th>\n",
+              "      <th>Country</th>\n",
+              "      <th>Primary completion rate: Male: % of relevant age group: 2015</th>\n",
+              "      <th>Primary completion rate: Female: % of relevant age group: 2015</th>\n",
+              "      <th>Lower secondary completion rate: Male: % of relevant age group: 2015</th>\n",
+              "      <th>Lower secondary completion rate: Female: % of relevant age group: 2015</th>\n",
+              "      <th>Youth literacy rate: Male: % of ages 15-24: 2005-14</th>\n",
+              "      <th>Youth literacy rate: Female: % of ages 15-24: 2005-14</th>\n",
+              "      <th>Adult literacy rate: Male: % ages 15 and older: 2005-14</th>\n",
+              "      <th>Adult literacy rate: Female: % ages 15 and older: 2005-14</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Access to improved sanitation facilities: % of population: 1990</th>\n",
+              "      <th>Access to improved sanitation facilities: % of population: 2015</th>\n",
+              "      <th>Child immunization rate: Measles: % of children ages 12-23 months: 2015</th>\n",
+              "      <th>Child immunization rate: DTP3: % of children ages 12-23 months: 2015</th>\n",
+              "      <th>Children with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016</th>\n",
+              "      <th>Children with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016</th>\n",
+              "      <th>Children sleeping under treated bed nets: % of children under age 5: 2009-2016</th>\n",
+              "      <th>Children with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016</th>\n",
+              "      <th>Tuberculosis: Treatment success rate: % of new cases: 2014</th>\n",
+              "      <th>Tuberculosis: Cases detection rate: % of new estimated cases: 2015</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Algeria</td>\n",
+              "      <td>106.0</td>\n",
+              "      <td>105.0</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>83.0</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>80.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>82.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>25.9</td>\n",
+              "      <td>28.3</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>64.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Benin</td>\n",
+              "      <td>83.0</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>79.0</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>72.7</td>\n",
+              "      <td>25.9</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>61.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Botswana</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>101.0</td>\n",
+              "      <td>86.0</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>99.0</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>63.0</td>\n",
+              "      <td>97.0</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>62.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Africa</td>\n",
+              "      <td>Burundi</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>88.0</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>85.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>53.8</td>\n",
+              "      <td>25.4</td>\n",
+              "      <td>91.0</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 47 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Continent   Country  \\\n",
+              "0    Africa   Algeria   \n",
+              "1    Africa    Angola   \n",
+              "2    Africa     Benin   \n",
+              "3    Africa  Botswana   \n",
+              "5    Africa   Burundi   \n",
+              "\n",
+              "   Primary completion rate: Male: % of relevant age group: 2015  \\\n",
+              "0                                              106.0              \n",
+              "1                                                NaN              \n",
+              "2                                               83.0              \n",
+              "3                                               98.0              \n",
+              "5                                               58.0              \n",
+              "\n",
+              "   Primary completion rate: Female: % of relevant age group: 2015  \\\n",
+              "0                                              105.0                \n",
+              "1                                                NaN                \n",
+              "2                                               73.0                \n",
+              "3                                              101.0                \n",
+              "5                                               66.0                \n",
+              "\n",
+              "   Lower secondary completion rate: Male: % of relevant age group: 2015  \\\n",
+              "0                                               68.0                      \n",
+              "1                                                NaN                      \n",
+              "2                                               50.0                      \n",
+              "3                                               86.0                      \n",
+              "5                                               35.0                      \n",
+              "\n",
+              "   Lower secondary completion rate: Female: % of relevant age group: 2015  \\\n",
+              "0                                               85.0                        \n",
+              "1                                                NaN                        \n",
+              "2                                               37.0                        \n",
+              "3                                               87.0                        \n",
+              "5                                               30.0                        \n",
+              "\n",
+              "   Youth literacy rate: Male: % of ages 15-24: 2005-14  \\\n",
+              "0                                               96.0     \n",
+              "1                                               79.0     \n",
+              "2                                               55.0     \n",
+              "3                                               96.0     \n",
+              "5                                               90.0     \n",
+              "\n",
+              "   Youth literacy rate: Female: % of ages 15-24: 2005-14  \\\n",
+              "0                                               92.0       \n",
+              "1                                               67.0       \n",
+              "2                                               31.0       \n",
+              "3                                               99.0       \n",
+              "5                                               88.0       \n",
+              "\n",
+              "   Adult literacy rate: Male: % ages 15 and older: 2005-14  \\\n",
+              "0                                               83.0         \n",
+              "1                                               82.0         \n",
+              "2                                               41.0         \n",
+              "3                                               87.0         \n",
+              "5                                               89.0         \n",
+              "\n",
+              "   Adult literacy rate: Female: % ages 15 and older: 2005-14  ...  \\\n",
+              "0                                               68.0          ...   \n",
+              "1                                               60.0          ...   \n",
+              "2                                               18.0          ...   \n",
+              "3                                               89.0          ...   \n",
+              "5                                               85.0          ...   \n",
+              "\n",
+              "   Access to improved sanitation facilities: % of population: 1990  \\\n",
+              "0                                               80.0                 \n",
+              "1                                               22.0                 \n",
+              "2                                                7.0                 \n",
+              "3                                               39.0                 \n",
+              "5                                               42.0                 \n",
+              "\n",
+              "   Access to improved sanitation facilities: % of population: 2015  \\\n",
+              "0                                               88.0                 \n",
+              "1                                               52.0                 \n",
+              "2                                               20.0                 \n",
+              "3                                               63.0                 \n",
+              "5                                               48.0                 \n",
+              "\n",
+              "   Child immunization rate: Measles: % of children ages 12-23 months: 2015  \\\n",
+              "0                                               95.0                         \n",
+              "1                                               55.0                         \n",
+              "2                                               75.0                         \n",
+              "3                                               97.0                         \n",
+              "5                                               93.0                         \n",
+              "\n",
+              "   Child immunization rate: DTP3: % of children ages 12-23 months: 2015  \\\n",
+              "0                                               95.0                      \n",
+              "1                                               64.0                      \n",
+              "2                                               79.0                      \n",
+              "3                                               95.0                      \n",
+              "5                                               94.0                      \n",
+              "\n",
+              "   Children with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016  \\\n",
+              "0                                               66.0                                                                   \n",
+              "1                                                NaN                                                                   \n",
+              "2                                               23.0                                                                   \n",
+              "3                                                NaN                                                                   \n",
+              "5                                               55.0                                                                   \n",
+              "\n",
+              "   Children with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016  \\\n",
+              "0                                               42.0                                                                                 \n",
+              "1                                                NaN                                                                                 \n",
+              "2                                               33.0                                                                                 \n",
+              "3                                                NaN                                                                                 \n",
+              "5                                               43.0                                                                                 \n",
+              "\n",
+              "   Children sleeping under treated bed nets: % of children under age 5: 2009-2016  \\\n",
+              "0                                                NaN                                \n",
+              "1                                               25.9                                \n",
+              "2                                               72.7                                \n",
+              "3                                                NaN                                \n",
+              "5                                               53.8                                \n",
+              "\n",
+              "   Children with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016  \\\n",
+              "0                                                NaN                                                   \n",
