diff --git a/docs/gradient_descent/gradient_descent.html b/docs/gradient_descent/gradient_descent.html
index 1560da8a..86ea8e02 100644
--- a/docs/gradient_descent/gradient_descent.html
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diff --git a/docs/index.html b/docs/index.html
index cd4c0959..1b0fce63 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -8,6 +8,7 @@
 
 <meta name="author" content="Bella Crouch">
 <meta name="author" content="Yash Dave">
+<meta name="author" content="Ian Dong">
 <meta name="author" content="Kanu Grover">
 <meta name="author" content="Ishani Gupta">
 <meta name="author" content="Sakshi Kolli">
@@ -294,6 +295,7 @@ <h1 class="title">Principles and Techniques of Data Science</h1>
     <div class="quarto-title-meta-contents">
              <p>Bella Crouch </p>
              <p>Yash Dave </p>
+             <p>Ian Dong </p>
              <p>Kanu Grover </p>
              <p>Ishani Gupta </p>
              <p>Sakshi Kolli </p>
diff --git a/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png b/docs/intro_to_modeling/intro_to_modeling_files/figure-html/cell-2-output-1.png
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diff --git a/docs/logistic_regression_1/logistic_reg_1_files/figure-html/cell-4-output-1.png b/docs/logistic_regression_1/logistic_reg_1_files/figure-html/cell-4-output-1.png
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diff --git a/docs/logistic_regression_1/logistic_reg_1_files/figure-html/cell-8-output-1.png b/docs/logistic_regression_1/logistic_reg_1_files/figure-html/cell-8-output-1.png
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diff --git a/docs/pandas_2/pandas_2.html b/docs/pandas_2/pandas_2.html
index 812e988c..a457b54c 100644
--- a/docs/pandas_2/pandas_2.html
+++ b/docs/pandas_2/pandas_2.html
@@ -1668,12 +1668,12 @@ <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">79462</td>
+<td data-quarto-table-cell-role="th">331926</td>
 <td>CA</td>
-<td>F</td>
-<td>1978</td>
-<td>Rosalind</td>
-<td>13</td>
+<td>M</td>
+<td>1995</td>
+<td>Kohl</td>
+<td>7</td>
 </tr>
 </tbody>
 </table>
@@ -1700,34 +1700,34 @@ <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">160002</td>
-<td>2002</td>
-<td>Brieana</td>
+<td data-quarto-table-cell-role="th">51257</td>
+<td>1964</td>
+<td>Tonie</td>
 <td>5</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">81577</td>
-<td>1979</td>
-<td>Lucinda</td>
-<td>21</td>
+<td data-quarto-table-cell-role="th">137876</td>
+<td>1996</td>
+<td>Saki</td>
+<td>6</td>
 </tr>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">98007</td>
-<td>1985</td>
-<td>Tammie</td>
-<td>15</td>
+<td data-quarto-table-cell-role="th">366718</td>
+<td>2008</td>
+<td>Keita</td>
+<td>5</td>
 </tr>
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-<td data-quarto-table-cell-role="th">267649</td>
-<td>1956</td>
-<td>Debra</td>
-<td>11</td>
+<td data-quarto-table-cell-role="th">369863</td>
+<td>2010</td>
+<td>Carlos</td>
+<td>1133</td>
 </tr>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">316403</td>
-<td>1989</td>
-<td>Esai</td>
-<td>9</td>
+<td data-quarto-table-cell-role="th">403962</td>
+<td>2021</td>
+<td>Heber</td>
+<td>6</td>
 </tr>
 </tbody>
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 </thead>
 <tbody>
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-<td data-quarto-table-cell-role="th">152740</td>
+<td data-quarto-table-cell-role="th">150028</td>
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-<td>Shaniya</td>
-<td>5</td>
+<td>Mara</td>
+<td>31</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">152430</td>
+<td data-quarto-table-cell-role="th">151231</td>
 <td>2000</td>
-<td>Diva</td>
-<td>5</td>
+<td>Alexcia</td>
+<td>9</td>
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 <tr class="odd">
-<td data-quarto-table-cell-role="th">344434</td>
+<td data-quarto-table-cell-role="th">149360</td>
 <td>2000</td>
-<td>Eriq</td>
-<td>6</td>
+<td>Sarai</td>
+<td>146</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">342872</td>
+<td data-quarto-table-cell-role="th">343226</td>
 <td>2000</td>
-<td>Justice</td>
-<td>65</td>
+<td>Kurtis</td>
+<td>24</td>
 </tr>
 </tbody>
 </table>
diff --git a/docs/pandas_3/pandas_3.html b/docs/pandas_3/pandas_3.html
index cb0299ee..8c780b6a 100644
--- a/docs/pandas_3/pandas_3.html
+++ b/docs/pandas_3/pandas_3.html
@@ -960,7 +960,7 @@ <h2 data-number="4.2" class="anchored" data-anchor-id="aggregating-data-with-.gr
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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 0x107ce7100&gt;</code></pre>
+<pre><code>&lt;pandas.core.groupby.generic.DataFrameGroupBy object at 0x10822aaf0&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>
@@ -1495,9 +1495,9 @@ <h3 data-number="4.2.2" class="anchored" data-anchor-id="plotting-birth-counts">
 </details>
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diff --git a/docs/regex/regex.html b/docs/regex/regex.html
index 194a5378..3671cbb1 100644
--- a/docs/regex/regex.html
+++ b/docs/regex/regex.html
@@ -704,11 +704,11 @@ <h4 data-number="6.2.1.2" class="anchored" data-anchor-id="canonicalization-with
 <span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>county_and_state[<span class="st">'clean_county_pandas'</span>] <span class="op">=</span> canonicalize_county_series(county_and_state[<span class="st">'County'</span>])</span>
 <span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>display(county_and_pop), display(county_and_state)<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>/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49409/2523629438.py:3: FutureWarning:
+<pre><code>/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_61725/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49409/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_61725/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 </code></pre>
diff --git a/docs/sampling/sampling.html b/docs/sampling/sampling.html
index 8f46b2e9..a6d6eb6a 100644
--- a/docs/sampling/sampling.html
+++ b/docs/sampling/sampling.html
@@ -722,7 +722,7 @@ <h4 data-number="9.3.3.3" class="anchored" data-anchor-id="simple-random-sample"
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>random_sample <span class="op">=</span> movie.sample(n, replace <span class="op">=</span> <span class="va">False</span>) <span class="co">## By default, replace = False</span></span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>np.mean(random_sample[<span class="st">"barbie"</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>0.5311745261494285</code></pre>
+<pre><code>0.5301227615221094</code></pre>
 </div>
 </div>
 <p>This is very close to the actual vote of 0.5302792307692308!</p>
@@ -740,7 +740,7 @@ <h4 data-number="9.3.3.3" class="anchored" data-anchor-id="simple-random-sample"
 <span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>Markdown(<span class="ss">f"**Actual** = </span><span class="sc">{</span>actual_barbie<span class="sc">:.4f}</span><span class="ss">, **Sample** = </span><span class="sc">{</span>sample_barbie<span class="sc">:.4f}</span><span class="ss">, "</span></span>
 <span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>         <span class="ss">f"**Err** = </span><span class="sc">{</span><span class="dv">100</span><span class="op">*</span>err<span class="sc">:.2f}</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-display" data-execution_count="10">
-<p><strong>Actual</strong> = 0.5303, <strong>Sample</strong> = 0.5450, <strong>Err</strong> = 2.78%.</p>
+<p><strong>Actual</strong> = 0.5303, <strong>Sample</strong> = 0.5225, <strong>Err</strong> = 1.47%.</p>
 </div>
 </div>
 <p>We’ll learn how to choose this number when we (re)learn the Central Limit Theorem later in the semester.</p>
@@ -773,7 +773,7 @@ <h4 data-number="9.3.3.4" class="anchored" data-anchor-id="quantifying-chance-er
 <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>poll_result <span class="op">=</span> pd.Series(poll_result)</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>np.<span class="bu">sum</span>(poll_result <span class="op">&gt;</span> <span class="fl">0.5</span>)<span class="op">/</span><span class="dv">1000</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>0.956</code></pre>
+<pre><code>0.936</code></pre>
 </div>
 </div>
 <p>You can see the curve looks roughly Gaussian/normal. Using KDE:</p>
diff --git a/docs/sampling/sampling_files/figure-html/cell-13-output-1.png b/docs/sampling/sampling_files/figure-html/cell-13-output-1.png
index 03cbd63d..8093e579 100644
Binary files a/docs/sampling/sampling_files/figure-html/cell-13-output-1.png and b/docs/sampling/sampling_files/figure-html/cell-13-output-1.png differ
diff --git a/docs/sampling/sampling_files/figure-html/cell-15-output-1.png b/docs/sampling/sampling_files/figure-html/cell-15-output-1.png
index 38c53ce5..e4a53d29 100644
Binary files a/docs/sampling/sampling_files/figure-html/cell-15-output-1.png and b/docs/sampling/sampling_files/figure-html/cell-15-output-1.png differ
diff --git a/docs/sql_I/sql_I.html b/docs/sql_I/sql_I.html
index 3741231a..2bdbb910 100644
--- a/docs/sql_I/sql_I.html
+++ b/docs/sql_I/sql_I.html
@@ -395,19 +395,15 @@ <h2 data-number="20.2" class="anchored" data-anchor-id="intro-to-sql"><span clas
 <div 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><span class="co"># Load %%sql cell magic</span></span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext sql</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>There's a new jupysql version available (0.10.10), you're running 0.8.0. To upgrade: pip install jupysql --upgrade
-Deploy Shiny apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup</code></pre>
-</div>
 </div>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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"># Connect to the database</span></span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>example_duck.db <span class="op">--</span>alias duck</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><span class="co"># Connect to the database</span></span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>example_duck.db <span class="op">--</span>alias duck</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now that we’re connected, let’s make some queries!</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<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><span class="op">%%</span>sql</span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -462,10 +458,10 @@ <h2 data-number="20.3" class="anchored" data-anchor-id="tables-and-schema"><span
 <p>Looking at the <code>Dragon</code> table above, we can see that it contains contains three columns. The first of these, <code>"name"</code>, contains text data. The <code>"year"</code> column contains integer data, with the constraint that year values must be greater than or equal to 2000. The final column, <code>"cute"</code>, contains integer data with no restrictions on allowable values.</p>
 <p>Now, let’s look at the <strong>schema</strong> of our database. A schema describes the logical structure of a table. Whenever a new table is created, the creator must declare its schema.</p>
 <div 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="op">%%</span>sql</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>FROM sqlite_master</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>WHERE <span class="bu">type</span><span class="op">=</span><span class="st">'table'</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="op">%%</span>sql</span>
+<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>FROM sqlite_master</span>
+<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>WHERE <span class="bu">type</span><span class="op">=</span><span class="st">'table'</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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -569,9 +565,9 @@ <h2 data-number="20.4" class="anchored" data-anchor-id="basic-queries"><span cla
 <h3 data-number="20.4.1" class="anchored" data-anchor-id="selecting-from-tables"><span class="header-section-number">20.4.1</span> <code>SELECT</code>ing From Tables</h3>
 <p>The basic unit of a SQL query is the <code>SELECT</code> statement. <code>SELECT</code> specifies what columns we would like to extract from a given table. We use <code>FROM</code> to tell SQL the table from which we want to <code>SELECT</code> our data.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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="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><span class="op">%%</span>sql</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -618,9 +614,9 @@ <h3 data-number="20.4.1" class="anchored" data-anchor-id="selecting-from-tables"
 </div>
 <p>In SQL, <code>*</code> means “everything.” The query above grabs <em>all</em> the columns in <code>Dragon</code> and displays them in the outputted table. We can also specify a specific subset of columns to be <code>SELECT</code>ed.&nbsp;Notice that the outputted columns appear in the order they were <code>SELECT</code>ed.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="7">
-<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="op">%%</span>sql</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>SELECT cute, year</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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="op">%%</span>sql</span>
+<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>SELECT cute, year</span>