+              "1                                               28.3                                                   \n",
+              "2                                               25.9                                                   \n",
+              "3                                                NaN                                                   \n",
+              "5                                               25.4                                                   \n",
+              "\n",
+              "   Tuberculosis: Treatment success rate: % of new cases: 2014  \\\n",
+              "0                                               88.0            \n",
+              "1                                               34.0            \n",
+              "2                                               89.0            \n",
+              "3                                               77.0            \n",
+              "5                                               91.0            \n",
+              "\n",
+              "   Tuberculosis: Cases detection rate: % of new estimated cases: 2015  \n",
+              "0                                               80.0                   \n",
+              "1                                               64.0                   \n",
+              "2                                               61.0                   \n",
+              "3                                               62.0                   \n",
+              "5                                               51.0                   \n",
+              "\n",
+              "[5 rows x 47 columns]"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#| code-fold: true\n",
+        "wb.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "How should we define our categories for this variable? In the previous example, these were a few unique values of the `Continent` column. If we use similar logic here, our categories are the different numerical values contained in the `Gross national income per capita` column.\n",
+        "\n",
+        "Under this assumption, let's plot this distribution using the `seaborn.countplot` function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.countplot(data = wb, x = 'Gross national income per capita, Atlas method: $: 2016');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "What happened? A bar plot (either `plt.bar` or `sns.countplot`) will create a separate bar for each unique value of a variable. With a continuous variable, we may not have a finite number of possible values, which can lead to situations like above where we would need many, many bars to display each unique value.\n",
+        "\n",
+        "Specifically, we can say this histogram suffers from **overplotting** as we are unable to interpret the plot and gain any meaningful insight. \n",
+        "\n",
+        "Rather than bar plots, to visualize the distribution of a continuous variable, we use one of the following types of plots:\n",
+        "\n",
+        "- Histogram\n",
+        "- Box plot\n",
+        "- Violin plot\n",
+        "\n",
+        "### Box Plots and Violin Plots\n",
+        "\n",
+        "Box plots and violin plots are two very similar kinds of visualizations. Both display the distribution of a variable using information about **quartiles**.\n",
+        "\n",
+        "In a box plot, the width of the box at any point does not encode meaning. In a violin plot, the width of the plot indicates the density of the distribution at each possible value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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CgcDAQISGhiIwMBAKhQJbt26Fh4cHAgICTB2ViKpRlYqmrKws3Lt3T+fYvXv3kJWVZZBQRESmZG1tjTFjxiAxMRHTp0/X2dM0ffp0JCYmYvTo0WxsSWRhqtRywMrKCm3atMGVK1ekY88//zyuXbtmsQ+zZMsBotrnWc+e8/DwwOjRo/kIFaJawuDPnnvasWPH4ODgoHNs8+bNKCoqqsrpiIjMUkhICLp27VppTxNXmIgsE5tbGghXmoiIiGoeNrckIiIiMjC9Ls9V3DGij/Pnz/+lQERERETmSK+i6c0335S+LykpwapVq+Dr64vg4GAAwOnTp5GWloYxY8YYJSQRERGRqelVNEVHR0vfjxgxAuPHj8fs2bMrzbl9+7Zh0xERERGZCdkbwZ2cnHDu3Dm0atVK5/jPP/+MTp06IT8/36ABawpuBCciIqp5jLoR3N7eHidPnqx0/OTJk7Czs5N7OiIiIqIaQXafpo8++gijR4/G+fPn0blzZwBAUlIS1q9fjxkzZhg8IBEREZE5kF00TZkyBc2bN8fy5cuxZcsWAI+7gW/YsAEDBgwweEAiIiIic8DmlgbCPU1EREQ1j9EfowIAycnJSE9PBwD4+fkhMDCwqqciIiIiMnuyi6bc3FwMGjQIP/74I5ydnQEAKpUKr7zyCrZv346//e1vhs5IREREZHKy754bN24cCgoKkJaWhry8POTl5SE1NRVqtRrjx483RkYiIiIik6tSn6YjR47ghRde0Dl+5swZ9OzZEyqVypD5agzuaSIiIqp5jNqnSavVwsbGptJxGxsbaLVauacjIiIiqhFkF02vvvoqJkyYgLt370rH7ty5g8jISISGhho0HBEREZG5kF00rVixAmq1Gs2aNUOLFi3QokUL+Pj4QK1W4/PPPzdGRiIiIiKTk333XNOmTXH+/HkcOXIEV69eBfC4uWWPHj0MHo6IiIjIXLC5pYFwIzgREVHNY/TmlvHx8YiPj0dubm6lzd/r16+vyimJiIiIzJrsoikmJgazZs1Cp06d4OHhAYVCYYxcRERERGZFdtH0xRdfYOPGjXj33XeNkYeIiIjILMm+e66srAxdunQxRhYiIiIisyW7aBoxYgS+/vprY2QhIiIiMlt6XZ6LioqSvtdqtVizZg2OHDmCgICASt3BlyxZYtiERERERGZAr6LpwoULOj+3b98eAJCammrwQERERETmSK+i6dixY8bOQURERGTWZO9pGjZsGAoKCiodLywsxLBhwwwSioiIiMjcyC6aNm3ahOLi4krHi4uLsXnzZoOEIiIiIjI3evdpUqvVEEJACIGCggLY2dlJYxqNBgcOHECjRo2MEpKIiIjI1PQumpydnaFQKKBQKNC6detK4wqFAjExMQYNR0RERGQu9C6ajh07BiEEXn31VezatQuurq7SmFKphLe3Nzw9PY0SkoiIiMjU9C6aunfvDgDIzMyEl5cXnzlHREREFkX2RnBvb2+cOHEC77zzDrp06YI7d+4AAL766iucOHHC4AGJiIiIzIHsomnXrl3o1asX7O3tcf78eZSWlgIA8vPzMW/ePIMHJCIiIjIHsoumOXPm4IsvvsDatWt1HqHStWtXnD9/3qDhiIiIiMyF7KIpIyMDISEhlY47OTlBpVIZIhMRERGR2ZFdNLm7u+P69euVjp84cQLNmzc3SCgiIiIicyO7aBo5ciQmTJiApKQkKBQK3L17F1u3bsWkSZMwevRoY2QkIiIiMjm9Ww5UmDJlCrRaLUJDQ1FUVISQkBDY2tpi0qRJGDdunDEyEhEREZmcQgghqvKLZWVluH79On7//Xf4+vqiXr16hs5Wo6jVajg5OSE/Px+Ojo6mjkNERER6kPP5LXulqYJSqYSvr29Vf52IiIioRpFdNJWUlODzzz/HsWPHkJubC61WqzPOtgNERERUG8kumoYPH44ffvgB//znP9G5c2c+ToWIiIgsguyiad++fThw4AC6du1qjDxEREREZkl2y4HGjRujfv36xshCREREZLZkF02LFy/GJ598glu3bhkjDxEREZFZkn15rlOnTigpKUHz5s3h4OCg8/w5AMjLyzNYOCIiIiJzIbtoGjx4MO7cuYN58+bBzc2NG8GJiIjIIsgumk6dOoXExES0a9fOGHmIiIiIzJLsPU1t2rRBcXGxMbIQERERmS3ZRdOCBQswceJE/Pjjj3jw4AHUarXOFxEREVFtJPvZc1ZWj+usp/cyCSGgUCig0WgMl64G4bPniIiIah6jPnvu2LFjVQ5GREREVFPJvjzXvXv3P/2S686dO3jnnXfQoEED2Nvbw9/fH+fOnZPGhRCYOXMmPDw8YG9vjx49euDnn3/WOUdeXh7Cw8Ph6OgIZ2dnDB8+HL///rvOnJSUFHTr1g12dnZo2rQpYmNjK2XZuXMn2rRpAzs7O/j7++PAgQOy3w8RERHVTrKLJkN6+PAhunbtChsbG3z//fe4cuUKFi9eDBcXF2lObGwsPvvsM3zxxRdISkpC3bp10atXL5SUlEhzwsPDkZaWhsOHD2Pfvn1ISEjAqFGjpHG1Wo2ePXvC29sbycnJWLhwIT799FOsWbNGmnPq1CkMHjwYw4cPx4ULF/Dmm2/izTffRGpqavX8wyAiIiLzJkzok08+ES+99NIfjmu1WuHu7i4WLlwoHVOpVMLW1lZs27ZNCCHElStXBABx9uxZac73338vFAqFuHPnjhBCiFWrVgkXFxdRWlqq89rPPfec9POAAQNEWFiYzusHBQWJDz74QK/3kp+fLwCI/Px8veYTERGR6cn5/DbpStN3332HTp06oX///mjUqBECAwOxdu1aaTwzMxPZ2dno0aOHdMzJyQlBQUFITEwEACQmJsLZ2RmdOnWS5vTo0QNWVlZISkqS5oSEhECpVEpzevXqhYyMDDx8+FCa8+TrVMypeJ2nlZaW8s5BIiIiC2LSounGjRtYvXo1WrVqhUOHDmH06NEYP348Nm3aBADIzs4GALi5uen8npubmzSWnZ2NRo0a6YzXqVMHrq6uOnOedY4nX+OP5lSMP23+/PlwcnKSvpo2bSr7/RMREVHNIbtoKi4uRlFRkfTzrVu3sGzZMvzwww+yX1yr1aJDhw6YN28eAgMDMWrUKIwcORJffPGF7HNVt6lTpyI/P1/6un37tqkjERERkRHJLpr69u2LzZs3AwBUKhWCgoKwePFi9O3bF6tXr5Z1Lg8PD/j6+uoce/7555GVlQUAcHd3BwDk5OTozMnJyZHG3N3dkZubqzP+6NEj5OXl6cx51jmefI0/mlMx/jRbW1s4OjrqfBEREVHtJbtoOn/+PLp16wYA+Oabb+Dm5oZbt25h8+bN+Oyzz2Sdq2vXrsjIyNA5du3aNXh7ewMAfHx84O7ujvj4eGlcrVYjKSkJwcHBAIDg4GCoVCokJydLc44ePQqtVougoCBpTkJCAsrLy6U5hw8fxnPPPSfdqRccHKzzOhVzKl6HiIiILJzcXeb29vbi1q1bQggh+vfvLz799FMhhBBZWVnC3t5e1rnOnDkj6tSpI+bOnSt+/vlnsXXrVuHg4CC2bNkizVmwYIFwdnYW3377rUhJSRF9+/YVPj4+ori4WJrz2muvicDAQJGUlCROnDghWrVqJQYPHiyNq1Qq4ebmJt59912Rmpoqtm/fLhwcHMR//vMfac7JkydFnTp1xKJFi0R6erqIjo4WNjY24vLly3q9F949R0REVPPI+fyWXTT5+/uL5cuXi6ysLOHo6ChOnTolhBDi3Llzws3NTXbYvXv3irZt2wpbW1vRpk0bsWbNGp1xrVYrZsyYIdzc3IStra0IDQ0VGRkZOnMePHggBg8eLOrVqyccHR3F0KFDRUFBgc6cS5cuiZdeeknY2tqKxo0biwULFlTKEhcXJ1q3bi2USqXw8/MT+/fv1/t9sGgiIiKqeeR8fst+9tw333yDf/3rX9BoNAgNDZU2gM+fPx8JCQn4/vvvDb4aVhPw2XNEREQ1j5zPb9lFE/D49vx79+6hXbt20gN8z5w5A0dHR7Rp06ZqqWs4Fk1EtZNGo0FKSgry8vLg6uqKgIAAWFtbmzoWERmIUR/YCzy+0+zpu8o6d+5clVMREZmthIQErFq1Sqdfm7u7O8aMGYOQkBATJiMiU6jSStO5c+cQFxeHrKwslJWV6Yzt3r3bYOFqEq40EdUuCQkJiI6OhlKpRGlpqXTc1tYWZWVliImJYeFEVAvI+fyW3XJg+/bt6NKlC9LT07Fnzx6Ul5cjLS0NR48ehZOTU5VDExGZC41GgyVLlkAIgQ4dOmDlypU4cOAAVq5ciQ4dOkAIgaVLl0Kj0Zg6KhFVI9lF07x587B06VLs3bsXSqUSy5cvx9WrVzFgwAB4eXkZIyMRUbW6ePEiVCoV/P39MXfuXPj5+cHBwQF+fn6YO3cu/P398fDhQ1y8eNHUUYmoGskumn755ReEhYUBAJRKJQoLC6FQKBAZGYk1a9YYPCARUXWrKIbef/996WaXClZWVnj//fd15hGRZZBdNLm4uKCgoAAA0LhxY6SmpgJ4/EiVJ59JR0RU0ykUClNHICIzIrtoCgkJweHDhwEA/fv3x4QJEzBy5EgMHjwYoaGhBg9IRFTd2rdvDwDYsGEDtFqtzphWq8XGjRt15hGRZZB991xeXh5KSkrg6ekJrVaL2NhYnDp1Cq1atcL06dOlZ7lZGt49R1R7aDQa9OvXDyqVCsHBwXjnnXfg4+ODzMxMbNmyBYmJiXB2dsauXbvYs4mohjN6c0uqjEUTUe2SkJCAmTNnwtbWtlLLgdLSUsyaNYstB4hqAYMXTWq1Wu8Xt9SCgUUTUe2TkJCAFStWIDc3Vzrm5uaGiIgIFkxEtYTBO4I7Ozv/zw2RQggoFAr2LSGiWoWbwYmogl5F07Fjx4ydg4jIrDx5ee5JKpUKM2fO5OU5IgukV9HUvXt36fusrCw0bdq00l9fQgjcvn3bsOmIiEygoiM4AHTo0OGZG8GXLFmCrl27ciM4kQWR3XLAx8cHv/32W6XjeXl58PHxMUgoIiJT0qcjuEqlYnNLIgsju2iq2Lv0tN9//x12dnYGCUVEZEoVxdDQoUPZEZyIJHpdngOAqKgoAI83Rc6YMQMODg7SmEajQVJSEhu9EVGtwo4sRPQkvYumCxcuAHj8H5HLly9DqVRKY0qlEu3atcOkSZMMn5CIqJq1b98eX331FTZu3IjAwECd1SZ2BCeyXHoXTRV30A0dOhTLly9/Zi8Dthsgotqgffv2cHZ2xuXLl/Hvf/8bQUFBUlPLpKQkXL58GS4uLiyaiCyMQTqCX7t2DV9++SU2b96Me/fuGSJXjcPmlkS1S0XLgT/ClgNEtYOcz2/ZG8ErFBUVYcOGDejWrRt8fX1x/Phxad8TEVFt8XSfpqd/JiLLoffluQqnT5/GunXrsHPnTnh5eSE9PR3Hjh1Dt27djJGPiKjaaTQarFq1Cl26dMHMmTOxd+9e3L17F56enujduzdmzZqF1atXs08TkYXRu2havHgx1q9fj/z8fAwePBgJCQlo164dbGxs0KBBA2NmJCKqVikpKcjOzkbv3r3x/vvvIzs7WxrbtWsX/vGPf+DUqVNISUlBYGCgCZMSUXXSu2j65JNP8Mknn2DWrFn8y4qIarW8vDwAwNq1a9GlSxfMmDFD6gi+detWrFu3TmceEVkGvfc0zZ49Gzt37oSPjw8++eQTpKamGjMXEZHJODs7AwD8/f0xZ84cnY7gc+bMgb+/v848IrIMehdNU6dOxbVr1/DVV18hOzsbQUFBaNeuHYQQePjwoTEzEhEREZmc7Lvnunfvjk2bNiE7OxtjxoxBx44d0b17d3Tp0kV6wCURUU2mUqkAAKmpqZg+fTrS0tJQVFSEtLQ0TJ8+XVppr5hHRJahyi0H6tevjw8++ABJSUm4cOECOnfujAULFhgyGxGRSbi6ugIARowYgRs3biAiIgJvvPEGIiIikJmZiREjRujMIyLLYJDmlhXKy8thY2NjqNPVKGxuSVR7aDQahIeHo3nz5oiJiUFqairy8vLg6uqKtm3bIjo6GpmZmdiyZQtvjCGq4aqlueWzWGrBRES1i7W1NcaMGYPExERER0dDqVQiODgYSqUS0dHRSExMxOjRo1kwEVkYg640WTKuNBHVPgkJCVi1apVOnyYPDw+MHj2aj1AhqiXkfH6zaDIQFk1EtZNGo0FKSop0eS4gIIArTES1iJzPb9mPUSEisiTW1tbs+k1EAAy8p4mIiIiotjJo0WRlZYVXX30VycnJhjwtERERkckZtGhav349QkJCEBERYcjTEhEREZkcN4IbCDeCExER1Twm69NEREREVFtV6e65c+fOIS4uDllZWSgrK9MZ2717t0GCEREREZkT2StN27dvR5cuXZCeno49e/agvLwcaWlpOHr0KJycnIyRkYiIiMjkZBdN8+bNw9KlS7F3714olUosX74cV69exYABA+Dl5WWMjEREREQmJ7to+uWXXxAWFgYAUCqVKCwshEKhQGRkJNasWWPwgERERETmQHbR5OLigoKCAgBA48aNkZqaCgBQqVQoKioybDoiIiIiMyF7I3hISAgOHz4Mf39/9O/fHxMmTMDRo0dx+PBhhIaGGiMjERERkcnJLppWrFiBkpISAMC0adNgY2ODU6dOoV+/fpg+fbrBAxIRmRIf2EtEFdjc0kDY3JKo9klISMCqVauQnZ0tHXN3d8eYMWMQEhJiwmREZChGbW5pbW2N3NzcSscfPHjAv76IqNZISEhAdHQ0mjdvjpUrV+LAgQNYuXIlmjdvjujoaCQkJJg6IhFVM9lF0x8tTJWWlkKpVP7lQEREpqbRaLBq1SoEBwdjzpw58PPzg4ODA/z8/DBnzhwEBwdj9erV0Gg0po5KRNVI7z1Nn332GQBAoVBg3bp1qFevnjSm0WiQkJCANm3aGD4hEVE1S0lJQXZ2NmbMmAEhBC5cuKCzpyk8PBwRERFISUlBYGCgqeMSUTXRu2haunQpgMcrTV988YXOpTilUolmzZrhiy++MHxCIqJqlpeXBwC4e/cuZs+eXWlP0/Dhw3XmEZFl0LtoyszMBAC88sor2L17N1xcXIwWiojIlFxdXQEAc+fORXBwMAYOHAhbW1uUlpbizJkzmDt3rs48IrIMslsOHDt2zBg5iIjMhp+fH6ytrWFnZ4cbN24gMTFRGnNzc0PdunVRUlICPz8/E6YkouqmV9EUFRWF2bNno27duoiKivrTuUuWLDFIMCIiU0lLS4NGo0FhYSGUSiUmTpyI4OBgJCYmYv369SgsLJTmcU8TkeXQq2i6cOECysvLpe//iEKhMEwqIiITun//PgCgVatWKCgowOLFi6UxDw8PtGrVCj///LM0j4gsg15F05OX5Hh5johqO5VKBQDo27cvXn/99UodwQ8cOIDFixdL84jIMsju0/Sk27dv4/bt24bKQkRkFpydnQEAP/30E8rLy3H9+nWkpqbi+vXrKC8vx4kTJ3TmEZFlkL0R/NGjR4iJicFnn32G33//HQBQr149jBs3DtHR0bCxsTF4SCKi6tSwYUMAQFJSEl577TWdsZUrV1aaR0SWQXbRNG7cOOzevRuxsbEIDg4GACQmJuLTTz/FgwcPsHr1aoOHJCKqTgEBAXBwcEBRUREUCoXOkxCsrKyg1Wrh4OCAgIAAE6Ykouomu2j6+uuvsX37drz++uvSsYCAADRt2hSDBw9m0URENZ5Go0FJSQkAoHPnznjxxRelPk2nT59GUlISSkpKoNFo+MxNIgsiu2iytbVFs2bNKh338fHhs+eIqFb49ttvodVq0adPH5w5cwZJSUnSmIeHB3r37o29e/fi22+/Rf/+/U2YlIiqk+yiaezYsZg9ezY2bNgAW1tbAI8f1jt37lyMHTvW4AGJiKrb3bt3AQBDhgzB2LFj8e233+Lu3bvw9PRE3759oVarsXfvXmkeEVkG2UXThQsXEB8fjyZNmqBdu3YAgEuXLqGsrAyhoaF4++23pbm7d+82XFIiomri6ekJANi0aRPOnDmj8+y5Xbt2oXPnzjrziMgyKMSTOxz1MHToUL3nbtiwQXagmkqtVsPJyQn5+flwdHQ0dRwi+gvKysrw2muvQavVIigoCC+++CLs7OxQUlIi7WmysrLCwYMHuS2BqIaT8/kte6XJkgohIrJMFc+dKyoqqrSnqeLJB/b29twETmRh/lJzSyKi2iglJQVFRUUAgKcX4yt+LiwsREpKSrVnIyLTkb3SBADffPMN4uLikJWVhbKyMp2x8+fPGyQYEZGpPPlMuaCgIDRp0gSlpaWwtbXFr7/+Kq088dlzRJZFdtH02WefYdq0aXj//ffx7bffYujQofjll19w9uxZREREGCMjEVG1ysvLAwC0aNEC8+fPh5XV/y3Ka7VajBgxAjdu3JDmEZFlkH15btWqVVizZg0+//xzKJVKTJ48GYcPH8b48eORn59vjIxERNVKrVYDgNRW5Wl2dnY684jIMsheacrKykKXLl0APN4IWVBQAAB499138eKLL2LFihWGTUhEVM0qVpbS09Mxbdo0dO7cWeoIfubMGaSnp+vMIyLLILtocnd3R15eHry9veHl5YXTp0+jXbt2yMzMrLRhkoioJmrfvj2++uorNGjQAElJSUhMTJTGrK2t0aBBA9y/fx/t27c3XUgiqnayi6ZXX30V3333HQIDAzF06FBERkbim2++wblz53QaWxIR1VTt27eHg4MD7t+/D2dnZ/Ts2ROenp64e/cufvjhB9y/fx8ODg4smogsjOy15TVr1mDatGkAgIiICKxfvx7PP/88Zs2a9Zce1rtgwQIoFAp89NFH0rGSkhJERESgQYMGqFevHvr164ecnByd38vKykJYWBgcHBzQqFEjfPzxx3j06JHOnB9//BEdOnSAra0tWrZsiY0bN1Z6/ZUrV6JZs2aws7NDUFAQzpw5U+X3QkQ1X0XTyuLiYsTFxWHZsmWIi4tDcXExgD/e70REtZfsosnKygp16vzfAtWgQYPw2WefYdy4cVXujHv27Fn85z//QUBAgM7xyMhI7N27Fzt37sTx48dx9+5dndUsjUaDsLAwlJWV4dSpU9i0aRM2btyImTNnSnMyMzMRFhaGV155BRcvXsRHH32EESNG4NChQ9KcHTt2ICoqCtHR0Th//jzatWuHXr16ITc3t0rvh4hqtpSUFKhUKowcORLOzs46Yy4uLhgxYgQePnzIPk1ElkbItH79ehEXF1fpeFxcnNi4caPc04mCggLRqlUrcfjwYdG9e3cxYcIEIYQQKpVK2NjYiJ07d0pz09PTBQCRmJgohBDiwIEDwsrKSmRnZ0tzVq9eLRwdHUVpaakQQojJkycLPz8/ndccOHCg6NWrl/Rz586dRUREhPSzRqMRnp6eYv78+Xq/j/z8fAFA5Ofn6//micgsHTlyRHTv3l0UFhaK0tJSERcXJ5YtWybi4uJEaWmpKCwsFN27dxdHjhwxdVQi+ovkfH7LXmmaP38+GjZsWOl4o0aNMG/ePNlFW0REBMLCwtCjRw+d48nJySgvL9c53qZNG3h5eUmbMhMTE+Hv7w83NzdpTq9evaBWq5GWlibNefrcvXr1ks5RVlaG5ORknTlWVlbo0aOHzubPp5WWlkKtVut8EVHt4OrqCgDYs2cP3nvvPaxcuRJ79uzBypUr8d5772HPnj0684jIMlSp5YCPj0+l497e3sjKypJ1ru3bt+P8+fM4e/ZspbHs7GwolcpKS+Nubm7SE8ezs7N1CqaK8YqxP5ujVqtRXFyMhw8fQqPRPHPO1atX/zD7/PnzERMTo98bJaIaJSAgAM7Ozli7dm2lvUsPHz7E2rVr4ezsXGlLARHVbrJXmho1avTM6/iXLl1CgwYN9D7P7du3MWHCBGzdulVqFFeTTJ06Ffn5+dLX7du3TR2JiAyovLwcwON+dJMmTcKuXbswadIk2Nvb64wTkeWQvdI0ePBgjB8/HvXr10dISAgA4Pjx45gwYQIGDRqk93mSk5ORm5uLDh06SMc0Gg0SEhKwYsUKHDp0CGVlZVCpVDqrTTk5OXB3dwfwuGfU03e5Vdxd9+Scp++4y8nJgaOjo/SUcmtr62fOqTjHs9ja2vLuGaJa6uLFiygsLISXlxdKS0uxaNEiaczd3R1eXl7IysrCxYsX0bFjRxMmJaLqJHulafbs2QgKCkJoaCjs7e1hb2+Pnj174tVXX5W1pyk0NBSXL1/GxYsXpa9OnTohPDxc+t7Gxgbx8fHS72RkZCArKwvBwcEAgODgYFy+fFnnLrfDhw/D0dERvr6+0pwnz1Exp+IcSqUSHTt21Jmj1WoRHx8vzSEiy3Lx4kUAwIQJE/DVV18hIiICb731FiIiIrB582ZMmDBBZx4RWQbZK01KpRI7duzAnDlzcPHiRdjb28Pf3x/e3t6yzlO/fn20bdtW51jdunXRoEED6fjw4cMRFRUFV1dXODo6Yty4cQgODsaLL74IAOjZsyd8fX3x7rvvIjY2FtnZ2Zg+fToiIiKkVaAPP/wQK1aswOTJkzFs2DAcPXoUcXFx2L9/v/S6UVFRGDJkCDp16oTOnTtj2bJlKCwsxNChQ+X+4yGiWuTy5ctYuHChtEcSAHbt2oVevXqZMBURmYrsoqlCq1at0KpVK0NmqWTp0qWwsrJCv379UFpail69emHVqlXSuLW1Nfbt24fRo0cjODgYdevWxZAhQzBr1ixpjo+PD/bv34/IyEgsX74cTZo0wbp163T+ozdw4ED89ttvmDlzJrKzs9G+fXscPHiw0uZwIrIMFY9R2bhxI4KDgzFjxgz4+PggMzMTW7ZswaZNm6R5RGQ5FELwgXGGoFar4eTkhPz8fDg6Opo6DhH9BWVlZXjttdeg1WoRHByMd955R6doSkxMhJWVFQ4ePFjlpr5EZB7kfH5XeaWJiKi2SktLg1arBQCcP39ep2dbxaV/rVaLtLQ0BAYGmiQjEVU/2RvBiYhqu7y8PADAtGnTnvkYlYrnb1bMIyLLIGul6dGjR5g3bx6GDRuGJk2aGCsTEZFJVXT69vT0xNdff42UlBTk5eXB1dUVAQEBUuNbdgQnsiyyVprq1KmDhQsX4tGjR8bKQ0RkcgEBAXB3d8fWrVuly3QVtFottm7dCg8PD3YEJ7Iwsvc0vfrqqzh+/DiaNWtmhDhERKZnbW2NMWPGIDo6Gv/4xz9QWloqjdna2qKsrAwxMTGwtrY2YUoiqm6yi6bXX38dU6ZMweXLl9GxY0fUrVtXZ7xPnz4GC0dEZEp/dHMxbzomskyyWw5YWf3xFT2FQgGNRvOXQ9VEbDlAVHtoNBqEh4ejefPmiImJQWpqqrSnqW3btoiOjpbaD3C1iahmk/P5LfvuOa1W+4dfllowEVHtkpKSguzsbISHh8PGxgaBgYEIDQ1FYGAgbGxsEB4ejnv37j3z4eVEVHv9pZYDJSUlhspBRGQ2KloJ+Pj4PHO84jhbDhBZFtlFk0ajwezZs9G4cWPUq1cPN27cAADMmDEDX375pcEDEhFVt4pWApmZmc8crzjOlgNElkV20TR37lxs3LgRsbGxOo8PaNu2LdatW2fQcEREpsCWA0T0LLKLps2bN2PNmjUIDw/X2QDZrl07qeEbEVFNVtFyIDExEdOnT0daWhqKioqQlpaG6dOnIzExEaNHj+YmcCILI7vlwJ07d9CyZctKx7VaLcrLyw0SiojI1EJCQhATE4NVq1YhIiJCOu7h4YGYmBiEhISYMB0RmYLsosnX1xc//fQTvL29dY5/8803fHAlEdUqISEh6Nq1a6XHqHCFicgyyS6aZs6ciSFDhuDOnTvQarXYvXs3MjIysHnzZuzbt88YGYmITMba2pp/EBIRgCrsaerbty/27t2LI0eOoG7dupg5cybS09Oxd+9e/P3vfzdGRiIiIiKTk90RnJ6NHcGJiIhqHjmf37Ivz1U4d+4c0tPTATze59SxY8eqnoqIiIjI7Mkumn799VcMHjwYJ0+ehLOzMwBApVKhS5cu2L59O5o0aWLojEREREQmJ3tP04gRI1BeXo709HTk5eUhLy8P6enp0Gq1GDFihDEyEhEREZmc7D1N9vb2OHXqVKW7SZKTk9GtWzcUFRUZNGBNwT1NRERENY+cz2/ZK01NmzZ9ZhNLjUYDT09PuacjIiIiqhFkF00LFy7EuHHjcO7cOenYuXPnMGHCBCxatMig4YiIiIjMhezLcy4uLigqKsKjR49Qp87jfeQV39etW1dnbl5enuGSmjleniMiIqp5jNpyYNmyZVXNRURERFRjyS6ahgwZYowcRERERGatys0tici4SkpKkJWVZeoYRGbJy8sLdnZ2po5BFoZFE5GZysrKwqhRo0wdg8gsrVmzBq1btzZ1DLIwLJqIzJSXlxfWrFlj6hgE4NatW5g7dy6mTZsGb29vU8chPP73g6i6sWgiMlN2dnb8S9rMeHt78/8TIgsmu09ThevXr+PQoUMoLi4GAMjsXEBERERUo8gumh48eIAePXqgdevWeOONN3Dv3j0AwPDhwzFx4kSDByQiIiIyB7KLpsjISNSpUwdZWVlwcHCQjg8cOBAHDx40aDgiIiIicyF7T9MPP/yAQ4cOoUmTJjrHW7VqhVu3bhksGBEREZE5kb3SVFhYqLPCVCEvLw+2trYGCUVERERkbmQXTd26dcPmzZulnxUKBbRaLWJjY/HKK68YNBwRERGRuZB9eS42NhahoaE4d+4cysrKMHnyZKSlpSEvLw8nT540RkYiIiIik5O90tS2bVtcu3YNL730Evr27YvCwkK8/fbbuHDhAlq0aGGMjEREREQmV6Xmlk5OTpg2bZqhsxARERGZrSoVTSUlJUhJSUFubi60Wq3OWJ8+fQwSjIiIiMicyC6aDh48iPfeew/379+vNKZQKKDRaAwSjIiIiMicyN7TNG7cOPTv3x/37t2DVqvV+WLBRERERLWV7KIpJycHUVFRcHNzM0YeIiIiIrMku2j65z//iR9//NEIUYiIiIjMl+w9TStWrED//v3x008/wd/fHzY2Njrj48ePN1g4IiIiInMhu2jatm0bfvjhB9jZ2eHHH3+EQqGQxhQKBYsmIiIiqpVkF03Tpk1DTEwMpkyZAisr2Vf3iIiIiGok2VVPWVkZBg4cyIKJiIiILIrsymfIkCHYsWOHMbIQERERmS3Zl+c0Gg1iY2Nx6NAhBAQEVNoIvmTJEoOFIyIiIjIXsoumy5cvIzAwEACQmpqqM/bkpnAiIiKi2kR20XTs2DFj5CAiIiIya39pN/evv/6KX3/91VBZiIiIiMyW7KJJq9Vi1qxZcHJygre3N7y9veHs7IzZs2dDq9UaIyMRERGRyVWpT9OXX36JBQsWoGvXrgCAEydO4NNPP0VJSQnmzp1r8JBEREREpia7aNq0aRPWrVuHPn36SMcCAgLQuHFjjBkzhkUTERER1UqyL8/l5eWhTZs2lY63adMGeXl5BglFREREZG5kF03t2rXDihUrKh1fsWIF2rVrZ5BQREREROZG9uW52NhYhIWF4ciRIwgODgYAJCYm4vbt2zhw4IDBAxIRERGZA9krTd27d0dGRgbeeustqFQqqFQqvP3228jIyEC3bt2MkZGIiIjI5GSvNAFA48aNueGbiIiILIrslaYNGzZg586dlY7v3LkTmzZtMkgoIiIiInMju2iaP38+GjZsWOl4o0aNMG/ePIOEIiIiIjI3soumrKws+Pj4VDru7e2NrKwsg4QiIiIiMjeyi6ZGjRohJSWl0vFLly6hQYMGBglFREREZG5kF02DBw/G+PHjcezYMWg0Gmg0Ghw9ehQTJkzAoEGDjJGRiIiIyORk3z03e/Zs3Lx5E6GhoahT5/Gva7VavPfee9zTRERERLWW7KJJqVRix44dmD17Ni5dugR7e3v4+/vD29vbGPmIiIiIzEKV+jQBQOvWrdG6dWtDZiEiIiIyW7KLJo1Gg40bNyI+Ph65ubnQarU640ePHjVYOCIiIiJzIbtomjBhAjZu3IiwsDC0bdsWCoXCGLmIiIiIzIrsu+e2b9+OuLg47NixA8uWLcPSpUt1vuSYP38+XnjhBdSvXx+NGjXCm2++iYyMDJ05JSUliIiIQIMGDVCvXj3069cPOTk5OnOysrIQFhYGBwcHNGrUCB9//DEePXqkM+fHH39Ehw4dYGtri5YtW2Ljxo2V8qxcuRLNmjWDnZ0dgoKCcObMGVnvh4iIiGov2UWTUqlEy5YtDfLix48fR0REBE6fPo3Dhw+jvLwcPXv2RGFhoTQnMjISe/fuxc6dO3H8+HHcvXsXb7/9tjSu0WgQFhaGsrIynDp1Cps2bcLGjRsxc+ZMaU5mZibCwsLwyiuv4OLFi/joo48wYsQIHDp0SJqzY8cOREVFITo6GufPn0e7du3Qq1cv5ObmGuS9EhERUQ0nZFq0aJEYM2aM0Gq1cn/1f8rNzRUAxPHjx4UQQqhUKmFjYyN27twpzUlPTxcARGJiohBCiAMHDggrKyuRnZ0tzVm9erVwdHQUpaWlQgghJk+eLPz8/HRea+DAgaJXr17Sz507dxYRERHSzxqNRnh6eor58+frlT0/P18AEPn5+TLfNRGZu4yMDNG9e3eRkZFh6ihEZGByPr9lrzSdOHECW7duRYsWLdC7d2+8/fbbOl9/RX5+PgDA1dUVAJCcnIzy8nL06NFDmtOmTRt4eXkhMTERAJCYmAh/f3+4ublJc3r16gW1Wo20tDRpzpPnqJhTcY6ysjIkJyfrzLGyskKPHj2kOU8rLS2FWq3W+SIiIqLaS/ZGcGdnZ7z11lsGD6LVavHRRx+ha9euaNu2LQAgOzsbSqUSzs7OOnPd3NyQnZ0tzXmyYKoYrxj7szlqtRrFxcV4+PAhNBrNM+dcvXr1mXnnz5+PmJiYqr1ZIiIiqnFkF00bNmwwRg5EREQgNTUVJ06cMMr5DW3q1KmIioqSflar1WjatKkJExEREZExVbm5pSGNHTsW+/btQ0JCApo0aSIdd3d3R1lZGVQqlc5qU05ODtzd3aU5T9/lVnF33ZNznr7jLicnB46OjrC3t4e1tTWsra2fOafiHE+ztbWFra1t1d4wERER1Th67Wnq0KEDHj58CAAIDAxEhw4d/vBLDiEExo4diz179uDo0aPw8fHRGe/YsSNsbGwQHx8vHcvIyEBWVhaCg4MBAMHBwbh8+bLOXW6HDx+Go6MjfH19pTlPnqNiTsU5lEolOnbsqDNHq9UiPj5emkNERESWTa+Vpr59+0qrKm+++abBXjwiIgJff/01vv32W9SvX1/ag+Tk5AR7e3s4OTlh+PDhiIqKgqurKxwdHTFu3DgEBwfjxRdfBAD07NkTvr6+ePfddxEbG4vs7GxMnz4dERERUuYPP/wQK1aswOTJkzFs2DAcPXoUcXFx2L9/v5QlKioKQ4YMQadOndC5c2csW7YMhYWFGDp0qMHeLxEREdVgxr+Z748BeObXhg0bpDnFxcVizJgxwsXFRTg4OIi33npL3Lt3T+c8N2/eFK+//rqwt7cXDRs2FBMnThTl5eU6c44dOybat28vlEqlaN68uc5rVPj888+Fl5eXUCqVonPnzuL06dN6vxe2HCCqvdhygKj2kvP5rRBCCNOVbLWHWq2Gk5MT8vPz4ejoaOo4RGRA165dw6hRo7BmzRo+qJyolpHz+S27TxMRERGRJWLRRERERKQHFk1EREREemDRRERERKQHvVoOPNn5+n9ZsmRJlcMQERERmSu9iqYLFy7odTKFQvGXwhARERGZK72KpmPHjhk7BxEREZFZ454mIiIiIj1U6YG9586dQ1xcHLKyslBWVqYztnv3boMEIyIiIjInsleatm/fji5duiA9PR179uxBeXk50tLScPToUTg5ORkjIxEREZHJyS6a5s2bh6VLl2Lv3r1QKpVYvnw5rl69igEDBsDLy8sYGYmIiIhMTnbR9MsvvyAsLAwAoFQqUVhYCIVCgcjISKxZs8bgAYmIiIjMgeyiycXFBQUFBQCAxo0bIzU1FQCgUqlQVFRk2HREREREZkL2RvCQkBAcPnwY/v7+6N+/PyZMmICjR4/i8OHDCA0NNUZGIiIiIpOTXTStWLECJSUlAIBp06bBxsYGp06dQr9+/TB9+nSDB6Tql5OTg/z8fFPHIDIbt27d0vlfIvo/Tk5OcHNzM3WMaqEQQghTh6gN1Go1nJyckJ+fD0dHR1PHqbKcnBy88+57KC8rNXUUIiKqAWyUttjy1eYaWzjJ+fyuUp8mrVaL69evIzc3F1qtVmcsJCSkKqckM5Gfn4/yslIUN+8OrR1bSBAR0R+zKskHbhxHfn5+jS2a5JBdNJ0+fRr/+te/cOvWLTy9SKVQKKDRaAwWjkxHa+cEbd2Gpo5BRERkNmQXTR9++CE6deqE/fv3w8PDgw/pJSIiIosgu2j6+eef8c0336Bly5bGyENERERklmT3aQoKCsL169eNkYWIiIjIbMleaRo3bhwmTpyI7Oxs+Pv7w8bGRmc8ICDAYOGIiIiIzIXsoqlfv34AgGHDhknHFAoFhBDcCE5ERES1luyiKTMz0xg5iIiIiMya7KLJ29vbGDmIiIiIzFqVmlv+8ssvWLZsGdLT0wEAvr6+mDBhAlq0aGHQcERERETmQvbdc4cOHYKvry/OnDmDgIAABAQEICkpCX5+fjh8+LAxMhIRERGZnOyVpilTpiAyMhILFiyodPyTTz7B3//+d4OFIyIiIjIXslea0tPTMXz48ErHhw0bhitXrhgkFBEREZG5kV00/e1vf8PFixcrHb948SIaNWpkiExEREREZkf25bmRI0di1KhRuHHjBrp06QIAOHnyJP7f//t/iIqKMnhAIiIiInMgu2iaMWMG6tevj8WLF2Pq1KkAAE9PT3z66acYP368wQMSERERmQPZRZNCoUBkZCQiIyNRUFAAAKhfv77BgxERERGZkyr1aarAYomIiIgshV5FU4cOHRAfHw8XFxcEBgZCoVD84dzz588bLBwRERGRudCraOrbty9sbW2l7/+saCIiIiKqjfQqmqKjo6XvP/30U2NlISIiIjJbsvs0NW/eHA8ePKh0XKVSoXnz5gYJRURERGRuZBdNN2/ehEajqXS8tLQUv/76q0FCEREREZkbve+e++6776TvDx06BCcnJ+lnjUaD+Ph4+Pj4GDYdERERkZnQu2h68803ATzu0zRkyBCdMRsbGzRr1gyLFy82aDgiIiIic6F30aTVagEAPj4+OHv2LBo2bGi0UERERETmRnZzy8zMTGPkICIiIjJrVeoIXlhYiOPHjyMrKwtlZWU6Y3z+HBEREdVGsoumCxcu4I033kBRUREKCwvh6uqK+/fvw8HBAY0aNWLRRERERLWS7JYDkZGR6N27Nx4+fAh7e3ucPn0at27dQseOHbFo0SJjZCQiIiIyOdlF08WLFzFx4kRYWVnB2toapaWlaNq0KWJjY/Hvf//bGBmJiIiITE520WRjYwMrq8e/1qhRI2RlZQEAnJyccPv2bcOmIyIiIjITsvc0BQYG4uzZs2jVqhW6d++OmTNn4v79+/jqq6/Qtm1bY2QkIiIiMjnZK03z5s2Dh4cHAGDu3LlwcXHB6NGj8dtvv2HNmjUGD0hERERkDmSvNHXq1En6vlGjRjh48KBBAxERERGZI9krTURERESWSPZKU05ODiZNmoT4+Hjk5uZCCKEzrtFoDBaOTMeqWGXqCEREZOYs7bNCdtH0/vvvIysrCzNmzICHhwcUCoUxcpGJ2WcmmDoCERGRWZFdNJ04cQI//fQT2rdvb4Q4ZC6KfUKgtXc2dQwiIjJjVsUqi/ojW3bR1LRp00qX5Kj20do7Q1u3oaljEBERmQ3ZG8GXLVuGKVOm4ObNm0aIQ0RERGSeZK80DRw4EEVFRWjRogUcHBxgY2OjM56Xl2ewcERERETmQnbRtGzZMiPEICIiIjJvsoumIUOGGCMHERERkVljc0siIiIiPbBoIiIiItIDiyYiIiIiPbBoIiIiItLDXy6a1Go1/vvf/yI9Pd0QeYiIiIjMkuy75wYMGICQkBCMHTsWxcXF6NSpE27evAkhBLZv345+/foZIydVM6uSfFNHICIiM2dpnxWyi6aEhARMmzYNALBnzx4IIaBSqbBp0ybMmTOHRVMN5+TkBBulLXDjuKmjEBFRDWCjtIWTk5OpY1QL2UVTfn4+XF1dAQAHDx5Ev3794ODggLCwMHz88ccGD0jVy83NDVu+2oz8fMv664Hoz9y6dQtz587FtGnT4O3tbeo4RGbFyckJbm5upo5RLar0wN7ExES4urri4MGD2L59OwDg4cOHsLOzM3hAqn5ubm4W8y8AkRze3t5o3bq1qWMQkYnILpo++ugjhIeHo169evD29sbLL78M4PFlO39/f0PnIyIiIjILsu+eGzNmDBITE7F+/XqcOHECVlaPT9G8eXPMmTPH4AGr28qVK9GsWTPY2dkhKCgIZ86cMXUkIiIiMgNVajnQqVMnvPXWW6hXrx40Gg0uXryILl26oGvXrobOV6127NiBqKgoREdH4/z582jXrh169eqF3NxcU0cjIiIiE6vS5Tl/f38MHz4cGo0G3bt3x6lTp+Dg4IB9+/ZJl+tqoiVLlmDkyJEYOnQoAOCLL77A/v37sX79ekyZMsXE6cjSlJSUICsry9QxCI83gj/5v2R6Xl5e3EdL1U520fTNN9/gnXfeAQDs3bsXmZmZuHr1Kr766itMmzYNJ0+eNHjI6lBWVobk5GRMnTpVOmZlZYUePXogMTGx0vzS0lKUlpZKP6vV6mrJSZYjKysLo0aNMnUMesLcuXNNHYH+f2vWrOGmfKp2soum+/fvw93dHQBw4MAB9O/fH61bt8awYcOwfPlygwesLvfv34dGo6l015ibmxuuXr1aaf78+fMRExNTXfHIAnl5eWHNmjWmjkFklry8vEwdgSyQ7KLJzc0NV65cgYeHBw4ePIjVq1cDAIqKimBtbW3wgOZq6tSpiIqKkn5Wq9Vo2rSpCRNRbWNnZ8e/pImIzIjsomno0KEYMGAAPDw8oFAo0KNHDwBAUlIS2rRpY/CA1aVhw4awtrZGTk6OzvGcnBxpZe1Jtra2sLW1ra54REREZGKy75779NNPsW7dOowaNQonT56UCgdra+savVlaqVSiY8eOiI+Pl45ptVrEx8cjODjYhMmIiIjIHMheaQKAf/7zn5WODRky5C+HMbWoqCgMGTIEnTp1QufOnbFs2TIUFhZKd9MRERGR5apS0XT8+HEsWrQI6enpAABfX198/PHH6Natm0HDVbeBAwfit99+w8yZM5GdnY327dvj4MGDfKQIERERQSGEEHJ+YcuWLRg6dCjefvttqZnlyZMnsWfPHmzcuBH/+te/jBLU3KnVajg5OSE/Px+Ojo6mjkNERER6kPP5Lbtoev755zFq1ChERkbqHF+yZAnWrl0rrT5ZGhZNRERENY+cz2/ZG8Fv3LiB3r17Vzrep08fZGZmyj0dERERUY0gu2hq2rSpzh1mFY4cOcI+RURERFRryd4IPnHiRIwfP156SC/weE/Txo0ba3RHcCIiIqI/I7toGj16NNzd3bF48WLExcUBeLzPaceOHejbt6/BAxIRERGZA1lF06NHjzBv3jwMGzYMJ06cMFYmIiIiIrMja09TnTp1EBsbi0ePHhkrDxEREZFZkr0RPDQ0FMePHzdGFiIiIiKzJXtP0+uvv44pU6bg8uXL6NixI+rWrasz3qdPH4OFq0kq2l2p1WoTJyEiIiJ9VXxu69O2UnZzSyurP16cUigU0Gg0ck5Xa/z6669suUBERFRD3b59G02aNPnTObKLJno2rVaLu3fvon79+lAoFKaOQ0QGpFar0bRpU9y+fZsd/4lqGSEECgoK4Onp+acLQwCLJiKi/4mPSSIiQMaepuLiYsTHx+Mf//gHAGDq1KkoLS2Vxq2trTF79mzY2dkZPiURERGRieldNG3atAn79++XiqYVK1bAz88P9vb2AICrV6/C09Oz0oN8iYiIiGoDvVsObN26FaNGjdI59vXXX+PYsWM4duwYFi5cKHUIJyKqTWxtbREdHQ1bW1tTRyEiE9J7T5OHhwcSExPRrFkzAMDf/vY3nD17Vvr52rVreOGFF5Cfn2+srEREREQmo/flOZVKpbOH6bffftMZ12q1OuNEREREtYnel+eaNGmC1NTUPxxPSUn5n/0NiIiIiGoqvYumN954AzNnzkRJSUmlseLiYsTExCAsLMyg4YiIiIjMhd57mnJyctC+fXsolUqMHTsWrVu3BgBkZGRgxYoVePToES5cuAA3NzejBiYiIiIyBVnNLTMzMzF69GgcPnxYekaLQqHA3//+d6xatQrNmzc3WlAiIiIiU6pSR/C8vDxcv34dANCyZUu4uroaPBgRERGROeFjVIiIiIj0oPdGcCIiIiJL9v8BQA2Gb4oZOTIAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=wb, y='Gross national income per capita, Atlas method: $: 2016');"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.violinplot(data=wb, y=\"Gross national income per capita, Atlas method: $: 2016\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A quartile represents a 25% portion of the data. We say that:\n",
+        "\n",
+        "- The first quartile (Q1) represents the 25th percentile – 25% of the data is smaller than or equal to the first quartile.\n",
+        "- The second quartile (Q2) represents the 50th percentile, also known as the median – 50% of the data is smaller than or equal to the second quartile.\n",
+        "- The third quartile (Q3) represents the 75th percentile – 75% of the data is smaller than or equal to the third quartile.\n",
+        "\n",
+        "This means that the middle 50% of the data lies between the first and third quartiles. This is demonstrated in the histogram below. The three quartiles are marked with red vertical bars."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#| code-fold: true\n",
+        "gdp = wb['Gross domestic product: % growth : 2016']\n",
+        "gdp = gdp[~gdp.isna()]\n",
+        "\n",
+        "q1, q2, q3 = np.percentile(gdp, [25, 50, 75])\n",
+        "\n",
+        "wb_quartiles = wb.copy()\n",
+        "wb_quartiles['category'] = None\n",
+        "wb_quartiles.loc[(wb_quartiles['Gross domestic product: % growth : 2016'] < q1) | (wb_quartiles['Gross domestic product: % growth : 2016'] > q3), 'category'] = 'Outside of the middle 50%'\n",
+        "wb_quartiles.loc[(wb_quartiles['Gross domestic product: % growth : 2016'] > q1) & (wb_quartiles['Gross domestic product: % growth : 2016'] < q3), 'category'] = 'In the middle 50%'\n",