+<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -674,9 +670,9 @@ <h4 data-number="20.4.1.2" class="anchored" data-anchor-id="aliasing-with-as"><s
 <p>The <code>AS</code> keyword allows us to give a column a new name (called an <strong>alias</strong>) after it has been <code>SELECT</code>ed.&nbsp;The general syntax is:</p>
 <pre><code>SELECT column_in_input_table AS new_name_in_output_table</code></pre>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>SELECT cute AS cuteness, year AS birth</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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="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="op">%%</span>sql</span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>SELECT cute AS cuteness, year AS birth</span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -720,9 +716,9 @@ <h4 data-number="20.4.1.2" class="anchored" data-anchor-id="aliasing-with-as"><s
 <h4 data-number="20.4.1.3" class="anchored" data-anchor-id="uniqueness-with-distinct"><span class="header-section-number">20.4.1.3</span> Uniqueness with <code>DISTINCT</code></h4>
 <p>To <code>SELECT</code> only the <em>unique</em> values in a column, we use the <code>DISTINCT</code> keyword. This will cause any any duplicate entries in a column to be removed. If we want to find only the unique years in <code>Dragon</code>, without any repeats, we would write:</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="9">
-<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="op">%%</span>sql</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>SELECT DISTINCT year</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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="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="op">%%</span>sql</span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>SELECT DISTINCT year</span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -755,10 +751,10 @@ <h4 data-number="20.4.1.3" class="anchored" data-anchor-id="uniqueness-with-dist
 <h3 data-number="20.4.2" class="anchored" data-anchor-id="applying-where-conditions"><span class="header-section-number">20.4.2</span> Applying <code>WHERE</code> Conditions</h3>
 <p>The <code>WHERE</code> keyword is used to select only some rows of a table, filtered on a given Boolean condition.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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>sql</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</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="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="op">%%</span>sql</span>
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -787,10 +783,10 @@ <h3 data-number="20.4.2" class="anchored" data-anchor-id="applying-where-conditi
 </div>
 <p>We can add complexity to the <code>WHERE</code> condition using the keywords <code>AND</code>, <code>OR</code>, and <code>NOT</code>, much like we would in Python.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="11">
-<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="op">%%</span>sql</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span> OR year <span class="op">&gt;</span> <span class="dv">2013</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="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>sql</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span> OR year <span class="op">&gt;</span> <span class="dv">2013</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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -823,10 +819,10 @@ <h3 data-number="20.4.2" class="anchored" data-anchor-id="applying-where-conditi
 </div>
 <p>To spare ourselves needing to write complicated logical expressions by combining several conditions, we can also filter for entries that are <code>IN</code> a specified list of values. This is similar to the use of <code>in</code> or <code>.isin</code> in Python.</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>WHERE name IN (<span class="st">'hiccup'</span>, <span class="st">'puff'</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="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="op">%%</span>sql</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>WHERE name IN (<span class="st">'hiccup'</span>, <span class="st">'puff'</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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -865,10 +861,10 @@ <h4 data-number="20.4.2.1" class="anchored" data-anchor-id="strings-in-sql"><spa
 <h4 data-number="20.4.2.2" class="anchored" data-anchor-id="where-with-null-values"><span class="header-section-number">20.4.2.2</span> <code>WHERE</code> WITH <code>NULL</code> Values</h4>
 <p>You may have noticed earlier that our table actually has a missing value. In SQL, missing data is given the special value <code>NULL</code>. <code>NULL</code> behaves in a fundamentally different way to other data types. We can’t use the typical operators (=, &gt;, and &lt;) on <code>NULL</code> values (in fact, <code>NULL == NULL</code> returns <code>False</code>!). Instead, we check to see if a value <code>IS</code> or <code>IS NOT</code> <code>NULL</code>.</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>SELECT name, cute</span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>WHERE cute IS NOT NULL<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="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="op">%%</span>sql</span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>SELECT name, cute</span>
+<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>WHERE cute IS NOT NULL<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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 <span style="None">Running query in 'duck'</span>
 </div>
@@ -911,10 +907,10 @@ <h3 data-number="20.4.3" class="anchored" data-anchor-id="sorting-and-restrictin
 <h4 data-number="20.4.3.1" class="anchored" data-anchor-id="sorting-with-order-by"><span class="header-section-number">20.4.3.1</span> Sorting with <code>ORDER BY</code></h4>
 <p>What if we want the output table to appear in a certain order? The <code>ORDER BY</code> keyword behaves similarly to <code>.sort_values()</code> in <code>pandas</code>.</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>ORDER BY cute<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="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>sql</span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>ORDER BY cute<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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 <span style="None">Running query in 'duck'</span>
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@@ -961,10 +957,10 @@ <h4 data-number="20.4.3.1" class="anchored" data-anchor-id="sorting-with-order-b
 </div>
 <p>By default, <code>ORDER BY</code> will display results in ascending order (<code>ASC</code>) with the lowest values at the top of the table. To sort in descending order, we use the <code>DESC</code> keyword after specifying the column to be used for ordering.</p>
 <div 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="op">%%</span>sql</span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>ORDER BY cute DESC<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="op">%%</span>sql</span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>ORDER BY cute DESC<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -1011,10 +1007,10 @@ <h4 data-number="20.4.3.1" class="anchored" data-anchor-id="sorting-with-order-b
 </div>
 <p>We can also tell SQL to <code>ORDER BY</code> two columns at once. This will sort the table by the first listed column, then use the values in the second listed column to break any ties.</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>ORDER BY year, cute DESC<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="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="op">%%</span>sql</span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>ORDER BY year, cute DESC<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">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -1065,10 +1061,10 @@ <h4 data-number="20.4.3.1" class="anchored" data-anchor-id="sorting-with-order-b
 <h4 data-number="20.4.3.2" class="anchored" data-anchor-id="limit-vs.-offset"><span class="header-section-number">20.4.3.2</span> <code>LIMIT</code> vs.&nbsp;<code>OFFSET</code></h4>
 <p>In many instances, we are only concerned with a certain number of rows in the output table (for example, wanting to find the first two dragons in the table). The <code>LIMIT</code> keyword restricts the output to a specified number of rows. It serves a function similar to that of <code>.head()</code> in <code>pandas</code>.</p>
 <div 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><span class="op">%%</span>sql</span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>LIMIT <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>
+<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="op">%%</span>sql</span>
+<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>LIMIT <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>
 <div class="cell-output cell-output-display">
 <span style="None">Running query in 'duck'</span>
 </div>
@@ -1100,11 +1096,11 @@ <h4 data-number="20.4.3.2" class="anchored" data-anchor-id="limit-vs.-offset"><s
 </div>
 <p>The <code>OFFSET</code> keyword indicates the index at which <code>LIMIT</code> should start. In other words, we can use <code>OFFSET</code> to shift where the <code>LIMIT</code>ing begins by a specified number of rows. For example, we might care about the dragons that are at positions 2 and 3 in the table.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
-<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="op">%%</span>sql</span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span></span>
-<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>OFFSET <span class="dv">1</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="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="op">%%</span>sql</span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span></span>
+<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>OFFSET <span class="dv">1</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">
 <span style="None">Running query in 'duck'</span>
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@@ -1458,360 +1454,360 @@ <h2 data-number="20.5" class="anchored" data-anchor-id="summary"><span class="he
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb28" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> SQL I</span></span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a><span class="co">  echo: true</span></span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
-<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
-<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: false</span></span>
-<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
-<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
-<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a><span class="co">    toc-title: SQL I</span></span>
-<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a><span class="co">    page-layout: full</span></span>
-<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a><span class="co">    theme:</span></span>
-<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a><span class="co">      - cosmo</span></span>
-<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a><span class="co">      - cerulean</span></span>
-<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a><span class="co">    callout-icon: false</span></span>
-<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a><span class="an">jupyter:</span></span>
-<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a><span class="co">  jupytext:</span></span>
-<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a><span class="co">    text_representation:</span></span>
-<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a><span class="co">      extension: .qmd</span></span>
-<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a><span class="co">      format_name: quarto</span></span>
-<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a><span class="co">      format_version: '1.0'</span></span>
-<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a><span class="co">      jupytext_version: 1.16.1</span></span>
-<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a><span class="co">  kernelspec:</span></span>
-<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a><span class="co">    display_name: Python 3 (ipykernel)</span></span>
-<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a><span class="co">    language: python</span></span>
-<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a><span class="co">    name: python3</span></span>
-<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a>::: {.callout-note collapse="false"}</span>
-<span id="cb28-30"><a href="#cb28-30" aria-hidden="true" tabindex="-1"></a><span class="fu">## Learning Outcomes</span></span>
-<span id="cb28-31"><a href="#cb28-31" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Recognizing situations where we need “bigger” tools for manipulating data</span>
-<span id="cb28-32"><a href="#cb28-32" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Write basic SQL queries using <span class="in">`SELECT`</span>, <span class="in">`FROM`</span>, <span class="in">`WHERE`</span>, <span class="in">`ORDER BY`</span>, <span class="in">`LIMIT`</span>, and <span class="in">`OFFSET`</span></span>
-<span id="cb28-33"><a href="#cb28-33" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform aggregations using <span class="in">`GROUP BY`</span></span>
-<span id="cb28-34"><a href="#cb28-34" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb28-35"><a href="#cb28-35" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-36"><a href="#cb28-36" aria-hidden="true" tabindex="-1"></a>So far in the course, we have made our way through the entire data science lifecycle: we learned how to load and explore a dataset, formulate questions, and use the tools of prediction and inference to come up with answers. For the remaining weeks of the semester, we are going to make a second pass through the lifecycle, this time with a different set of tools, ideas, and abstractions. </span>
-<span id="cb28-37"><a href="#cb28-37" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-38"><a href="#cb28-38" aria-hidden="true" tabindex="-1"></a><span class="fu">## Databases</span></span>
-<span id="cb28-39"><a href="#cb28-39" aria-hidden="true" tabindex="-1"></a>With this goal in mind, let's go back to the very beginning of the lifecycle. We first started our work in data analysis by looking at the <span class="in">`pandas`</span> library, which offered us powerful tools to manipulate tabular data stored in (primarily) CSV files. CSVs work well when analyzing relatively small datasets (less than 10GB) that don't need to be shared across many users. In research and industry, however, data scientists often need to access enormous bodies of data that cannot be easily stored in a CSV format. Collaborating with others when working with CSVs can also be tricky —— a real-world data scientist may run into problems when multiple users try to make modifications or more dire security issues arise regarding who should and should not have access to the data. </span>
-<span id="cb28-40"><a href="#cb28-40" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-41"><a href="#cb28-41" aria-hidden="true" tabindex="-1"></a>A **database** is a large, organized collection of data. Databases are administered by **Database Management Systems (DBMS)**, which are software systems that store, manage, and facilitate access to one or more databases. Databases help mitigate many of the issues that come with using CSVs for data storage: they provide reliable storage that can survive system crashes or disk failures, are optimized to compute on data that does not fit into memory, and contain special data structures to improve performance. Using databases rather than CSVs offers further benefits from the standpoint of data management. A DBMS can apply settings that configure how data is organized, block certain data anomalies (for example, enforcing non-negative weights or ages), and determine who is allowed access to the data. It can also ensure safe concurrent operations where multiple users reading and writing to the database will not lead to fatal errors. Below, you can see the functionality of the different types of data storage and management architectures. In data science, common large-scale DBMS systems used are Google BigQuery, Amazon Redshift, Snowflake, Databricks, Microsoft SQL Server, and more. To learn more about these, consider taking <span class="co">[</span><span class="ot">Data 101</span><span class="co">](https://www.data101.org/sp24/)</span>!</span>
-<span id="cb28-42"><a href="#cb28-42" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-43"><a href="#cb28-43" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;p</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align:center"</span><span class="kw">&gt;</span></span>
-<span id="cb28-44"><a href="#cb28-44" aria-hidden="true" tabindex="-1"></a>    <span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">'images/data_storage.png'</span> <span class="er">width</span><span class="ot">=</span><span class="st">"700px"</span> <span class="kw">/&gt;</span></span>
-<span id="cb28-45"><a href="#cb28-45" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/p&gt;</span></span>
-<span id="cb28-46"><a href="#cb28-46" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-47"><a href="#cb28-47" aria-hidden="true" tabindex="-1"></a>As you may have guessed, we can't use our usual <span class="in">`pandas`</span> methods to work with data in a database. Instead, we'll turn to Structured Query Language.</span>
-<span id="cb28-48"><a href="#cb28-48" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-49"><a href="#cb28-49" aria-hidden="true" tabindex="-1"></a><span class="fu">## Intro to SQL</span></span>
-<span id="cb28-50"><a href="#cb28-50" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-51"><a href="#cb28-51" aria-hidden="true" tabindex="-1"></a>**Structured Query Language**, or **SQL** (commonly pronounced "sequel," though this is the subject of [fierce debate](https://patorjk.com/blog/2012/01/26/pronouncing-sql-s-q-l-or-sequel/)), is a special programming language designed to communicate with databases, and it is the dominant language/technology for working with data. You may have encountered it in classes like CS 61A or Data C88C before, and you likely will encounter it in the future. It is a language of tables: all inputs and outputs are tables. Unlike Python, it is a **declarative programming language** – this means that rather than writing the exact logic needed to complete a task, a piece of SQL code "declares" what the desired final output should be and leaves the program to determine what logic should be implemented. This logic differs depending on the SQL code itself or on the system it's running on (ie. <span class="co">[</span><span class="ot">MongoDB</span><span class="co">](https://www.mongodb.com/)</span>, <span class="co">[</span><span class="ot">SQLite</span><span class="co">](https://www.sqlite.org/)</span>, <span class="co">[</span><span class="ot">DuckDB</span><span class="co">](https://duckdb.org/)</span>, etc.). Most systems don’t follow the standards, and every system you work with will be a little different. </span>
-<span id="cb28-52"><a href="#cb28-52" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-53"><a href="#cb28-53" aria-hidden="true" tabindex="-1"></a>For the purposes of Data 100, we use SQLite or DuckDB. SQLite is an easy-to-use library that allows users to directly manipulate a database file or an in-memory database with a simplified version of SQL. It's commonly used to store data for small apps on mobile devices and is optimized for simplicity and speed of simple data tasks. DuckDB is an easy-to-use library that lets you directly manipulate a database file, collection of table formatted files (e.g., CSV), or in-memory <span class="in">`pandas`</span> <span class="in">`DataFrame`</span>s using a more complete version of SQL. It's optimized for simplicity and speed of advanced data analysis tasks and is becoming increasingly popular for data analysis tasks on large datasets.</span>
-<span id="cb28-54"><a href="#cb28-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-55"><a href="#cb28-55" aria-hidden="true" tabindex="-1"></a>It is important to reiterate that SQL is an entirely different language from Python. However, Python *does* have special engines that allow us to run SQL code in a Jupyter notebook. While this is typically not how SQL is used outside of an educational setting, we will use this workflow to illustrate how SQL queries are constructed using the tools we've already worked with this semester. You will learn more about how to run SQL queries in Jupyter in Lab 10 and Homework 10.</span>
-<span id="cb28-56"><a href="#cb28-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-57"><a href="#cb28-57" aria-hidden="true" tabindex="-1"></a>The syntax below will seem unfamiliar to you; for now, just focus on understanding the output displayed. We will clarify the SQL code in a bit.</span>
-<span id="cb28-58"><a href="#cb28-58" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-59"><a href="#cb28-59" aria-hidden="true" tabindex="-1"></a>To start, we'll look at a database called <span class="in">`example_duck.db`</span> and connect to it using DuckDB.</span>
-<span id="cb28-60"><a href="#cb28-60" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-63"><a href="#cb28-63" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-64"><a href="#cb28-64" aria-hidden="true" tabindex="-1"></a><span class="co">#| code-fold: true</span></span>
-<span id="cb28-65"><a href="#cb28-65" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-66"><a href="#cb28-66" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the SQL Alchemy Python library and DuckDB</span></span>
-<span id="cb28-67"><a href="#cb28-67" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sqlalchemy</span>
-<span id="cb28-68"><a href="#cb28-68" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> duckdb</span>
-<span id="cb28-69"><a href="#cb28-69" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-70"><a href="#cb28-70" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-73"><a href="#cb28-73" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-74"><a href="#cb28-74" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-75"><a href="#cb28-75" aria-hidden="true" tabindex="-1"></a><span class="co"># Load %%sql cell magic</span></span>
-<span id="cb28-76"><a href="#cb28-76" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext sql</span>
-<span id="cb28-77"><a href="#cb28-77" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-78"><a href="#cb28-78" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-81"><a href="#cb28-81" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-82"><a href="#cb28-82" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-83"><a href="#cb28-83" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the database</span></span>
-<span id="cb28-84"><a href="#cb28-84" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>example_duck.db <span class="op">--</span>alias duck</span>
-<span id="cb28-85"><a href="#cb28-85" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-86"><a href="#cb28-86" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-87"><a href="#cb28-87" aria-hidden="true" tabindex="-1"></a>Now that we’re connected, let’s make some queries!</span>
-<span id="cb28-88"><a href="#cb28-88" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-91"><a href="#cb28-91" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-92"><a href="#cb28-92" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-93"><a href="#cb28-93" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-94"><a href="#cb28-94" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<span class="op">;</span></span>
-<span id="cb28-95"><a href="#cb28-95" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-96"><a href="#cb28-96" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-97"><a href="#cb28-97" aria-hidden="true" tabindex="-1"></a>Thanks to the <span class="in">`pandas`</span> magic, the resulting return data is displayed in a format almost identical to our <span class="in">`pandas`</span> tables but without an index.</span>
-<span id="cb28-98"><a href="#cb28-98" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-99"><a href="#cb28-99" aria-hidden="true" tabindex="-1"></a><span class="fu">## Tables and Schema</span></span>
-<span id="cb28-100"><a href="#cb28-100" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;p</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align:center"</span><span class="kw">&gt;</span></span>
-<span id="cb28-101"><a href="#cb28-101" aria-hidden="true" tabindex="-1"></a>    <span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">'images/sql_terminology.png'</span> <span class="er">width</span><span class="ot">=</span><span class="st">"700px"</span> <span class="kw">/&gt;</span></span>
-<span id="cb28-102"><a href="#cb28-102" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/p&gt;</span></span>
-<span id="cb28-103"><a href="#cb28-103" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-104"><a href="#cb28-104" aria-hidden="true" tabindex="-1"></a>Looking at the <span class="in">`Dragon`</span> table above, we can see that it contains contains three columns. The first of these, <span class="in">`"name"`</span>, contains text data. The <span class="in">`"year"`</span> column contains integer data, with the constraint that year values must be greater than or equal to 2000. The final column, <span class="in">`"cute"`</span>, contains integer data with no restrictions on allowable values.</span>
-<span id="cb28-105"><a href="#cb28-105" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-106"><a href="#cb28-106" aria-hidden="true" tabindex="-1"></a>Now, let's look at the **schema** of our database. A schema describes the logical structure of a table. Whenever a new table is created, the creator must declare its schema.</span>
-<span id="cb28-107"><a href="#cb28-107" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-110"><a href="#cb28-110" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-111"><a href="#cb28-111" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-112"><a href="#cb28-112" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-113"><a href="#cb28-113" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
-<span id="cb28-114"><a href="#cb28-114" aria-hidden="true" tabindex="-1"></a>FROM sqlite_master</span>