+        "\n",
+        "sns.histplot(wb_quartiles, x=\"Gross domestic product: % growth : 2016\", hue=\"category\")\n",
+        "sns.rugplot([q1, q2, q3], c=\"firebrick\", lw=6, height=0.1);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In a box plot, the lower extent of the box lies at Q1, while the upper extent of the box lies at Q3. The horizontal line in the middle of the box corresponds to Q2 (equivalently, the median)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=wb, y='Gross domestic product: % growth : 2016');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The **whiskers** of a box-plot are the two points that lie at the \\[$1^{st}$ Quartile $-$ ($1.5\\times$ IQR)\\], and the \\[$3^{rd}$ Quartile $+$ ($1.5\\times$ IQR)\\]. They are the lower and upper ranges of \"normal\" data (the points excluding outliers). \n",
+        "\n",
+        "The different forms of information contained in a box plot can be summarised as follows:\n",
+        "\n",
+        "<center><img src = \"images/box_plot_diagram.png\" width = \"600\"></img></a></center>\n",
+        "\n",
+        "\n",
+        "A violin plot displays quartile information, albeit a bit more subtly through smoothed density curves. Look closely at the center vertical bar of the violin plot below; the three quartiles and \"whiskers\" are still present!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.violinplot(data=wb, y='Gross domestic product: % growth : 2016');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Side-by-Side Box and Violin Plots\n",
+        "\n",
+        "Plotting side-by-side box or violin plots allows us to compare distributions across different categories. In other words, they enable us to plot both a qualitative variable and a quantitative continuous variable in one visualization.\n",
+        "\n",
+        "With `seaborn`, we can easily create side-by-side plots by specifying both an x and y column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=wb, x=\"Continent\", y='Gross domestic product: % growth : 2016');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Histograms\n",
+        "\n",
+        "You are likely familiar with histograms from Data 8. A histogram collects continuous data into bins, then plots this binned data. Each bin reflects the density of datapoints with values that lie between the left and right ends of the bin; in other words, the **area** of each bin is proportional to the **percentage** of datapoints it contains. \n",
+        "\n",
+        "#### Plotting Histograms\n",
+        "Below, we plot a histogram using matplotlib and seaborn. Which graph do you prefer? "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# The `edgecolor` argument controls the color of the bin edges\n",
+        "gni = wb[\"Gross national income per capita, Atlas method: $: 2016\"]\n",
+        "plt.hist(gni, density=True, edgecolor=\"white\")\n",
+        "\n",
+        "# Add labels\n",
+        "plt.xlabel(\"Gross national income per capita\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "plt.title(\"Distribution of gross national income per capita\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.histplot(data=wb, x=\"Gross national income per capita, Atlas method: $: 2016\", stat=\"density\")\n",
+        "plt.title(\"Distribution of gross national income per capita\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Overlaid Histograms\n",
+        "\n",
+        "We can overlay histograms (or density curves) to compare distributions across qualitative categories.\n",
+        "\n",
+        "The `hue` parameter of `sns.histplot` specifies the column that should be used to determine the color of each category. `hue` can be used in many `seaborn` plotting functions.\n",
+        "\n",
+        "Notice that the resulting plot includes a legend describing which color corresponds to each hemisphere – a legend should always be included if color is used to encode information in a visualization!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Create a new variable to store the hemisphere in which each country is located\n",
+        "north = [\"Asia\", \"Europe\", \"N. America\"]\n",
+        "south = [\"Africa\", \"Oceania\", \"S. America\"]\n",
+        "wb.loc[wb[\"Continent\"].isin(north), \"Hemisphere\"] = \"Northern\"\n",
+        "wb.loc[wb[\"Continent\"].isin(south), \"Hemisphere\"] = \"Southern\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.histplot(data=wb, x=\"Gross national income per capita, Atlas method: $: 2016\", hue=\"Hemisphere\", stat=\"density\")\n",
+        "plt.title(\"Distribution of gross national income per capita\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Again, each bin of a histogram is scaled such that its **area** is proportional to the **percentage** of all datapoints that it contains."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First bin has width 16410.0 and height 4.7741589911386953e-05\n",
+            "This corresponds to 16410.0 * 4.7741589911386953e-05 = 78.343949044586% of the data\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "densities, bins, _ = plt.hist(gni, density=True, edgecolor=\"white\", bins=5)\n",
+        "plt.xlabel(\"Gross national income per capita\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "\n",
+        "print(f\"First bin has width {bins[1]-bins[0]} and height {densities[0]}\")\n",
+        "print(f\"This corresponds to {bins[1]-bins[0]} * {densities[0]} = {(bins[1]-bins[0])*densities[0]*100}% of the data\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Evaluating Histograms\n",
+        "\n",
+        "Histograms allow us to assess a distribution by their shape. There are a few properties of histograms we can analyze:\n",
+        "\n",
+        "1. Skewness and Tails\n",
+        "    - Skewed left vs skewed right\n",
+        "    - Left tail vs right tail\n",
+        "2. Outliers\n",
+        "    - Using percentiles\n",
+        "3. Modes\n",
+        "    - Most commonly occuring data\n",
+        "\n",
+        "##### Skewness and Tails\n",
+        "\n",
+        "The skew of a histogram describes the direction in which its \"tail\" extends.\n",
+        "- A distribution with a long right tail is **skewed right** (such as `Gross national income per capita`). In a right-skewed distribution, the few large outliers \"pull\" the mean to the **right** of the median. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Distribution with a long right tail')"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.histplot(data = wb, x = 'Gross national income per capita, Atlas method: $: 2016', stat = 'density');\n",
+        "plt.title('Distribution with a long right tail')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "- A distribution with a long left tail is **skewed left** (such as `Access to an improved water source`). In a left-skewed distribution, the few small outliers \"pull\" the mean to the **left** of the median. \n",
+        "\n",
+        "In the case where a distribution has equal-sized right and left tails, it is **symmetric**. The mean is approximately **equal** to the median. Think of mean as the balancing point of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Distribution with a long left tail')"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.histplot(data = wb, x = 'Access to an improved water source: % of population: 2015', stat = 'density');\n",
+        "plt.title('Distribution with a long left tail')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Outliers\n",
+        "\n",
+        "Loosely speaking, an **outlier** is defined as a data point that lies an abnormally large distance away from other values. Let's make this more concrete. As you may have observed in the box plot infographic earlier, we define **outliers** to be the data points that fall beyond the whiskers. Specifically, values that are less than the \\[$1^{st}$ Quartile $-$ ($1.5\\times$ IQR)\\], or greater than \\[$3^{rd}$ Quartile $+$ ($1.5\\times$ IQR).\\]\n",
+        "\n",
+        "##### Modes\n",
+        "\n",
+        "In Data 100, we describe a \"mode\" of a histogram as a peak in the distribution. Often, however, it is difficult to determine what counts as its own \"peak.\" For example, the number of peaks in the distribution of HIV rates across different countries varies depending on the number of histogram bins we plot.\n",
+        "\n",
+        "If we set the number of bins to 5, the distribution appears unimodal."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Rename the very long column name for convenience\n",
+        "wb = wb.rename(columns={'Antiretroviral therapy coverage: % of people living with HIV: 2015':\"HIV rate\"})\n",
+        "# With 5 bins, it seems that there is only one peak\n",
+        "sns.histplot(data=wb, x=\"HIV rate\", stat=\"density\", bins=5)\n",
+        "plt.title(\"5 histogram bins\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# With 10 bins, there seem to be two peaks\n",
+        "\n",
+        "sns.histplot(data=wb, x=\"HIV rate\", stat=\"density\", bins=10)\n",
+        "plt.title(\"10 histogram bins\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# And with 20 bins, it becomes hard to say what counts as a \"peak\"!\n",
+        "\n",
+        "sns.histplot(data=wb, x =\"HIV rate\", stat=\"density\", bins=20)\n",
+        "plt.title(\"20 histogram bins\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In part, it is these ambiguities that motivate us to consider using Kernel Density Estimation (KDE), which we will explore more in the next lecture."
+      ]
+    }
+  ],
+  "metadata": {
+    "jupytext": {
+      "text_representation": {
+        "extension": ".qmd",
+        "format_name": "quarto",
+        "format_version": "1.0",
+        "jupytext_version": "1.16.1"
+      }
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/visualization_1/visualization_1.qmd b/visualization_1/visualization_1.qmd
index 4c49a47d..aa3145f4 100644
--- a/visualization_1/visualization_1.qmd
+++ b/visualization_1/visualization_1.qmd
@@ -87,6 +87,9 @@ Let's contextualize this in an example. We will use the World Bank dataset (`wb`
 #| code-fold: true
 import pandas as pd
 import numpy as np
+import warnings 
+
+warnings.filterwarnings("ignore", "use_inf_as_na") # Supresses distracting deprecation warnings
 
 wb = pd.read_csv("data/world_bank.csv", index_col=0)
 wb.head()
@@ -348,3 +351,4 @@ plt.title("20 histogram bins");
 ```
 
 In part, it is these ambiguities that motivate us to consider using Kernel Density Estimation (KDE), which we will explore more in the next lecture.
+
diff --git a/visualization_2/images/gaussian_10.png b/visualization_2/images/gaussian_10.png
deleted file mode 100644
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diff --git a/visualization_2/visualization_2.ipynb b/visualization_2/visualization_2.ipynb
new file mode 100644
index 00000000..c14abb8c
--- /dev/null
+++ b/visualization_2/visualization_2.ipynb
@@ -0,0 +1,1170 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "title: Visualization II\n",
+        "format:\n",
+        "  html:\n",
+        "    toc: true\n",
+        "    toc-depth: 5\n",
+        "    toc-location: right\n",
+        "    code-fold: false\n",
+        "    theme:\n",
+        "      - cosmo\n",
+        "      - cerulean\n",
+        "    callout-icon: false\n",
+        "jupyter:\n",
+        "  jupytext:\n",
+        "    text_representation:\n",
+        "      extension: .qmd\n",
+        "      format_name: quarto\n",
+        "      format_version: '1.0'\n",
+        "      jupytext_version: 1.16.1\n",
+        "  kernelspec:\n",
+        "    display_name: Python 3 (ipykernel)\n",
+        "    language: python\n",
+        "    name: python3\n",
+        "---\n",
+        "\n",
+        "::: {.callout-note collapse=\"false\"}\n",
+        "## Learning Outcomes\n",
+        "- Understanding KDE for plotting distributions and estimating density curves.\n",
+        "- Using transformations to analyze the relationship between two variables.\n",
+        "- Evaluating the quality of a visualization based on visualization theory concepts.\n",
+        ":::\n",
+        "\n",
+        "## Kernel Density Estimation\n",
+        "Often, we want to identify general trends across a distribution, rather than focus on detail. Smoothing a distribution helps generalize the structure of the data and eliminate noise.\n",
+        "\n",
+        "### KDE Theory\n",
+        "\n",
+        "A **kernel density estimate (KDE)** is a smooth, continuous function that approximates a curve. It allows us to represent general trends in a distribution without focusing on the details, which is useful for analyzing the broad structure of a dataset. \n",
+        "\n",
+        "More formally, a KDE attempts to approximate the underlying **probability distribution** from which our dataset was drawn. You may have encountered the idea of a probability distribution in your other classes; if not, we'll discuss it at length in the next lecture. For now, you can think of a probability distribution as a description of how likely it is for us to sample a particular value in our dataset. \n",
+        "\n",
+        "A KDE curve estimates the probability density function of a random variable. Consider the example below, where we have used `sns.displot` to plot both a histogram (containing the data points we actually collected) and a KDE curve (representing the *approximated* probability distribution from which this data was drawn) using data from the World Bank dataset (`wb`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "wb = pd.read_csv(\"data/world_bank.csv\", index_col=0)\n",
+        "wb = wb.rename(columns={'Antiretroviral therapy coverage: % of people living with HIV: 2015':\"HIV rate\",\n",
+        "                       'Gross national income per capita, Atlas method: $: 2016':'gni'})\n",
+        "wb.head()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "sns.displot(data = wb, x = 'HIV rate', \\\n",
+        "                       kde = True, stat = \"density\")\n",
+        "\n",
+        "plt.title(\"Distribution of HIV rates\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Notice that the smooth KDE curve is higher when the histogram bins are taller. You can think of the height of the KDE curve as representing how \"probable\" it is that we randomly sample a datapoint with the corresponding value. This intuitively makes sense – if we have already collected more datapoints with a particular value (resulting in a tall histogram bin), it is more likely that, if we randomly sample another datapoint, we will sample one with a similar value (resulting in a high KDE curve).\n",
+        "\n",
+        "The area under a probability density function should always integrate to 1, representing the fact that the total probability of a distribution should always sum to 100%. Hence, a KDE curve will always have an area under the curve of 1.\n",
+        "\n",
+        "### Constructing a KDE\n",
+        "\n",
+        "We perform kernel density estimation using three steps.\n",
+        "\n",
+        "1. Place a kernel at each datapoint.\n",
+        "2. Normalize the kernels to have a total area of 1 (across all kernels).\n",
+        "3. Sum the normalized kernels.\n",
+        "\n",
+        "We'll explain what a \"kernel\" is momentarily.\n",
+        "\n",
+        "To make things simpler, let's construct a KDE for a small, artificially generated dataset of 5 datapoints: $[2.2, 2.8, 3.7, 5.3, 5.7]$. In the plot below, each vertical bar represents one data point."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "data = [2.2, 2.8, 3.7, 5.3, 5.7]\n",
+        "\n",
+        "sns.rugplot(data, height=0.3)\n",
+        "\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Our goal is to create the following KDE curve, which was generated automatically by `sns.kdeplot`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "sns.kdeplot(data)\n",
+        "\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Step 1: Place a Kernel at Each Data Point\n",
+        "\n",
+        "To begin generating a density curve, we need to choose a **kernel** and **bandwidth value ($\\alpha$)**. What are these exactly? \n",
+        "\n",
+        "A **kernel** is a density curve. It is the mathematical function that attempts to capture the randomness of each data point in our sampled data. To explain what this means, consider just *one* of the datapoints in our dataset: $2.2$. We obtained this datapoint by randomly sampling some information out in the real world (you can imagine $2.2$ as representing a single measurement taken in an experiment, for example). If we were to sample a new datapoint, we may obtain a slightly different value. It could be higher than $2.2$; it could also be lower than $2.2$. We make the assumption that any future sampled datapoints will likely be similar in value to the data we've already drawn. This means that our *kernel* – our description of the probability of randomly sampling any new value – will be greatest at the datapoint we've already drawn but still have non-zero probability above and below it. The area under any kernel should integrate to 1, representing the total probability of drawing a new datapoint.\n",
+        "\n",
+        "A **bandwidth value**, usually denoted by $\\alpha$, represents the width of the kernel. A large value of $\\alpha$ will result in a wide, short kernel function, while a small value with result in a narrow, tall kernel.\n",
+        "\n",
+        "Below, we place a **Gaussian kernel**, plotted in orange, over the datapoint $2.2$. A Gaussian kernel is simply the normal distribution, which you may have called a bell curve in Data 8. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "\n",
+        "def gaussian_kernel(x, z, a):\n",
+        "    # We'll discuss where this mathematical formulation came from later\n",
+        "    return (1/np.sqrt(2*np.pi*a**2)) * np.exp((-(x - z)**2 / (2 * a**2)))\n",
+        "\n",
+        "# Plot our datapoint\n",
+        "sns.rugplot([2.2], height=0.3)\n",
+        "\n",
+        "# Plot the kernel\n",
+        "x = np.linspace(-3, 10, 1000)\n",
+        "plt.plot(x, gaussian_kernel(x, 2.2, 1))\n",
+        "\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To begin creating our KDE, we place a kernel on *each* datapoint in our dataset. For our dataset of 5 points, we will have 5 kernels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "\n",
+        "# You will work with the functions below in Lab 4\n",
+        "def create_kde(kernel, pts, a):\n",
+        "    # Takes in a kernel, set of points, and alpha\n",
+        "    # Returns the KDE as a function\n",
+        "    def f(x):\n",
+        "        output = 0\n",
+        "        for pt in pts:\n",
+        "            output += kernel(x, pt, a)\n",
+        "        return output / len(pts) # Normalization factor\n",
+        "    return f\n",
+        "\n",
+        "def plot_kde(kernel, pts, a):\n",
+        "    # Calls create_kde and plots the corresponding KDE\n",
+        "    f = create_kde(kernel, pts, a)\n",
+        "    x = np.linspace(min(pts) - 5, max(pts) + 5, 1000)\n",
+        "    y = [f(xi) for xi in x]\n",
+        "    plt.plot(x, y);\n",
+        "    \n",
+        "def plot_separate_kernels(kernel, pts, a, norm=False):\n",
+        "    # Plots individual kernels, which are then summed to create the KDE\n",
+        "    x = np.linspace(min(pts) - 5, max(pts) + 5, 1000)\n",
+        "    for pt in pts:\n",
+        "        y = kernel(x, pt, a)\n",
+        "        if norm:\n",
+        "            y /= len(pts)\n",
+        "        plt.plot(x, y)\n",
+        "    \n",
+        "    plt.show();\n",
+        "    \n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5)\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "\n",
+        "plot_separate_kernels(gaussian_kernel, data, a = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Step 2: Normalize Kernels to Have a Total Area of 1\n",
+        "\n",
+        "Above, we said that *each* kernel has an area of 1. Earlier, we also said that our goal is to construct a KDE curve using these kernels with a *total* area of 1. If we were to directly sum the kernels as they are, we would produce a KDE curve with an integrated area of (5 kernels) $\\times$ (area of 1 each) = 5. To avoid this, we will **normalize** each of our kernels. This involves multiplying each kernel by $\\frac{1}{\\#\\:\\text{datapoints}}$. \n",
+        "\n",
+        "In the cell below, we multiply each of our 5 kernels by $\\frac{1}{5}$ to apply normalization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5)\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "\n",
+        "# The `norm` argument specifies whether or not to normalize the kernels\n",
+        "plot_separate_kernels(gaussian_kernel, data, a = 1, norm = True)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Step 3: Sum the Normalized Kernels\n",
+        "\n",
+        "Our KDE curve is the sum of the normalized kernels. Notice that the final curve is identical to the plot generated by `sns.kdeplot` we saw earlier!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "plt.xlim(-3, 10)\n",
+        "plt.ylim(0, 0.5)\n",
+        "plt.xlabel(\"Data\")\n",
+        "plt.ylabel(\"Density\")\n",
+        "\n",
+        "plot_kde(gaussian_kernel, data, a = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Kernel Functions and Bandwidths\n",
+        "\n",
+        "<center><img src = \"images/kde_function.png\" width = \"345\"></img></a></center>\n",
+        "\n",
+        "A general “KDE formula” function is given above.\n",
+        "\n",
+        "1. $K_{\\alpha}(x, x_i)$ is the kernel centered on the observation `i`. \n",
+        "    - Each kernel individually has area 1.\n",
+        "    - x represents any number on the number line. It is the input to our function.\n",
+        "2. $n$ is the number of observed datapoints that we have.\n",
+        "    - We multiply by $\\frac{1}{n}$ so that the total area of the KDE is still 1.\n",
+        "3. Each $x_i \\in \\{x_1, x_2, \\dots, x_n\\}$ represents an observed datapoint. \n",
+        "    - These are what we use to create our KDE by summing multiple shifted kernels centered at these points.\n",
+        "\n",
+        "* $\\alpha$ (alpha) is the bandwidth or smoothing parameter. \n",
+        "\n",
+        "A **kernel** (for our purposes) is a valid density function. This means it:\n",
+        "\n",
+        "- Must be non-negative for all inputs.\n",
+        "- Must integrate to 1.\n",
+        "\n",
+        "#### Gaussian Kernel\n",
+        "\n",
+        "The most common kernel is the **Gaussian kernel**. The Gaussian kernel is equivalent to the Gaussian probability density function (the Normal distribution), centered at the observed value with a standard deviation of (this is known as the **bandwidth** parameter).\n",
+        "\n",
+        "$$K_a(x, x_i) = \\frac{1}{\\sqrt{2\\pi\\alpha^{2}}}e^{-\\frac{(x-x_i)^{2}}{2\\alpha^{2}}}$$\n",
+        "\n",
+        "In this formula:\n",
+        "\n",
+        "* $x$ (no subscript) represents any value along the x-axis of our plot\n",
+        "* $x_i$ represents the $i$ -th datapoint in our dataset. It is one of the values that we have actually collected in our data sampling process. In our example earlier, $x_i=2.2$. Those of you who have taken a probability class may recognize $x_i$ as the **mean** of the normal distribution.\n",
+        "* Each kernel is **centered** on our observed values, so its distribution mean is $x_i$.\n",
+        "* $\\alpha$ is the bandwidth parameter, representing the width of our kernel. More formally, $\\alpha$ is the **standard deviation** of the Gaussian curve.\n",
+        "    * A large value of $\\alpha$ will produce a kernel that is wider and shorter – this leads to a smoother KDE when the kernels are summed together.\n",
+        "    * A small value of $\\alpha$ will produce a narrower, taller kernel, and, with it, a noisier KDE.\n",
+        "\n",
+        "The details of this (admittedly intimidating) formula are less important than understanding its role in kernel density estimation – this equation gives us the shape of each kernel.\n",
+        "\n",
+        "| Gaussian Kernel, $\\alpha$ = 0.1 | Gaussian Kernel, $\\alpha$ = 1 | \n",
+        "| --- | --- |\n",
+        "| <img src=\"images/gaussian_0.1.png\" alt='gaussian_0.1' width='345'> | <img src=\"images/gaussian_1.png\" alt='gaussian_1' width='345'> | \n",
+        "\n",
+        "| Gaussian Kernel, $\\alpha$ = 2 | Gaussian Kernel, $\\alpha$ = 10 | \n",
+        "|  --- | --- | \n",
+        "| <img src=\"images/gaussian_2.png\" alt='gaussian_2' width='345'> | <img src=\"images/gaussian_10.png\" alt='gaussian_10' width='345'> | \n",
+        "\n",
+        "#### Boxcar Kernel\n",
+        "\n",
+        "Another example of a kernel is the **Boxcar kernel**. The boxcar kernel assigns a uniform density to points within a \"window\" of the observation, and a density of 0 elsewhere. The equation below is a boxcar kernel with the center at $x_i$ and the bandwidth of $\\alpha$.\n",
+        "\n",
+        "$$K_a(x, x_i) = \\begin{cases}\n",
+        "        \\frac{1}{\\alpha}, & |x - x_i| \\le \\frac{\\alpha}{2}\\\\\n",
+        "        0, & \\text{else }\n",
+        "    \\end{cases}$$\n",
+        "    \n",
+        "The boxcar kernel is seldom used in practice – we include it here to demonstrate that a kernel function can take whatever form you would like, provided it integrates to 1 and does not output negative values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| code-fold: true\n",
+        "#| fig-cap: The Boxcar kernel centered at 0 with bandwidth $\\alpha$ = 1.\n",