-<span id="cb28-115"><a href="#cb28-115" aria-hidden="true" tabindex="-1"></a>WHERE <span class="bu">type</span><span class="op">=</span><span class="st">'table'</span></span>
-<span id="cb28-116"><a href="#cb28-116" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-117"><a href="#cb28-117" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-118"><a href="#cb28-118" aria-hidden="true" tabindex="-1"></a>The summary above displays information about the database; it contains four tables named <span class="in">`sqlite_sequence`</span>, <span class="in">`Dragon`</span>, <span class="in">`Dish`</span>, and <span class="in">`Scene`</span>. The rightmost column above lists the command that was used to construct each table. </span>
-<span id="cb28-119"><a href="#cb28-119" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-120"><a href="#cb28-120" aria-hidden="true" tabindex="-1"></a>Let's look more closely at the command used to create the <span class="in">`Dragon`</span> table (the second entry above). </span>
-<span id="cb28-121"><a href="#cb28-121" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-122"><a href="#cb28-122" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE Dragon (name TEXT PRIMARY KEY,</span></span>
-<span id="cb28-123"><a href="#cb28-123" aria-hidden="true" tabindex="-1"></a><span class="in">                         year INTEGER CHECK (year &gt;= 2000),</span></span>
-<span id="cb28-124"><a href="#cb28-124" aria-hidden="true" tabindex="-1"></a><span class="in">                         cute INTEGER)</span></span>
-<span id="cb28-125"><a href="#cb28-125" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-126"><a href="#cb28-126" aria-hidden="true" tabindex="-1"></a>The statement <span class="in">`CREATE TABLE`</span> is used to specify the **schema** of the table – a description of what logic is used to organize the table. Schema follows a set format:</span>
-<span id="cb28-127"><a href="#cb28-127" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-128"><a href="#cb28-128" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`ColName`</span>: the name of a column</span>
-<span id="cb28-129"><a href="#cb28-129" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`DataType`</span>: the type of data to be stored in a column. Some of the most common SQL data types are:</span>
-<span id="cb28-130"><a href="#cb28-130" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-131"><a href="#cb28-131" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`INT`</span> (integers)</span>
-<span id="cb28-132"><a href="#cb28-132" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`FLOAT`</span> (floating point numbers)</span>
-<span id="cb28-133"><a href="#cb28-133" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`TEXT`</span> (strings)</span>
-<span id="cb28-134"><a href="#cb28-134" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`BLOB`</span> (arbitrary data, such as audio/video files)</span>
-<span id="cb28-135"><a href="#cb28-135" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`DATETIME`</span> (a date and time)</span>
-<span id="cb28-136"><a href="#cb28-136" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`Constraint`</span>: some restriction on the data to be stored in the column. Common constraints are:</span>
-<span id="cb28-137"><a href="#cb28-137" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`CHECK`</span> (data must obey a certain condition)</span>
-<span id="cb28-138"><a href="#cb28-138" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`PRIMARY KEY`</span> (designate a column as the table's primary key)</span>
-<span id="cb28-139"><a href="#cb28-139" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`NOT NULL`</span> (data cannot be null)</span>
-<span id="cb28-140"><a href="#cb28-140" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`DEFAULT`</span> (a default fill value if no specific entry is given)</span>
-<span id="cb28-141"><a href="#cb28-141" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-142"><a href="#cb28-142" aria-hidden="true" tabindex="-1"></a>Note that different implementations of SQL (e.g., <span class="co">[</span><span class="ot">DuckDB</span><span class="co">](https://duckdb.org/docs/sql/data_types/overview.html)</span>, <span class="co">[</span><span class="ot">SQLite</span><span class="co">](https://www.sqlite.org/datatype3.html)</span>, <span class="co">[</span><span class="ot">MySQL</span><span class="co">](https://dev.mysql.com/doc/refman/8.0/en/data-types.html)</span>) will support different types. In Data 100, we'll primarily use DuckDB.</span>
-<span id="cb28-143"><a href="#cb28-143" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-144"><a href="#cb28-144" aria-hidden="true" tabindex="-1"></a>Database tables (also referred to as **relations**) are structured much like `DataFrame`s in `pandas`. Each row, sometimes called a **tuple**, represents a single record in the dataset. Each column, sometimes called an **attribute** or **field**, describes some feature of the record. </span>
-<span id="cb28-145"><a href="#cb28-145" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-146"><a href="#cb28-146" aria-hidden="true" tabindex="-1"></a><span class="fu">### Primary Keys</span></span>
-<span id="cb28-147"><a href="#cb28-147" aria-hidden="true" tabindex="-1"></a>The **primary key** is a set of column(s) that uniquely identify each record in the table. In the <span class="in">`Dragon`</span> table, the <span class="in">`"name"`</span> column is its primary key that *uniquely* identifies each entry in the table. Because <span class="in">`"name"`</span> is the primary key of the table, no two entries in the table can have the same name – a given value of <span class="in">`"name"`</span> is unique to each dragon. Primary keys are used to ensure data integrity and to optimize data access.</span>
-<span id="cb28-148"><a href="#cb28-148" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-149"><a href="#cb28-149" aria-hidden="true" tabindex="-1"></a><span class="fu">### Foreign Keys</span></span>
-<span id="cb28-150"><a href="#cb28-150" aria-hidden="true" tabindex="-1"></a>A foreign key is a column or set of columns that references a *primary key in another table*. A foreign key constraint ensures that a primary key exists in the referenced table. For example, let's say we have 2 tables, <span class="in">`student`</span> and <span class="in">`assignment`</span>, with the following schemas: </span>
-<span id="cb28-151"><a href="#cb28-151" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-152"><a href="#cb28-152" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE student (</span></span>
-<span id="cb28-153"><a href="#cb28-153" aria-hidden="true" tabindex="-1"></a><span class="in">        student_id INTEGER PRIMARY KEY,</span></span>
-<span id="cb28-154"><a href="#cb28-154" aria-hidden="true" tabindex="-1"></a><span class="in">        name VARCHAR,</span></span>
-<span id="cb28-155"><a href="#cb28-155" aria-hidden="true" tabindex="-1"></a><span class="in">        email VARCHAR</span></span>
-<span id="cb28-156"><a href="#cb28-156" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
-<span id="cb28-157"><a href="#cb28-157" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-158"><a href="#cb28-158" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE assignment (</span></span>
-<span id="cb28-159"><a href="#cb28-159" aria-hidden="true" tabindex="-1"></a><span class="in">        assignment_id INTEGER PRIMARY KEY,</span></span>
-<span id="cb28-160"><a href="#cb28-160" aria-hidden="true" tabindex="-1"></a><span class="in">        description VARCHAR</span></span>
-<span id="cb28-161"><a href="#cb28-161" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
-<span id="cb28-162"><a href="#cb28-162" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-163"><a href="#cb28-163" aria-hidden="true" tabindex="-1"></a>Note that each table has a primary key that uniquely identifies each student and assignment.</span>
-<span id="cb28-164"><a href="#cb28-164" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-165"><a href="#cb28-165" aria-hidden="true" tabindex="-1"></a>Say we want to create the table <span class="in">`grade`</span> to store the score each student got on each assignment. Naturally, this will depend on the information in <span class="in">`student`</span> and <span class="in">`assignment`</span>; we should not be saving the grade for a nonexisistent student nor a nonexisistent assignment. Hence, we can create the columns <span class="in">`student_id`</span> and <span class="in">`assignment_id`</span> that reference foreign tables <span class="in">`student`</span> and <span class="in">`assignment`</span>, respectively. This way, we ensure that the data in <span class="in">`grade`</span> is always up-to-date with the other tables.  </span>
-<span id="cb28-166"><a href="#cb28-166" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-167"><a href="#cb28-167" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE grade (</span></span>
-<span id="cb28-168"><a href="#cb28-168" aria-hidden="true" tabindex="-1"></a><span class="in">        student_id INTEGER,</span></span>
-<span id="cb28-169"><a href="#cb28-169" aria-hidden="true" tabindex="-1"></a><span class="in">        assignment_id INTEGER,</span></span>
-<span id="cb28-170"><a href="#cb28-170" aria-hidden="true" tabindex="-1"></a><span class="in">        score REAL,</span></span>
-<span id="cb28-171"><a href="#cb28-171" aria-hidden="true" tabindex="-1"></a><span class="in">        FOREIGN KEY (student_id) REFERENCES student(student_id),</span></span>
-<span id="cb28-172"><a href="#cb28-172" aria-hidden="true" tabindex="-1"></a><span class="in">        FOREIGN KEY (assignment_id) REFERENCES assignment(assignment_id)</span></span>
-<span id="cb28-173"><a href="#cb28-173" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
-<span id="cb28-174"><a href="#cb28-174" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-175"><a href="#cb28-175" aria-hidden="true" tabindex="-1"></a><span class="fu">## Basic Queries</span></span>
-<span id="cb28-176"><a href="#cb28-176" aria-hidden="true" tabindex="-1"></a>To extract and manipulate data stored in a SQL table, we will need to familiarize ourselves with the syntax to write pieces of SQL code, which we call **queries**. </span>
-<span id="cb28-177"><a href="#cb28-177" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-178"><a href="#cb28-178" aria-hidden="true" tabindex="-1"></a><span class="fu">### `SELECT`ing From Tables</span></span>
-<span id="cb28-179"><a href="#cb28-179" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-180"><a href="#cb28-180" aria-hidden="true" tabindex="-1"></a>The basic unit of a SQL query is the <span class="in">`SELECT`</span> statement. <span class="in">`SELECT`</span> specifies what columns we would like to extract from a given table. We use <span class="in">`FROM`</span> to tell SQL the table from which we want to <span class="in">`SELECT`</span> our data. </span>
-<span id="cb28-181"><a href="#cb28-181" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-184"><a href="#cb28-184" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-185"><a href="#cb28-185" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-186"><a href="#cb28-186" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-187"><a href="#cb28-187" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-188"><a href="#cb28-188" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
-<span id="cb28-189"><a href="#cb28-189" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-190"><a href="#cb28-190" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-191"><a href="#cb28-191" aria-hidden="true" tabindex="-1"></a>In SQL, <span class="in">`*`</span> means "everything." The query above grabs *all* the columns in <span class="in">`Dragon`</span> and displays them in the outputted table. We can also specify a specific subset of columns to be <span class="in">`SELECT`</span>ed. Notice that the outputted columns appear in the order they were <span class="in">`SELECT`</span>ed.</span>
-<span id="cb28-192"><a href="#cb28-192" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-195"><a href="#cb28-195" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-196"><a href="#cb28-196" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-197"><a href="#cb28-197" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-198"><a href="#cb28-198" aria-hidden="true" tabindex="-1"></a>SELECT cute, year</span>
-<span id="cb28-199"><a href="#cb28-199" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
-<span id="cb28-200"><a href="#cb28-200" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-201"><a href="#cb28-201" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-202"><a href="#cb28-202" aria-hidden="true" tabindex="-1"></a>**Every** SQL query must include both a <span class="in">`SELECT`</span> and <span class="in">`FROM`</span> statement. Intuitively, this makes sense —— we know that we'll want to extract some piece of information from the table; to do so, we also need to indicate what table we want to consider. </span>
-<span id="cb28-203"><a href="#cb28-203" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-204"><a href="#cb28-204" aria-hidden="true" tabindex="-1"></a>It is important to note that SQL enforces a strict "order of operations" —— SQL clauses must *always* follow the same sequence. For example, the <span class="in">`SELECT`</span> statement must always precede <span class="in">`FROM`</span>. This means that any SQL query will follow the same structure. </span>