+        "\n",
+        "def boxcar_kernel(alpha, x, z):\n",
+        "    return (((x-z)>=-alpha/2)&((x-z)<=alpha/2))/alpha\n",
+        "\n",
+        "xs = np.linspace(-5, 5, 200)\n",
+        "alpha=1\n",
+        "kde_curve = [boxcar_kernel(alpha, x, 0) for x in xs]\n",
+        "plt.plot(xs, kde_curve);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The diagram on the right is how the density curve for our 5 point dataset would have looked had we used the Boxcar kernel with bandwidth $\\alpha$ = 1.\n",
+        "\n",
+        "| KDE | Boxcar | \n",
+        "| --- | --- | \n",
+        "| <img src=\"images/gaussian_kernel.png\" alt='kde_step_3' width='350'> | <img src=\"images/boxcar_kernel.png\" alt='boxcar_kernel' width='350'> | \n",
+        "\n",
+        "## Diving Deeper into `displot`\n",
+        "\n",
+        "As we saw earlier, we can use `seaborn`'s `displot` function to plot various distributions. In particular, `displot` allows you to specify the `kind` of plot and is a wrapper for `histplot`, `kdeplot`, and `ecdfplot`.\n",
+        "\n",
+        "Below, we can see a couple of examples of how `sns.displot` can be used to plot various distributions.\n",
+        "\n",
+        "First, we can plot a histogram by setting `kind` to `\"hist\"`. Note that here we've specified `stat = density` to normalize the histogram such that the area under the histogram is equal to 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.displot(data=wb, \n",
+        "            x=\"gni\", \n",
+        "            kind=\"hist\", \n",
+        "            stat=\"density\") # default: stat=count and density integrates to 1\n",
+        "plt.title(\"Distribution of gross national income per capita\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, what if we want to generate a KDE plot? We can set `kind` = to `\"kde\"`! "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.displot(data=wb, \n",
+        "            x=\"gni\", \n",
+        "            kind='kde')\n",
+        "plt.title(\"Distribution of gross national income per capita\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And finally, if we want to generate an Empirical Cumulative Distribution Function (ECDF), we can specify `kind = \"ecdf\"`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.displot(data=wb, \n",
+        "            x=\"gni\", \n",
+        "            kind='ecdf')\n",
+        "plt.title(\"Cumulative Distribution of gross national income per capita\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Relationships Between Quantitative Variables\n",
+        "\n",
+        "Up until now, we've discussed how to visualize single-variable distributions. Going beyond this, we want to understand the relationship between pairs of numerical variables.\n",
+        "\n",
+        "#### Scatter Plots\n",
+        "\n",
+        "**Scatter plots** are one of the most useful tools in representing the relationship between **pairs** of quantitative variables. They are particularly important in gauging the strength, or correlation, of the relationship between variables. Knowledge of these relationships can then motivate decisions in our modeling process.\n",
+        "\n",
+        "In `matplotlib`, we use the function `plt.scatter` to generate a scatter plot. Notice that, unlike our examples of plotting single-variable distributions, now we specify sequences of values to be plotted along the x-axis *and* the y-axis. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "plt.scatter(wb[\"per capita: % growth: 2016\"], \\\n",
+        "            wb['Adult literacy rate: Female: % ages 15 and older: 2005-14'])\n",
+        "\n",
+        "plt.xlabel(\"% growth per capita\")\n",
+        "plt.ylabel(\"Female adult literacy rate\")\n",
+        "plt.title(\"Female adult literacy against % growth\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In `seaborn`, we call the function `sns.scatterplot`. We use the `x` and `y` parameters to indicate the values to be plotted along the x and y axes, respectively. By using the `hue` parameter, we can specify a third variable to be used for coloring each scatter point."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.scatterplot(data = wb, x = \"per capita: % growth: 2016\", \\\n",
+        "               y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \n",
+        "               hue = \"Continent\")\n",
+        "\n",
+        "plt.title(\"Female adult literacy against % growth\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Overplotting \n",
+        "\n",
+        "Although the plots above communicate the general relationship between the two plotted variables, they both suffer a major limitation – **overplotting**. Overplotting occurs when scatter points with similar values are stacked on top of one another, making it difficult to see the number of scatter points actually plotted in the visualization. Notice how in the upper righthand region of the plots, we cannot easily tell just how many points have been plotted. This makes our visualizations difficult to interpret. \n",
+        "\n",
+        "We have a few methods to help reduce overplotting:\n",
+        "\n",
+        "* Decreasing the size of the scatter point markers can improve readability. We do this by setting a new value to the size parameter, `s`, of `plt.scatter` or `sns.scatterplot`.\n",
+        "* **Jittering** is the process of adding a small amount of random noise to all x and y values to slightly shift the position of each datapoint. By randomly shifting all the data by some small distance, we can discern individual points more clearly without modifying the major trends of the original dataset.\n",
+        "\n",
+        "In the cell below, we first jitter the data using `np.random.uniform`, then re-plot it with smaller markers. The resulting plot is much easier to interpret."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "# Setting a seed ensures that we produce the same plot each time\n",
+        "# This means that the course notes will not change each time you access them\n",
+        "np.random.seed(150)\n",
+        "\n",
+        "# This call to np.random.uniform generates random numbers between -1 and 1\n",
+        "# We add these random numbers to the original x data to jitter it slightly\n",
+        "x_noise = np.random.uniform(-1, 1, len(wb))\n",
+        "jittered_x = wb[\"per capita: % growth: 2016\"] + x_noise\n",
+        "\n",
+        "# Repeat for y data\n",
+        "y_noise = np.random.uniform(-5, 5, len(wb))\n",
+        "jittered_y = wb[\"Adult literacy rate: Female: % ages 15 and older: 2005-14\"] + y_noise\n",
+        "\n",
+        "# Setting the size parameter `s` changes the size of each point\n",
+        "plt.scatter(jittered_x, jittered_y, s=15)\n",
+        "\n",
+        "plt.xlabel(\"% growth per capita (jittered)\")\n",
+        "plt.ylabel(\"Female adult literacy rate (jittered)\")\n",
+        "plt.title(\"Female adult literacy against % growth\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### `lmplot` and `jointplot`\n",
+        "\n",
+        "`seaborn` also includes several built-in functions for creating more sophisticated scatter plots. Two of the most commonly used examples are `sns.lmplot` and `sns.jointplot`. \n",
+        "\n",
+        "`sns.lmplot` plots both a scatter plot *and* a linear regression line, all in one function call. We'll discuss linear regression in a few lectures. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.lmplot(data = wb, x = \"per capita: % growth: 2016\", \\\n",
+        "           y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\")\n",
+        "\n",
+        "plt.title(\"Female adult literacy against % growth\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "`sns.jointplot` creates a visualization with three components: a scatter plot, a histogram of the distribution of x values, and a histogram of the distribution of y values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.jointplot(data = wb, x = \"per capita: % growth: 2016\", \\\n",
+        "           y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\")\n",
+        "\n",
+        "# plt.suptitle allows us to shift the title up so it does not overlap with the histogram\n",
+        "plt.suptitle(\"Female adult literacy against % growth\")\n",
+        "plt.subplots_adjust(top=0.9);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Hex plots\n",
+        "For datasets with a very large number of datapoints, jittering is unlikely to fully resolve the issue of overplotting. In these cases, we can attempt to visualize our data by its *density*, rather than displaying each individual datapoint.\n",
+        "\n",
+        "**Hex plots** can be thought of as two-dimensional histograms that show the joint distribution between two variables. This is particularly useful when working with very dense data. In a hex plot, the x-y plane is binned into hexagons. Hexagons that are darker in color indicate a greater density of data – that is, there are more data points that lie in the region enclosed by the hexagon.\n",
+        "\n",
+        "We can generate a hex plot using `sns.jointplot` modified with the `kind` parameter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.jointplot(data = wb, x = \"per capita: % growth: 2016\", \\\n",
+        "              y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \\\n",
+        "              kind = \"hex\")\n",
+        "\n",
+        "# plt.suptitle allows us to shift the title up so it does not overlap with the histogram\n",
+        "plt.suptitle(\"Female adult literacy against % growth\")\n",
+        "plt.subplots_adjust(top=0.9);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Contour Plots\n",
+        "\n",
+        "**Contour plots** are an alternative way of plotting the joint distribution of two variables. You can think of them as the 2-dimensional versions of KDE plots. A contour plot can be interpreted in a similar way to a [topographic map](https://gisgeography.com/contour-lines-topographic-map/). Each contour line represents an area that has the same *density* of datapoints throughout the region. Contours marked with darker colors contain more datapoints (a higher density) in that region.\n",
+        "\n",
+        "`sns.kdeplot` will generate a contour plot if we specify both x and y data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "sns.kdeplot(data = wb, x = \"per capita: % growth: 2016\", \\\n",
+        "            y = \"Adult literacy rate: Female: % ages 15 and older: 2005-14\", \\\n",
+        "            fill = True)\n",
+        "\n",
+        "plt.title(\"Female adult literacy against % growth\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Transformations\n",
+        "We have now covered visualizations in great depth, looking into various forms of visualizations, plotting libraries, and high-level theory.\n",
+        "\n",
+        "Much of this was done to uncover insights in data, which will prove necessary when we begin building models of data later in the course. A strong graphical correlation between two variables hints at an underlying relationship that we may want to study in greater detail. However, relying on visual relationships alone is limiting - not all plots show association. The presence of outliers and other statistical anomalies makes it hard to interpret data.\n",
+        "\n",
+        "**Transformations** are the process of manipulating data to find significant relationships between variables. These are often found by applying mathematical functions to variables that \"transform\" their range of possible values and highlight some previously hidden associations between data.\n",
+        "\n",
+        "To see why we may want to transform data, consider the following plot of adult literacy rates against gross national income."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "#| code-fold: true\n",
+        "\n",
+        "# Some data cleaning to help with the next example\n",
+        "df = pd.DataFrame(index=wb.index)\n",
+        "df['lit'] = wb['Adult literacy rate: Female: % ages 15 and older: 2005-14'] \\\n",
+        "            + wb[\"Adult literacy rate: Male: % ages 15 and older: 2005-14\"]\n",
+        "df['inc'] = wb['gni']\n",
+        "df.dropna(inplace=True)\n",
+        "\n",
+        "plt.scatter(df[\"inc\"], df[\"lit\"])\n",
+        "plt.xlabel(\"Gross national income per capita\")\n",
+        "plt.ylabel(\"Adult literacy rate\")\n",
+        "plt.title(\"Adult literacy rate against GNI per capita\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This plot is difficult to interpret for two reasons:\n",
+        "\n",
+        "* The data shown in the visualization appears almost \"smushed\" – it is heavily concentrated in the upper lefthand region of the plot. Even if we jittered the dataset, we likely would not be able to fully assess all datapoints in that area.\n",
+        "* It is hard to generalize a clear relationship between the two plotted variables. While adult literacy rate appears to share some positive relationship with gross national income, we are not able to describe the specifics of this trend in much detail.\n",
+        "\n",
+        "A transformation would allow us to visualize this data more clearly, which, in turn, would enable us to describe the underlying relationship between our variables of interest.\n",
+        "\n",
+        "We will most commonly apply a transformation to **linearize a relationship** between variables. If we find a transformation to make a scatter plot of two variables linear, we can \"backtrack\" to find the exact relationship between the variables. This helps us in two major ways. Firstly, linear relationships are particularly simple to interpret – we have an intuitive sense of what the slope and intercept of a linear trend represent, and how they can help us understand the relationship between two variables. Secondly, linear relationships are the backbone of linear models. We will begin exploring linear modeling in great detail next week. As we'll soon see, linear models become much more effective when we are working with linearized data.  \n",
+        "\n",
+        "In the remainder of this note, we will discuss how to linearize a dataset to produce the result below. Notice that the resulting plot displays a rough linear relationship between the values plotted on the x and y axes.\n",
+        "\n",
+        "<img src=\"images/linearize.png\" alt='linearize' width='800'>\n",
+        "\n",
+        "### Linearization and Applying Transformations\n",
+        "\n",
+        "To linearize a relationship, begin by asking yourself: what makes the data non-linear? It is helpful to repeat this question for each variable in your visualization.\n",
+        "\n",
+        "Let's start by considering the gross national income variable in our plot above. Looking at the y values in the scatter plot, we can see that many large y values are all clumped together, compressing the vertical axis. The scale of the horizontal axis is also being distorted by the few large outlying x values on the right. \n",
+        "\n",
+        "<img src=\"images/horizontal.png\" alt='horizontal' width='450'>\n",
+        "\n",
+        "If we decreased the size of these outliers relative to the bulk of the data, we could reduce the distortion of the horizontal axis. How can we do this? We need a transformation that will:\n",
+        "\n",
+        "* Decrease the magnitude of large x values by a significant amount.\n",
+        "* Not drastically change the magnitude of small x values.\n",
+        "\n",
+        "One function that produces this result is the **log transformation**. When we take the logarithm of a large number, the original number will decrease in magnitude dramatically. Conversely, when we take the logarithm of a small number, the original number does not change its value by as significant of an amount (to illustrate this, consider the difference between $\\log{(100)} = 4.61$ and $\\log{(10)} = 2.3$).\n",
+        "\n",
+        "In Data 100 (and most upper-division STEM classes), $\\log$ is used to refer to the natural logarithm with base $e$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "# np.log takes the logarithm of an array or Series\n",
+        "plt.scatter(np.log(df[\"inc\"]), df[\"lit\"])\n",
+        "\n",
+        "plt.xlabel(\"Log(gross national income per capita)\")\n",
+        "plt.ylabel(\"Adult literacy rate\")\n",
+        "plt.title(\"Adult literacy rate against Log(GNI per capita)\");"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After taking the logarithm of our x values, our plot appears much more balanced in its horizontal scale. We no longer have many datapoints clumped on one end and a few outliers out at extreme values. \n",
+        "\n",
+        "Let's repeat this reasoning for the y values. Considering only the vertical axis of the plot, notice how there are many datapoints concentrated at large y values. Only a few datapoints lie at smaller values of y.\n",
+        "\n",
+        "If we were to \"spread out\" these large values of y more, we would no longer see the dense concentration in one region of the y-axis. We need a transformation that will:\n",
+        "\n",
+        "* Increase the magnitude of large values of y so these datapoints are distributed more broadly on the vertical scale,\n",
+        "* Not substantially alter the scaling of small values of y (we do not want to drastically modify the lower end of the y axis, which is already distributed evenly on the vertical scale).\n",
+        "\n",
+        "In this case, it is helpful to apply a **power transformation** – that is, raise our y values to a power. Let's try raising our adult literacy rate values to the power of 4. Large values raised to the power of 4 will increase in magnitude proportionally much more than small values raised to the power of 4 (consider the difference between $2^4 = 16$ and $200^4 = 1600000000$). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "# Apply a log transformation to the x values and a power transformation to the y values\n",
+        "plt.scatter(np.log(df[\"inc\"]), df[\"lit\"]**4)\n",
+        "\n",
+        "plt.xlabel(\"Log(gross national income per capita)\")\n",
+        "plt.ylabel(\"Adult literacy rate (4th power)\")\n",
+        "plt.suptitle(\"Adult literacy rate (4th power) against Log(GNI per capita)\")\n",
+        "plt.subplots_adjust(top=0.9);"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Our scatter plot is looking a lot better! Now, we are plotting the log of our original x values on the horizontal axis, and the 4th power of our original y values on the vertical axis. We start to see an approximate *linear* relationship between our transformed variables. \n",
+        "\n",
+        "What can we take away from this? We now know that the log of gross national income and adult literacy to the power of 4 are roughly linearly related. If we denote the original, untransformed gross national income values as $x$ and the original adult literacy rate values as $y$, we can use the standard form of a linear fit to express this relationship:\n",
+        "\n",
+        "$$y^4 = m(\\log{x}) + b$$\n",
+        "\n",
+        "Where $m$ represents the slope of the linear fit, while $b$ represents the intercept. \n",
+        "\n",
+        "The cell below computes $m$ and $b$ for our transformed data. We'll discuss how this code was generated in a future lecture."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "#| code-fold: true\n",
+        "# The code below fits a linear regression model. We'll discuss it at length in a future lecture\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "model = LinearRegression()\n",
+        "model.fit(np.log(df[[\"inc\"]]), df[\"lit\"]**4)\n",
+        "m, b = model.coef_[0], model.intercept_\n",
+        "\n",
+        "print(f\"The slope, m, of the transformed data is: {m}\")\n",
+        "print(f\"The intercept, b, of the transformed data is: {b}\")\n",
+        "\n",
+        "df = df.sort_values(\"inc\")\n",
+        "plt.scatter(np.log(df[\"inc\"]), df[\"lit\"]**4, label=\"Transformed data\")\n",
+        "plt.plot(np.log(df[\"inc\"]), m*np.log(df[\"inc\"])+b, c=\"red\", label=\"Linear regression\")\n",
+        "plt.xlabel(\"Log(gross national income per capita)\")\n",
+        "plt.ylabel(\"Adult literacy rate (4th power)\")\n",
+        "plt.legend();"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "What if we want to understand the *underlying* relationship between our original variables, before they were transformed? We can simply rearrange our linear expression above! \n",
+        "\n",
+        "Recall our linear relationship between the transformed variables $\\log{x}$ and $y^4$. \n",
+        "\n",
+        "$$y^4 = m(\\log{x}) + b$$\n",
+        "\n",
+        "By rearranging the equation, we find a relationship between the untransformed variables $x$ and $y$. \n",
+        "\n",
+        "$$y = [m(\\log{x}) + b]^{(1/4)}$$\n",
+        "\n",
+        "When we plug in the values for $m$ and $b$ computed above, something interesting happens."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "vscode": {
+          "languageId": "python"
+        }
+      },
+      "source": [
+        "#| code-fold: true\n",
+        "# Now, plug the values for m and b into the relationship between the untransformed x and y\n",
+        "plt.scatter(df[\"inc\"], df[\"lit\"], label=\"Untransformed data\")\n",
+        "plt.plot(df[\"inc\"], (m*np.log(df[\"inc\"])+b)**(1/4), c=\"red\", label=\"Modeled relationship\")\n",
+        "plt.xlabel(\"Gross national income per capita\")\n",
+        "plt.ylabel(\"Adult literacy rate\")\n",
+        "plt.legend();"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have found a relationship between our original variables – gross national income and adult literacy rate!\n",
+        "\n",
+        "Transformations are powerful tools for understanding our data in greater detail. To summarize what we just achieved:\n",
+        "\n",
+        "* We identified appropriate transformations to **linearize** the original data.\n",
+        "* We used our knowledge of linear curves to compute the slope and intercept of the transformed data.\n",
+        "* We used this slope and intercept information to derive a relationship in the untransformed data.\n",
+        "\n",
+        "Linearization will be an important tool as we begin our work on linear modeling next week.\n",
+        "\n",
+        "#### Tukey-Mosteller Bulge Diagram\n",
+        "\n",
+        "The **Tukey-Mosteller Bulge Diagram** is a good guide when determining possible transformations to achieve linearity. It is a visual summary of the reasoning we just worked through above. \n",
+        "\n",
+        "<img src=\"images/tukey_mosteller.png\" alt='tukey_mosteller' width='300'>\n",
+        "\n",
+        "How does it work? Each curved \"bulge\" represents a possible shape of non-linear data. To use the diagram, find which of the four bulges resembles your dataset the most closely. Then, look at the axes of the quadrant for this bulge. The horizontal axis will list possible transformations that could be applied to your x data for linearization. Similarly, the vertical axis will list possible transformations that could be applied to your y data. Note that each axis lists two possible transformations. While *either* of these transformations has the *potential* to linearize your dataset, note that this is an iterative process. It's important to try out these transformations and look at the results to see whether you've actually achieved linearity. If not, you'll need to continue testing other possible transformations.\n",
+        "\n",
+        "Generally:\n",
+        "\n",
+        "* $\\sqrt{}$ and $\\log{}$ will reduce the magnitude of large values.\n",
+        "* Powers ($^2$ and $^3$) will increase the spread in magnitude of large values.\n",
+        "\n",
+        "\n",
+        "<center><img src=\"images/bulge.png\" alt='bulge' width='800'></img></a></center>\n",
+        "\n",
+        "\n",
+        "**Important:** You should still understand the *logic* we worked through to determine how best to transform the data. The bulge diagram is just a summary of this same reasoning. You will be expected to be able to explain why a given transformation is or is not appropriate for linearization.\n",
+        "\n",
+        "### Additional Remarks\n",
+        "\n",
+        "Visualization requires a lot of thought!\n",
+        "\n",
+        "- There are many tools for visualizing distributions.\n",
+        "    - Distribution of a single variable:\n",
+        "        1. Rugplot\n",
+        "        2. Histogram\n",
+        "        3. Density plot\n",
+        "        4. Box plot\n",
+        "        5. Violin plot\n",
+        "    - Joint distribution of two quantitative variables: \n",
+        "        1. Scatter plot\n",
+        "        2. Hex plot\n",
+        "        3. Contour plot\n",
+        "\n",
+        "This class primarily uses `seaborn` and `matplotlib`, but `pandas` also has basic built-in plotting methods. Many other visualization libraries exist, and `plotly` is one of them.\n",
+        "\n",
+        "- `plotly` creates very easily creates interactive plots.\n",
+        "- `plotly` will occasionally appear in lecture code, labs, and assignments! \n",
+        "\n",
+        "Next, we’ll go deeper into the theory behind visualization.\n",
+        "\n",
+        "## Visualization Theory\n",
+        "\n",
+        "This section marks a pivot to the second major topic of this lecture - visualization theory. We'll discuss the abstract nature of visualizations and analyze how they convey information.\n",