-<span id="cb28-205"><a href="#cb28-205" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-206"><a href="#cb28-206" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
-<span id="cb28-207"><a href="#cb28-207" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
-<span id="cb28-208"><a href="#cb28-208" aria-hidden="true" tabindex="-1"></a><span class="in">    [additional clauses]</span></span>
-<span id="cb28-209"><a href="#cb28-209" aria-hidden="true" tabindex="-1"></a><span class="in">    </span></span>
-<span id="cb28-210"><a href="#cb28-210" aria-hidden="true" tabindex="-1"></a>The additional clauses we use depend on the specific task we're trying to achieve. We may refine our query to filter on a certain condition, aggregate a particular column, or join several tables together. We will spend the rest of this lecture outlining some useful clauses to build up our understanding of the order of operations.</span>
-<span id="cb28-211"><a href="#cb28-211" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-212"><a href="#cb28-212" aria-hidden="true" tabindex="-1"></a><span class="fu">#### SQL Style Conventions</span></span>
-<span id="cb28-213"><a href="#cb28-213" aria-hidden="true" tabindex="-1"></a>And just like that, we've already written two SQL queries. There are a few things to note in the queries above. Firstly, notice that every "verb" is written in uppercase. It is convention to write SQL operations in capital letters, but your code will run just fine even if you choose to keep things in lowercase. Second, the query above separates each statement with a new line. SQL queries are not impacted by whitespace within the query; this means that SQL code is typically written with a new line after each statement to make things more readable. The semicolon (<span class="in">`;`</span>) indicates the end of a query. There are some "flavors" of SQL in which a query will not run if no semicolon is present; however, in Data 100, the SQL version we will use works with or without an ending semicolon. Queries in these notes will end with semicolons to build up good habits.</span>
-<span id="cb28-214"><a href="#cb28-214" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-215"><a href="#cb28-215" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Aliasing with `AS`</span></span>
-<span id="cb28-216"><a href="#cb28-216" aria-hidden="true" tabindex="-1"></a>The <span class="in">`AS`</span> keyword allows us to give a column a new name (called an **alias**) after it has been <span class="in">`SELECT`</span>ed. The general syntax is:</span>
-<span id="cb28-217"><a href="#cb28-217" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-218"><a href="#cb28-218" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT column_in_input_table AS new_name_in_output_table</span></span>
-<span id="cb28-219"><a href="#cb28-219" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-222"><a href="#cb28-222" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-223"><a href="#cb28-223" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-224"><a href="#cb28-224" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-225"><a href="#cb28-225" aria-hidden="true" tabindex="-1"></a>SELECT cute AS cuteness, year AS birth</span>
-<span id="cb28-226"><a href="#cb28-226" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
-<span id="cb28-227"><a href="#cb28-227" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-228"><a href="#cb28-228" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-229"><a href="#cb28-229" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Uniqueness with `DISTINCT`</span></span>
-<span id="cb28-230"><a href="#cb28-230" aria-hidden="true" tabindex="-1"></a>To <span class="in">`SELECT`</span> only the *unique* values in a column, we use the <span class="in">`DISTINCT`</span> keyword. This will cause any any duplicate entries in a column to be removed. If we want to find only the unique years in <span class="in">`Dragon`</span>, without any repeats, we would write:</span>
-<span id="cb28-231"><a href="#cb28-231" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-234"><a href="#cb28-234" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-235"><a href="#cb28-235" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-236"><a href="#cb28-236" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-237"><a href="#cb28-237" aria-hidden="true" tabindex="-1"></a>SELECT DISTINCT year</span>
-<span id="cb28-238"><a href="#cb28-238" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
-<span id="cb28-239"><a href="#cb28-239" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-240"><a href="#cb28-240" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-241"><a href="#cb28-241" aria-hidden="true" tabindex="-1"></a><span class="fu">### Applying `WHERE` Conditions</span></span>
-<span id="cb28-242"><a href="#cb28-242" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-243"><a href="#cb28-243" aria-hidden="true" tabindex="-1"></a>The <span class="in">`WHERE`</span> keyword is used to select only some rows of a table, filtered on a given Boolean condition. </span>
-<span id="cb28-244"><a href="#cb28-244" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-247"><a href="#cb28-247" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-248"><a href="#cb28-248" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-249"><a href="#cb28-249" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-250"><a href="#cb28-250" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb28-251"><a href="#cb28-251" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-252"><a href="#cb28-252" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span><span class="op">;</span></span>
-<span id="cb28-253"><a href="#cb28-253" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-254"><a href="#cb28-254" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-255"><a href="#cb28-255" aria-hidden="true" tabindex="-1"></a>We can add complexity to the <span class="in">`WHERE`</span> condition using the keywords <span class="in">`AND`</span>, <span class="in">`OR`</span>, and <span class="in">`NOT`</span>, much like we would in Python.</span>
-<span id="cb28-256"><a href="#cb28-256" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-259"><a href="#cb28-259" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-260"><a href="#cb28-260" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-261"><a href="#cb28-261" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-262"><a href="#cb28-262" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb28-263"><a href="#cb28-263" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-264"><a href="#cb28-264" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span> OR year <span class="op">&gt;</span> <span class="dv">2013</span><span class="op">;</span></span>
-<span id="cb28-265"><a href="#cb28-265" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-266"><a href="#cb28-266" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-267"><a href="#cb28-267" aria-hidden="true" tabindex="-1"></a>To spare ourselves needing to write complicated logical expressions by combining several conditions, we can also filter for entries that are <span class="in">`IN`</span> a specified list of values. This is similar to the use of <span class="in">`in`</span> or <span class="in">`.isin`</span> in Python.</span>
-<span id="cb28-268"><a href="#cb28-268" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-271"><a href="#cb28-271" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-272"><a href="#cb28-272" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-273"><a href="#cb28-273" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-274"><a href="#cb28-274" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
-<span id="cb28-275"><a href="#cb28-275" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-276"><a href="#cb28-276" aria-hidden="true" tabindex="-1"></a>WHERE name IN (<span class="st">'hiccup'</span>, <span class="st">'puff'</span>)<span class="op">;</span></span>
-<span id="cb28-277"><a href="#cb28-277" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-278"><a href="#cb28-278" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-279"><a href="#cb28-279" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Strings in SQL</span></span>
-<span id="cb28-280"><a href="#cb28-280" aria-hidden="true" tabindex="-1"></a>In <span class="in">`Python`</span>, there is no distinction between double <span class="in">`""`</span> and single quotes <span class="in">`''`</span>. SQL, on the other hand, distinguishes double quotes <span class="in">`""`</span> as *column names* and single quotes `''` as *strings*. For example, we can make the call</span>
-<span id="cb28-281"><a href="#cb28-281" aria-hidden="true" tabindex="-1"></a>  </span>
-<span id="cb28-282"><a href="#cb28-282" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT "birth weight"</span></span>
-<span id="cb28-283"><a href="#cb28-283" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM patient</span></span>
-<span id="cb28-284"><a href="#cb28-284" aria-hidden="true" tabindex="-1"></a><span class="in">    WHERE "first name" = 'Joey'</span></span>
-<span id="cb28-285"><a href="#cb28-285" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-286"><a href="#cb28-286" aria-hidden="true" tabindex="-1"></a>to select the column <span class="in">`"birth weight"`</span> from the <span class="in">`patient`</span> table and only select rows where the column <span class="in">`"first name"`</span> is equal to <span class="in">`'Joey'`</span>.</span>
-<span id="cb28-287"><a href="#cb28-287" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-288"><a href="#cb28-288" aria-hidden="true" tabindex="-1"></a><span class="fu">#### `WHERE` WITH `NULL` Values</span></span>
-<span id="cb28-289"><a href="#cb28-289" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-290"><a href="#cb28-290" aria-hidden="true" tabindex="-1"></a>You may have noticed earlier that our table actually has a missing value. In SQL, missing data is given the special value <span class="in">`NULL`</span>. <span class="in">`NULL`</span> behaves in a fundamentally different way to other data types. We can't use the typical operators (=, &gt;, and &lt;) on <span class="in">`NULL`</span> values (in fact, <span class="in">`NULL == NULL`</span> returns <span class="in">`False`</span>!). Instead, we check to see if a value <span class="in">`IS`</span> or <span class="in">`IS NOT`</span> <span class="in">`NULL`</span>.</span>
-<span id="cb28-291"><a href="#cb28-291" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-294"><a href="#cb28-294" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-295"><a href="#cb28-295" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-296"><a href="#cb28-296" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-297"><a href="#cb28-297" aria-hidden="true" tabindex="-1"></a>SELECT name, cute</span>
-<span id="cb28-298"><a href="#cb28-298" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-299"><a href="#cb28-299" aria-hidden="true" tabindex="-1"></a>WHERE cute IS NOT NULL<span class="op">;</span></span>
-<span id="cb28-300"><a href="#cb28-300" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-301"><a href="#cb28-301" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-302"><a href="#cb28-302" aria-hidden="true" tabindex="-1"></a><span class="fu">### Sorting and Restricting Output</span></span>
-<span id="cb28-303"><a href="#cb28-303" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-304"><a href="#cb28-304" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Sorting with  `ORDER BY`</span></span>
-<span id="cb28-305"><a href="#cb28-305" aria-hidden="true" tabindex="-1"></a>What if we want the output table to appear in a certain order? The <span class="in">`ORDER BY`</span> keyword behaves similarly to <span class="in">`.sort_values()`</span> in <span class="in">`pandas`</span>. </span>
-<span id="cb28-306"><a href="#cb28-306" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-309"><a href="#cb28-309" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-310"><a href="#cb28-310" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-311"><a href="#cb28-311" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-312"><a href="#cb28-312" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-313"><a href="#cb28-313" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-314"><a href="#cb28-314" aria-hidden="true" tabindex="-1"></a>ORDER BY cute<span class="op">;</span></span>
-<span id="cb28-315"><a href="#cb28-315" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-316"><a href="#cb28-316" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-317"><a href="#cb28-317" aria-hidden="true" tabindex="-1"></a>By default, <span class="in">`ORDER BY`</span> will display results in ascending order (<span class="in">`ASC`</span>) with the lowest values at the top of the table. To sort in descending order, we use the <span class="in">`DESC`</span> keyword after specifying the column to be used for ordering.</span>