+        "\n",
+        "Remember, we had two goals for visualizing data. This section is particularly important in:\n",
+        "\n",
+        "1. Helping us understand the data and results,\n",
+        "2. Communicating our results and conclusions with others.\n",
+        "\n",
+        "### Information Channels\n",
+        "\n",
+        "Visualizations are able to convey information through various encodings. In the remainder of this lecture, we'll look at the use of color, scale, and depth, to name a few. \n",
+        "\n",
+        "#### Encodings in Rugplots\n",
+        "\n",
+        "One detail that we may have overlooked in our earlier discussion of rugplots is the importance of encodings. Rugplots are effective visuals because they utilize line thickness to encode frequency. Consider the following diagram:\n",
+        "\n",
+        "<img src=\"images/rugplot_encoding.png\" alt='rugplot_encoding' width='600'> \n",
+        "\n",
+        "#### Multi-Dimensional Encodings\n",
+        "\n",
+        "Encodings are also useful for representing multi-dimensional data. Notice how the following visual highlights four distinct \"dimensions\" of data:\n",
+        "\n",
+        "- X-axis\n",
+        "- Y-axis\n",
+        "- Area\n",
+        "- Color\n",
+        "\n",
+        "<img src=\"images/mutli_dim_encodings.png\" alt='multi_dim_encoding' width='400'> \n",
+        "\n",
+        "The human visual perception system is only capable of visualizing data in a three-dimensional plane, but as you've seen, we can encode many more channels of information.\n",
+        "\n",
+        "### Harnessing the Axes\n",
+        "\n",
+        "#### Consider the Scale of the Data\n",
+        "\n",
+        "However, we should be careful to not misrepresent relationships in our data by manipulating the scale or axes. The visualization below improperly portrays two seemingly independent relationships on the same plot. The authors have clearly changed the scale of the y-axis to mislead their audience.\n",
+        "\n",
+        "<img src=\"images/wrong_scale_viz.png\" alt='wrong_scale_viz' width='350'> \n",
+        "\n",
+        "Notice how the downwards-facing line segment contains values in the millions, while the upwards-trending segment only contains values near three hundred thousand. These lines should not be intersecting.\n",
+        "\n",
+        "When there is a large difference in the magnitude of the data, it's advised to analyze percentages instead of counts. The following diagrams correctly display the trends in cancer screening and abortion rates. \n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/good_viz_scale_1.png\" alt='good_viz_scale_1' width='345'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/good_viz_scale_2.png\" alt='good_viz_scale_2' width='345'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "#### Reveal the Data\n",
+        "\n",
+        "Great visualizations not only consider the scale of the data but also utilize the axes in a way that best conveys information. For example, data scientists commonly set certain axes limits to highlight parts of the visualization they are most interested in.\n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/unrevealed_viz.png\" alt='unrevealed_viz' width='345'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/revealed_viz.png\" alt='revealed_viz' width='345'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "The visualization on the right captures the trend in coronavirus cases during March of 2020. From only looking at the visualization on the left, a viewer may incorrectly believe that coronavirus began to skyrocket on March 4^th^, 2020. However, the second illustration tells a different story - cases rose closer to March 21^th^, 2020.\n",
+        "\n",
+        "### Harnessing Color\n",
+        "\n",
+        "Color is another important feature in visualizations that does more than what meets the eye. \n",
+        "\n",
+        "We already explored using color to encode a categorical variable in our scatter plot. Let's now discuss the uses of color in novel visualizations like colormaps and heatmaps.\n",
+        "\n",
+        "5-8% of the world is red-green color blind, so we have to be very particular about our color scheme. We want to make these as accessible as possible. Choosing a set of colors that work together is evidently a challenging task!\n",
+        "\n",
+        "#### Colormaps\n",
+        "\n",
+        "Colormaps are mappings from pixel data to color values, and they're often used to highlight distinct parts of an image. Let's investigate a few properties of colormaps.\n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "**Jet Colormap**\n",
+        "<img src=\"images/jet_colormap.png\" alt='jet_colormap' width='250'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "**Viridis Colormap**\n",
+        "<img src=\"images/viridis_colormap.png\" alt='viridis_colormap' width='250'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "The jet colormap is infamous for being misleading. While it seems more vibrant than viridis, the aggressive colors poorly encode numerical data. To understand why, let's analyze the following images.\n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/jet_four_by_four.png\" alt='four_by_four_colormap' width='300'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/jet_3_images.png\" alt='jet_3_colormap' width='250'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "The diagram on the left compares how a variety of colormaps represent pixel data that transitions from a high to low intensity. These include the jet colormap (row a) and grayscale (row b). Notice how the grayscale images do the best job in smoothly transitioning between pixel data. The jet colormap is the worst at this - the four images in row (a) look like a conglomeration of individual colors. \n",
+        "\n",
+        "The difference is also evident in the images labeled (a) and (b) on the left side. The grayscale image is better at preserving finer detail in the vertical line strokes. Additionally, grayscale is preferred in X-ray scans for being more neutral. The intensity of the dark red color in the jet colormap is frightening and indicates something is wrong.\n",
+        "\n",
+        "Why is the jet colormap so much worse? The answer lies in how its color composition is perceived to the human eye.\n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "**Jet Colormap Perception**\n",
+        "<img src=\"images/jet_perceptually_uniform.png\" alt='jet_perceptually_uniform' width='300'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "**Viridis Colormap Perception**\n",
+        "<img src=\"images/viridis_perceptually_uniform.png\" alt='viridis_perceptually_uniform' width='336'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "The jet colormap is largely misleading because it is not perceptually uniform. **Perceptually uniform colormaps** have the property that if the pixel data goes from 0.1 to 0.2, the perceptual change is the same as when the data goes from 0.8 to 0.9.\n",
+        "\n",
+        "Notice how the said uniformity is present within the linear trend displayed in the viridis colormap. On the other hand, the jet colormap is largely non-linear - this is precisely why it's considered a worse colormap.\n",
+        "\n",
+        "### Harnessing Markings\n",
+        "\n",
+        "In our earlier discussion of multi-dimensional encodings, we analyzed a scatter plot with four pseudo-dimensions: the two axes, area, and color. Were these appropriate to use? The following diagram analyzes how well the human eye can distinguish between these \"markings\".\n",
+        "\n",
+        "<img src=\"images/markings_viz.png\" alt='markings_viz' width='400'>\n",
+        "\n",
+        "There are a few key takeaways from this diagram\n",
+        "\n",
+        "- Lengths are easy to discern. Don't use plots with jiggled baselines - keep everything axis-aligned.\n",
+        "- Avoid pie charts! Angle judgments are inaccurate.\n",
+        "- Areas and volumes are hard to distinguish (area charts, word clouds, etc.).\n",
+        "\n",
+        "### Harnessing Conditioning\n",
+        "\n",
+        "Conditioning is the process of comparing data that belong to separate groups. We've seen this before in overlayed distributions, side-by-side box plots, and scatter plots with categorical encodings. Here, we'll introduce terminology that formalizes these examples.\n",
+        "\n",
+        "Consider an example where we want to analyze income earnings for males and females with varying levels of education. There are multiple ways to compare this data. \n",
+        "\n",
+        ":::: {.columns}\n",
+        "\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/male_female_earnings_barplot.png\" alt='jet_perceptually_uniform' width='360'>\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"20%\"}\n",
+        "<!-- empty column to create gap -->\n",
+        ":::\n",
+        "\n",
+        "::: {.column width=\"30%\"}\n",
+        "<img src=\"images/male_female_earnings_scatterplot.png\" alt='viridis_perceptually_uniform' width='340'>\n",
+        ":::\n",
+        "\n",
+        "::::\n",
+        "\n",
+        "The barplot is an example of **juxtaposition**: placing multiple plots side by side, with the same scale. The scatter plot is an example of **superposition**: placing multiple density curves and scatter plots on top of each other. \n",
+        "\n",
+        "Which is better depends on the problem at hand. Here, superposition makes the precise wage difference very clear from a quick glance. However, many sophisticated plots convey information that favors the use of juxtaposition. Below is one example.\n",
+        "\n",
+        "<img src=\"images/small_multiples.png\" alt='small_multiples' width='600'>\n",
+        "                                                        \n",
+        "\n",
+        "### Harnessing Context\n",
+        "\n",
+        "The last component of a great visualization is perhaps the most critical - the use of context. Adding informative titles, axis labels, and descriptive captions are all best practices that we've heard repeatedly in Data 8.\n",
+        "\n",
+        "A publication-ready plot (and every Data 100 plot) needs:\n",
+        "\n",
+        "- Informative title (takeaway, not description),\n",
+        "- Axis labels,\n",
+        "- Reference lines, markers, etc,\n",
+        "- Legends, if appropriate,\n",
+        "- Captions that describe data,\n",
+        "\n",
+        "Captions should:\n",
+        "\n",
+        "- Be comprehensive and self-contained,\n",
+        "- Describe what has been graphed,\n",
+        "- Draw attention to important features,\n",
+        "- Describe conclusions drawn from graphs."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "jupytext": {
+      "text_representation": {
+        "extension": ".qmd",
+        "format_name": "quarto",
+        "format_version": "1.0",
+        "jupytext_version": "1.16.1"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/visualization_2/visualization_2.qmd b/visualization_2/visualization_2.qmd
index dbf6545f..5861eeed 100644
--- a/visualization_2/visualization_2.qmd
+++ b/visualization_2/visualization_2.qmd
@@ -48,6 +48,9 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+import warnings 
+
+warnings.filterwarnings("ignore", "use_inf_as_na") # Supresses distracting deprecation warnings
 
 wb = pd.read_csv("data/world_bank.csv", index_col=0)
 wb = wb.rename(columns={'Antiretroviral therapy coverage: % of people living with HIV: 2015':"HIV rate",
@@ -204,7 +207,20 @@ plt.ylim(0, 0.5)
 plt.xlabel("Data")
 plt.ylabel("Density")
 
-plot_kde(gaussian_kernel, data, a = 1)
+sns.kdeplot(data, bw_method=0.65)
+sns.histplot(data, stat="density", bins=2);
+```
+
+An alternative method to generate the above KDE is shown below, this time using `sns.histplot`'s arguments.
+
+```{python}
+#| code-fold: true
+plt.xlim(-3, 10)
+plt.ylim(0, 0.5)
+plt.xlabel("Data")
+plt.ylabel("Density")
+
+sns.histplot(data, bins=2, kde=True, stat="density", kde_kws=dict(cut=3, bw_method=0.65))
 ```
 
 ### Kernel Functions and Bandwidths
@@ -249,9 +265,9 @@ The details of this (admittedly intimidating) formula are less important than un
 | --- | --- |
 | <img src="images/gaussian_0.1.png" alt='gaussian_0.1' width='345'> | <img src="images/gaussian_1.png" alt='gaussian_1' width='345'> | 
 
-| Gaussian Kernel, $\alpha$ = 2 | Gaussian Kernel, $\alpha$ = 10 | 
+| Gaussian Kernel, $\alpha$ = 2 | Gaussian Kernel, $\alpha$ = 5 | 
 |  --- | --- | 
-| <img src="images/gaussian_2.png" alt='gaussian_2' width='345'> | <img src="images/gaussian_10.png" alt='gaussian_10' width='345'> | 
+| <img src="images/gaussian_2.png" alt='gaussian_2' width='345'> | <img src="images/gaussian_5.png" alt='gaussian_5' width='345'> | 
 
 #### Boxcar Kernel