-<span id="cb28-318"><a href="#cb28-318" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-321"><a href="#cb28-321" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-322"><a href="#cb28-322" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-323"><a href="#cb28-323" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-324"><a href="#cb28-324" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-325"><a href="#cb28-325" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-326"><a href="#cb28-326" aria-hidden="true" tabindex="-1"></a>ORDER BY cute DESC<span class="op">;</span></span>
-<span id="cb28-327"><a href="#cb28-327" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-328"><a href="#cb28-328" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-329"><a href="#cb28-329" aria-hidden="true" tabindex="-1"></a>We can also tell SQL to <span class="in">`ORDER BY`</span> two columns at once. This will sort the table by the first listed column, then use the values in the second listed column to break any ties.</span>
-<span id="cb28-330"><a href="#cb28-330" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-333"><a href="#cb28-333" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-334"><a href="#cb28-334" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-335"><a href="#cb28-335" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-336"><a href="#cb28-336" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-337"><a href="#cb28-337" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-338"><a href="#cb28-338" aria-hidden="true" tabindex="-1"></a>ORDER BY year, cute DESC<span class="op">;</span></span>
-<span id="cb28-339"><a href="#cb28-339" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-340"><a href="#cb28-340" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-341"><a href="#cb28-341" aria-hidden="true" tabindex="-1"></a>Note that in this example, <span class="in">`year`</span> is sorted in ascending order and <span class="in">`cute`</span> in descending order. If you want <span class="in">`year`</span> to be ordered in descending order as well, you need to specify <span class="in">`year DESC, cute DESC;`</span>.</span>
-<span id="cb28-342"><a href="#cb28-342" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-343"><a href="#cb28-343" aria-hidden="true" tabindex="-1"></a><span class="fu">#### `LIMIT` vs. `OFFSET`</span></span>
-<span id="cb28-344"><a href="#cb28-344" aria-hidden="true" tabindex="-1"></a>In many instances, we are only concerned with a certain number of rows in the output table (for example, wanting to find the first two dragons in the table). The <span class="in">`LIMIT`</span> keyword restricts the output to a specified number of rows. It serves a function similar to that of <span class="in">`.head()`</span> in <span class="in">`pandas`</span>.</span>
-<span id="cb28-345"><a href="#cb28-345" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-348"><a href="#cb28-348" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-349"><a href="#cb28-349" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-350"><a href="#cb28-350" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-351"><a href="#cb28-351" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-352"><a href="#cb28-352" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-353"><a href="#cb28-353" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span><span class="op">;</span></span>
-<span id="cb28-354"><a href="#cb28-354" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-355"><a href="#cb28-355" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-356"><a href="#cb28-356" aria-hidden="true" tabindex="-1"></a>The <span class="in">`OFFSET`</span> keyword indicates the index at which <span class="in">`LIMIT`</span> should start. In other words, we can use <span class="in">`OFFSET`</span> to shift where the <span class="in">`LIMIT`</span>ing begins by a specified number of rows. For example, we might care about the dragons that are at positions 2 and 3 in the table. </span>
-<span id="cb28-357"><a href="#cb28-357" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-360"><a href="#cb28-360" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb28-361"><a href="#cb28-361" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb28-362"><a href="#cb28-362" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb28-363"><a href="#cb28-363" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb28-364"><a href="#cb28-364" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb28-365"><a href="#cb28-365" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span></span>
-<span id="cb28-366"><a href="#cb28-366" aria-hidden="true" tabindex="-1"></a>OFFSET <span class="dv">1</span><span class="op">;</span></span>
-<span id="cb28-367"><a href="#cb28-367" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb28-368"><a href="#cb28-368" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-369"><a href="#cb28-369" aria-hidden="true" tabindex="-1"></a>With these keywords in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
-<span id="cb28-370"><a href="#cb28-370" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-371"><a href="#cb28-371" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
-<span id="cb28-372"><a href="#cb28-372" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
-<span id="cb28-373"><a href="#cb28-373" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
-<span id="cb28-374"><a href="#cb28-374" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
-<span id="cb28-375"><a href="#cb28-375" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
-<span id="cb28-376"><a href="#cb28-376" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
-<span id="cb28-377"><a href="#cb28-377" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-378"><a href="#cb28-378" aria-hidden="true" tabindex="-1"></a><span class="fu">## Summary</span></span>
-<span id="cb28-379"><a href="#cb28-379" aria-hidden="true" tabindex="-1"></a>Let's summarize what we've learned so far. We know that <span class="in">`SELECT`</span> and <span class="in">`FROM`</span> are the fundamental building blocks of any SQL query. We can augment these two keywords with additional clauses to refine the data in our output table. </span>
-<span id="cb28-380"><a href="#cb28-380" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-381"><a href="#cb28-381" aria-hidden="true" tabindex="-1"></a>Any clauses that we include must follow a strict ordering within the query:</span>
-<span id="cb28-382"><a href="#cb28-382" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-383"><a href="#cb28-383" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
-<span id="cb28-384"><a href="#cb28-384" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
-<span id="cb28-385"><a href="#cb28-385" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
-<span id="cb28-386"><a href="#cb28-386" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
-<span id="cb28-387"><a href="#cb28-387" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
-<span id="cb28-388"><a href="#cb28-388" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;]</span></span>
-<span id="cb28-389"><a href="#cb28-389" aria-hidden="true" tabindex="-1"></a><span class="in">    </span></span>
-<span id="cb28-390"><a href="#cb28-390" aria-hidden="true" tabindex="-1"></a>Here, any clause contained in square brackets <span class="in">`[ ]`</span> is optional —— we only need to use the keyword if it is relevant to the table operation we want to perform. Also note that by convention, we use all caps for keywords in SQL statements and use newlines to make code more readable.</span>
+<div class="sourceCode" id="cb27" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> SQL I</span></span>
+<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span>
+<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a><span class="co">  echo: true</span></span>
+<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
+<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
+<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: false</span></span>
+<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
+<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
+<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a><span class="co">    toc-title: SQL I</span></span>
+<span id="cb27-11"><a href="#cb27-11" aria-hidden="true" tabindex="-1"></a><span class="co">    page-layout: full</span></span>
+<span id="cb27-12"><a href="#cb27-12" aria-hidden="true" tabindex="-1"></a><span class="co">    theme:</span></span>
+<span id="cb27-13"><a href="#cb27-13" aria-hidden="true" tabindex="-1"></a><span class="co">      - cosmo</span></span>
+<span id="cb27-14"><a href="#cb27-14" aria-hidden="true" tabindex="-1"></a><span class="co">      - cerulean</span></span>
+<span id="cb27-15"><a href="#cb27-15" aria-hidden="true" tabindex="-1"></a><span class="co">    callout-icon: false</span></span>
+<span id="cb27-16"><a href="#cb27-16" aria-hidden="true" tabindex="-1"></a><span class="an">jupyter:</span></span>
+<span id="cb27-17"><a href="#cb27-17" aria-hidden="true" tabindex="-1"></a><span class="co">  jupytext:</span></span>
+<span id="cb27-18"><a href="#cb27-18" aria-hidden="true" tabindex="-1"></a><span class="co">    text_representation:</span></span>
+<span id="cb27-19"><a href="#cb27-19" aria-hidden="true" tabindex="-1"></a><span class="co">      extension: .qmd</span></span>
+<span id="cb27-20"><a href="#cb27-20" aria-hidden="true" tabindex="-1"></a><span class="co">      format_name: quarto</span></span>
+<span id="cb27-21"><a href="#cb27-21" aria-hidden="true" tabindex="-1"></a><span class="co">      format_version: '1.0'</span></span>
+<span id="cb27-22"><a href="#cb27-22" aria-hidden="true" tabindex="-1"></a><span class="co">      jupytext_version: 1.16.1</span></span>
+<span id="cb27-23"><a href="#cb27-23" aria-hidden="true" tabindex="-1"></a><span class="co">  kernelspec:</span></span>
+<span id="cb27-24"><a href="#cb27-24" aria-hidden="true" tabindex="-1"></a><span class="co">    display_name: Python 3 (ipykernel)</span></span>
+<span id="cb27-25"><a href="#cb27-25" aria-hidden="true" tabindex="-1"></a><span class="co">    language: python</span></span>
+<span id="cb27-26"><a href="#cb27-26" aria-hidden="true" tabindex="-1"></a><span class="co">    name: python3</span></span>
+<span id="cb27-27"><a href="#cb27-27" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb27-28"><a href="#cb27-28" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-29"><a href="#cb27-29" aria-hidden="true" tabindex="-1"></a>::: {.callout-note collapse="false"}</span>
+<span id="cb27-30"><a href="#cb27-30" aria-hidden="true" tabindex="-1"></a><span class="fu">## Learning Outcomes</span></span>
+<span id="cb27-31"><a href="#cb27-31" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Recognizing situations where we need “bigger” tools for manipulating data</span>
+<span id="cb27-32"><a href="#cb27-32" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Write basic SQL queries using <span class="in">`SELECT`</span>, <span class="in">`FROM`</span>, <span class="in">`WHERE`</span>, <span class="in">`ORDER BY`</span>, <span class="in">`LIMIT`</span>, and <span class="in">`OFFSET`</span></span>
+<span id="cb27-33"><a href="#cb27-33" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform aggregations using <span class="in">`GROUP BY`</span></span>
+<span id="cb27-34"><a href="#cb27-34" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb27-35"><a href="#cb27-35" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-36"><a href="#cb27-36" aria-hidden="true" tabindex="-1"></a>So far in the course, we have made our way through the entire data science lifecycle: we learned how to load and explore a dataset, formulate questions, and use the tools of prediction and inference to come up with answers. For the remaining weeks of the semester, we are going to make a second pass through the lifecycle, this time with a different set of tools, ideas, and abstractions. </span>
+<span id="cb27-37"><a href="#cb27-37" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-38"><a href="#cb27-38" aria-hidden="true" tabindex="-1"></a><span class="fu">## Databases</span></span>
+<span id="cb27-39"><a href="#cb27-39" aria-hidden="true" tabindex="-1"></a>With this goal in mind, let's go back to the very beginning of the lifecycle. We first started our work in data analysis by looking at the <span class="in">`pandas`</span> library, which offered us powerful tools to manipulate tabular data stored in (primarily) CSV files. CSVs work well when analyzing relatively small datasets (less than 10GB) that don't need to be shared across many users. In research and industry, however, data scientists often need to access enormous bodies of data that cannot be easily stored in a CSV format. Collaborating with others when working with CSVs can also be tricky —— a real-world data scientist may run into problems when multiple users try to make modifications or more dire security issues arise regarding who should and should not have access to the data. </span>
+<span id="cb27-40"><a href="#cb27-40" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-41"><a href="#cb27-41" aria-hidden="true" tabindex="-1"></a>A **database** is a large, organized collection of data. Databases are administered by **Database Management Systems (DBMS)**, which are software systems that store, manage, and facilitate access to one or more databases. Databases help mitigate many of the issues that come with using CSVs for data storage: they provide reliable storage that can survive system crashes or disk failures, are optimized to compute on data that does not fit into memory, and contain special data structures to improve performance. Using databases rather than CSVs offers further benefits from the standpoint of data management. A DBMS can apply settings that configure how data is organized, block certain data anomalies (for example, enforcing non-negative weights or ages), and determine who is allowed access to the data. It can also ensure safe concurrent operations where multiple users reading and writing to the database will not lead to fatal errors. Below, you can see the functionality of the different types of data storage and management architectures. In data science, common large-scale DBMS systems used are Google BigQuery, Amazon Redshift, Snowflake, Databricks, Microsoft SQL Server, and more. To learn more about these, consider taking <span class="co">[</span><span class="ot">Data 101</span><span class="co">](https://www.data101.org/sp24/)</span>!</span>
+<span id="cb27-42"><a href="#cb27-42" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-43"><a href="#cb27-43" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;p</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align:center"</span><span class="kw">&gt;</span></span>
+<span id="cb27-44"><a href="#cb27-44" aria-hidden="true" tabindex="-1"></a>    <span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">'images/data_storage.png'</span> <span class="er">width</span><span class="ot">=</span><span class="st">"700px"</span> <span class="kw">/&gt;</span></span>
+<span id="cb27-45"><a href="#cb27-45" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/p&gt;</span></span>
+<span id="cb27-46"><a href="#cb27-46" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-47"><a href="#cb27-47" aria-hidden="true" tabindex="-1"></a>As you may have guessed, we can't use our usual <span class="in">`pandas`</span> methods to work with data in a database. Instead, we'll turn to Structured Query Language.</span>
+<span id="cb27-48"><a href="#cb27-48" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-49"><a href="#cb27-49" aria-hidden="true" tabindex="-1"></a><span class="fu">## Intro to SQL</span></span>
+<span id="cb27-50"><a href="#cb27-50" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-51"><a href="#cb27-51" aria-hidden="true" tabindex="-1"></a>**Structured Query Language**, or **SQL** (commonly pronounced "sequel," though this is the subject of [fierce debate](https://patorjk.com/blog/2012/01/26/pronouncing-sql-s-q-l-or-sequel/)), is a special programming language designed to communicate with databases, and it is the dominant language/technology for working with data. You may have encountered it in classes like CS 61A or Data C88C before, and you likely will encounter it in the future. It is a language of tables: all inputs and outputs are tables. Unlike Python, it is a **declarative programming language** – this means that rather than writing the exact logic needed to complete a task, a piece of SQL code "declares" what the desired final output should be and leaves the program to determine what logic should be implemented. This logic differs depending on the SQL code itself or on the system it's running on (ie. <span class="co">[</span><span class="ot">MongoDB</span><span class="co">](https://www.mongodb.com/)</span>, <span class="co">[</span><span class="ot">SQLite</span><span class="co">](https://www.sqlite.org/)</span>, <span class="co">[</span><span class="ot">DuckDB</span><span class="co">](https://duckdb.org/)</span>, etc.). Most systems don’t follow the standards, and every system you work with will be a little different. </span>
+<span id="cb27-52"><a href="#cb27-52" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-53"><a href="#cb27-53" aria-hidden="true" tabindex="-1"></a>For the purposes of Data 100, we use SQLite or DuckDB. SQLite is an easy-to-use library that allows users to directly manipulate a database file or an in-memory database with a simplified version of SQL. It's commonly used to store data for small apps on mobile devices and is optimized for simplicity and speed of simple data tasks. DuckDB is an easy-to-use library that lets you directly manipulate a database file, collection of table formatted files (e.g., CSV), or in-memory <span class="in">`pandas`</span> <span class="in">`DataFrame`</span>s using a more complete version of SQL. It's optimized for simplicity and speed of advanced data analysis tasks and is becoming increasingly popular for data analysis tasks on large datasets.</span>
+<span id="cb27-54"><a href="#cb27-54" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-55"><a href="#cb27-55" aria-hidden="true" tabindex="-1"></a>It is important to reiterate that SQL is an entirely different language from Python. However, Python *does* have special engines that allow us to run SQL code in a Jupyter notebook. While this is typically not how SQL is used outside of an educational setting, we will use this workflow to illustrate how SQL queries are constructed using the tools we've already worked with this semester. You will learn more about how to run SQL queries in Jupyter in Lab 10 and Homework 10.</span>
+<span id="cb27-56"><a href="#cb27-56" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-57"><a href="#cb27-57" aria-hidden="true" tabindex="-1"></a>The syntax below will seem unfamiliar to you; for now, just focus on understanding the output displayed. We will clarify the SQL code in a bit.</span>
+<span id="cb27-58"><a href="#cb27-58" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-59"><a href="#cb27-59" aria-hidden="true" tabindex="-1"></a>To start, we'll look at a database called <span class="in">`example_duck.db`</span> and connect to it using DuckDB.</span>
+<span id="cb27-60"><a href="#cb27-60" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-63"><a href="#cb27-63" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-64"><a href="#cb27-64" aria-hidden="true" tabindex="-1"></a><span class="co">#| code-fold: true</span></span>
+<span id="cb27-65"><a href="#cb27-65" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-66"><a href="#cb27-66" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the SQL Alchemy Python library and DuckDB</span></span>
+<span id="cb27-67"><a href="#cb27-67" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sqlalchemy</span>
+<span id="cb27-68"><a href="#cb27-68" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> duckdb</span>
+<span id="cb27-69"><a href="#cb27-69" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-70"><a href="#cb27-70" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-73"><a href="#cb27-73" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-74"><a href="#cb27-74" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-75"><a href="#cb27-75" aria-hidden="true" tabindex="-1"></a><span class="co"># Load %%sql cell magic</span></span>
+<span id="cb27-76"><a href="#cb27-76" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext sql</span>
+<span id="cb27-77"><a href="#cb27-77" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-78"><a href="#cb27-78" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-81"><a href="#cb27-81" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-82"><a href="#cb27-82" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-83"><a href="#cb27-83" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the database</span></span>
+<span id="cb27-84"><a href="#cb27-84" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>example_duck.db <span class="op">--</span>alias duck</span>
+<span id="cb27-85"><a href="#cb27-85" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-86"><a href="#cb27-86" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-87"><a href="#cb27-87" aria-hidden="true" tabindex="-1"></a>Now that we’re connected, let’s make some queries!</span>
+<span id="cb27-88"><a href="#cb27-88" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-91"><a href="#cb27-91" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-92"><a href="#cb27-92" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-93"><a href="#cb27-93" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-94"><a href="#cb27-94" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<span class="op">;</span></span>
+<span id="cb27-95"><a href="#cb27-95" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-96"><a href="#cb27-96" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-97"><a href="#cb27-97" aria-hidden="true" tabindex="-1"></a>Thanks to the <span class="in">`pandas`</span> magic, the resulting return data is displayed in a format almost identical to our <span class="in">`pandas`</span> tables but without an index.</span>
+<span id="cb27-98"><a href="#cb27-98" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-99"><a href="#cb27-99" aria-hidden="true" tabindex="-1"></a><span class="fu">## Tables and Schema</span></span>
+<span id="cb27-100"><a href="#cb27-100" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;p</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align:center"</span><span class="kw">&gt;</span></span>
+<span id="cb27-101"><a href="#cb27-101" aria-hidden="true" tabindex="-1"></a>    <span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">'images/sql_terminology.png'</span> <span class="er">width</span><span class="ot">=</span><span class="st">"700px"</span> <span class="kw">/&gt;</span></span>
+<span id="cb27-102"><a href="#cb27-102" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/p&gt;</span></span>
+<span id="cb27-103"><a href="#cb27-103" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-104"><a href="#cb27-104" aria-hidden="true" tabindex="-1"></a>Looking at the <span class="in">`Dragon`</span> table above, we can see that it contains contains three columns. The first of these, <span class="in">`"name"`</span>, contains text data. The <span class="in">`"year"`</span> column contains integer data, with the constraint that year values must be greater than or equal to 2000. The final column, <span class="in">`"cute"`</span>, contains integer data with no restrictions on allowable values.</span>
+<span id="cb27-105"><a href="#cb27-105" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-106"><a href="#cb27-106" aria-hidden="true" tabindex="-1"></a>Now, let's look at the **schema** of our database. A schema describes the logical structure of a table. Whenever a new table is created, the creator must declare its schema.</span>
+<span id="cb27-107"><a href="#cb27-107" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-110"><a href="#cb27-110" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-111"><a href="#cb27-111" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-112"><a href="#cb27-112" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-113"><a href="#cb27-113" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
+<span id="cb27-114"><a href="#cb27-114" aria-hidden="true" tabindex="-1"></a>FROM sqlite_master</span>
+<span id="cb27-115"><a href="#cb27-115" aria-hidden="true" tabindex="-1"></a>WHERE <span class="bu">type</span><span class="op">=</span><span class="st">'table'</span></span>
+<span id="cb27-116"><a href="#cb27-116" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-117"><a href="#cb27-117" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-118"><a href="#cb27-118" aria-hidden="true" tabindex="-1"></a>The summary above displays information about the database; it contains four tables named <span class="in">`sqlite_sequence`</span>, <span class="in">`Dragon`</span>, <span class="in">`Dish`</span>, and <span class="in">`Scene`</span>. The rightmost column above lists the command that was used to construct each table. </span>
+<span id="cb27-119"><a href="#cb27-119" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-120"><a href="#cb27-120" aria-hidden="true" tabindex="-1"></a>Let's look more closely at the command used to create the <span class="in">`Dragon`</span> table (the second entry above). </span>
+<span id="cb27-121"><a href="#cb27-121" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-122"><a href="#cb27-122" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE Dragon (name TEXT PRIMARY KEY,</span></span>
+<span id="cb27-123"><a href="#cb27-123" aria-hidden="true" tabindex="-1"></a><span class="in">                         year INTEGER CHECK (year &gt;= 2000),</span></span>
+<span id="cb27-124"><a href="#cb27-124" aria-hidden="true" tabindex="-1"></a><span class="in">                         cute INTEGER)</span></span>
+<span id="cb27-125"><a href="#cb27-125" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-126"><a href="#cb27-126" aria-hidden="true" tabindex="-1"></a>The statement <span class="in">`CREATE TABLE`</span> is used to specify the **schema** of the table – a description of what logic is used to organize the table. Schema follows a set format:</span>
+<span id="cb27-127"><a href="#cb27-127" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-128"><a href="#cb27-128" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`ColName`</span>: the name of a column</span>
+<span id="cb27-129"><a href="#cb27-129" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`DataType`</span>: the type of data to be stored in a column. Some of the most common SQL data types are:</span>
+<span id="cb27-130"><a href="#cb27-130" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-131"><a href="#cb27-131" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`INT`</span> (integers)</span>
+<span id="cb27-132"><a href="#cb27-132" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`FLOAT`</span> (floating point numbers)</span>
+<span id="cb27-133"><a href="#cb27-133" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`TEXT`</span> (strings)</span>
+<span id="cb27-134"><a href="#cb27-134" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`BLOB`</span> (arbitrary data, such as audio/video files)</span>
+<span id="cb27-135"><a href="#cb27-135" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`DATETIME`</span> (a date and time)</span>
+<span id="cb27-136"><a href="#cb27-136" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`Constraint`</span>: some restriction on the data to be stored in the column. Common constraints are:</span>
+<span id="cb27-137"><a href="#cb27-137" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`CHECK`</span> (data must obey a certain condition)</span>
+<span id="cb27-138"><a href="#cb27-138" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`PRIMARY KEY`</span> (designate a column as the table's primary key)</span>
+<span id="cb27-139"><a href="#cb27-139" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`NOT NULL`</span> (data cannot be null)</span>
+<span id="cb27-140"><a href="#cb27-140" aria-hidden="true" tabindex="-1"></a><span class="ss">  * </span><span class="in">`DEFAULT`</span> (a default fill value if no specific entry is given)</span>
+<span id="cb27-141"><a href="#cb27-141" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-142"><a href="#cb27-142" aria-hidden="true" tabindex="-1"></a>Note that different implementations of SQL (e.g., <span class="co">[</span><span class="ot">DuckDB</span><span class="co">](https://duckdb.org/docs/sql/data_types/overview.html)</span>, <span class="co">[</span><span class="ot">SQLite</span><span class="co">](https://www.sqlite.org/datatype3.html)</span>, <span class="co">[</span><span class="ot">MySQL</span><span class="co">](https://dev.mysql.com/doc/refman/8.0/en/data-types.html)</span>) will support different types. In Data 100, we'll primarily use DuckDB.</span>
+<span id="cb27-143"><a href="#cb27-143" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-144"><a href="#cb27-144" aria-hidden="true" tabindex="-1"></a>Database tables (also referred to as **relations**) are structured much like `DataFrame`s in `pandas`. Each row, sometimes called a **tuple**, represents a single record in the dataset. Each column, sometimes called an **attribute** or **field**, describes some feature of the record. </span>
+<span id="cb27-145"><a href="#cb27-145" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-146"><a href="#cb27-146" aria-hidden="true" tabindex="-1"></a><span class="fu">### Primary Keys</span></span>
+<span id="cb27-147"><a href="#cb27-147" aria-hidden="true" tabindex="-1"></a>The **primary key** is a set of column(s) that uniquely identify each record in the table. In the <span class="in">`Dragon`</span> table, the <span class="in">`"name"`</span> column is its primary key that *uniquely* identifies each entry in the table. Because <span class="in">`"name"`</span> is the primary key of the table, no two entries in the table can have the same name – a given value of <span class="in">`"name"`</span> is unique to each dragon. Primary keys are used to ensure data integrity and to optimize data access.</span>
+<span id="cb27-148"><a href="#cb27-148" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-149"><a href="#cb27-149" aria-hidden="true" tabindex="-1"></a><span class="fu">### Foreign Keys</span></span>
+<span id="cb27-150"><a href="#cb27-150" aria-hidden="true" tabindex="-1"></a>A foreign key is a column or set of columns that references a *primary key in another table*. A foreign key constraint ensures that a primary key exists in the referenced table. For example, let's say we have 2 tables, <span class="in">`student`</span> and <span class="in">`assignment`</span>, with the following schemas: </span>
+<span id="cb27-151"><a href="#cb27-151" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-152"><a href="#cb27-152" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE student (</span></span>
+<span id="cb27-153"><a href="#cb27-153" aria-hidden="true" tabindex="-1"></a><span class="in">        student_id INTEGER PRIMARY KEY,</span></span>
+<span id="cb27-154"><a href="#cb27-154" aria-hidden="true" tabindex="-1"></a><span class="in">        name VARCHAR,</span></span>
+<span id="cb27-155"><a href="#cb27-155" aria-hidden="true" tabindex="-1"></a><span class="in">        email VARCHAR</span></span>
+<span id="cb27-156"><a href="#cb27-156" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
+<span id="cb27-157"><a href="#cb27-157" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-158"><a href="#cb27-158" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE assignment (</span></span>
+<span id="cb27-159"><a href="#cb27-159" aria-hidden="true" tabindex="-1"></a><span class="in">        assignment_id INTEGER PRIMARY KEY,</span></span>
+<span id="cb27-160"><a href="#cb27-160" aria-hidden="true" tabindex="-1"></a><span class="in">        description VARCHAR</span></span>
+<span id="cb27-161"><a href="#cb27-161" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
+<span id="cb27-162"><a href="#cb27-162" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-163"><a href="#cb27-163" aria-hidden="true" tabindex="-1"></a>Note that each table has a primary key that uniquely identifies each student and assignment.</span>
+<span id="cb27-164"><a href="#cb27-164" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-165"><a href="#cb27-165" aria-hidden="true" tabindex="-1"></a>Say we want to create the table <span class="in">`grade`</span> to store the score each student got on each assignment. Naturally, this will depend on the information in <span class="in">`student`</span> and <span class="in">`assignment`</span>; we should not be saving the grade for a nonexisistent student nor a nonexisistent assignment. Hence, we can create the columns <span class="in">`student_id`</span> and <span class="in">`assignment_id`</span> that reference foreign tables <span class="in">`student`</span> and <span class="in">`assignment`</span>, respectively. This way, we ensure that the data in <span class="in">`grade`</span> is always up-to-date with the other tables.  </span>
+<span id="cb27-166"><a href="#cb27-166" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-167"><a href="#cb27-167" aria-hidden="true" tabindex="-1"></a><span class="in">    CREATE TABLE grade (</span></span>
+<span id="cb27-168"><a href="#cb27-168" aria-hidden="true" tabindex="-1"></a><span class="in">        student_id INTEGER,</span></span>
+<span id="cb27-169"><a href="#cb27-169" aria-hidden="true" tabindex="-1"></a><span class="in">        assignment_id INTEGER,</span></span>
+<span id="cb27-170"><a href="#cb27-170" aria-hidden="true" tabindex="-1"></a><span class="in">        score REAL,</span></span>
+<span id="cb27-171"><a href="#cb27-171" aria-hidden="true" tabindex="-1"></a><span class="in">        FOREIGN KEY (student_id) REFERENCES student(student_id),</span></span>
+<span id="cb27-172"><a href="#cb27-172" aria-hidden="true" tabindex="-1"></a><span class="in">        FOREIGN KEY (assignment_id) REFERENCES assignment(assignment_id)</span></span>
+<span id="cb27-173"><a href="#cb27-173" aria-hidden="true" tabindex="-1"></a><span class="in">    );</span></span>
+<span id="cb27-174"><a href="#cb27-174" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-175"><a href="#cb27-175" aria-hidden="true" tabindex="-1"></a><span class="fu">## Basic Queries</span></span>
+<span id="cb27-176"><a href="#cb27-176" aria-hidden="true" tabindex="-1"></a>To extract and manipulate data stored in a SQL table, we will need to familiarize ourselves with the syntax to write pieces of SQL code, which we call **queries**. </span>
+<span id="cb27-177"><a href="#cb27-177" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-178"><a href="#cb27-178" aria-hidden="true" tabindex="-1"></a><span class="fu">### `SELECT`ing From Tables</span></span>
+<span id="cb27-179"><a href="#cb27-179" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-180"><a href="#cb27-180" aria-hidden="true" tabindex="-1"></a>The basic unit of a SQL query is the <span class="in">`SELECT`</span> statement. <span class="in">`SELECT`</span> specifies what columns we would like to extract from a given table. We use <span class="in">`FROM`</span> to tell SQL the table from which we want to <span class="in">`SELECT`</span> our data. </span>
+<span id="cb27-181"><a href="#cb27-181" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-184"><a href="#cb27-184" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-185"><a href="#cb27-185" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-186"><a href="#cb27-186" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-187"><a href="#cb27-187" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-188"><a href="#cb27-188" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
+<span id="cb27-189"><a href="#cb27-189" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-190"><a href="#cb27-190" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-191"><a href="#cb27-191" aria-hidden="true" tabindex="-1"></a>In SQL, <span class="in">`*`</span> means "everything." The query above grabs *all* the columns in <span class="in">`Dragon`</span> and displays them in the outputted table. We can also specify a specific subset of columns to be <span class="in">`SELECT`</span>ed. Notice that the outputted columns appear in the order they were <span class="in">`SELECT`</span>ed.</span>
+<span id="cb27-192"><a href="#cb27-192" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-195"><a href="#cb27-195" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-196"><a href="#cb27-196" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-197"><a href="#cb27-197" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-198"><a href="#cb27-198" aria-hidden="true" tabindex="-1"></a>SELECT cute, year</span>
+<span id="cb27-199"><a href="#cb27-199" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
+<span id="cb27-200"><a href="#cb27-200" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-201"><a href="#cb27-201" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-202"><a href="#cb27-202" aria-hidden="true" tabindex="-1"></a>**Every** SQL query must include both a <span class="in">`SELECT`</span> and <span class="in">`FROM`</span> statement. Intuitively, this makes sense —— we know that we'll want to extract some piece of information from the table; to do so, we also need to indicate what table we want to consider. </span>
+<span id="cb27-203"><a href="#cb27-203" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-204"><a href="#cb27-204" aria-hidden="true" tabindex="-1"></a>It is important to note that SQL enforces a strict "order of operations" —— SQL clauses must *always* follow the same sequence. For example, the <span class="in">`SELECT`</span> statement must always precede <span class="in">`FROM`</span>. This means that any SQL query will follow the same structure. </span>
+<span id="cb27-205"><a href="#cb27-205" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-206"><a href="#cb27-206" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
+<span id="cb27-207"><a href="#cb27-207" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
+<span id="cb27-208"><a href="#cb27-208" aria-hidden="true" tabindex="-1"></a><span class="in">    [additional clauses]</span></span>
+<span id="cb27-209"><a href="#cb27-209" aria-hidden="true" tabindex="-1"></a><span class="in">    </span></span>
+<span id="cb27-210"><a href="#cb27-210" aria-hidden="true" tabindex="-1"></a>The additional clauses we use depend on the specific task we're trying to achieve. We may refine our query to filter on a certain condition, aggregate a particular column, or join several tables together. We will spend the rest of this lecture outlining some useful clauses to build up our understanding of the order of operations.</span>
+<span id="cb27-211"><a href="#cb27-211" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-212"><a href="#cb27-212" aria-hidden="true" tabindex="-1"></a><span class="fu">#### SQL Style Conventions</span></span>
+<span id="cb27-213"><a href="#cb27-213" aria-hidden="true" tabindex="-1"></a>And just like that, we've already written two SQL queries. There are a few things to note in the queries above. Firstly, notice that every "verb" is written in uppercase. It is convention to write SQL operations in capital letters, but your code will run just fine even if you choose to keep things in lowercase. Second, the query above separates each statement with a new line. SQL queries are not impacted by whitespace within the query; this means that SQL code is typically written with a new line after each statement to make things more readable. The semicolon (<span class="in">`;`</span>) indicates the end of a query. There are some "flavors" of SQL in which a query will not run if no semicolon is present; however, in Data 100, the SQL version we will use works with or without an ending semicolon. Queries in these notes will end with semicolons to build up good habits.</span>
+<span id="cb27-214"><a href="#cb27-214" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-215"><a href="#cb27-215" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Aliasing with `AS`</span></span>
+<span id="cb27-216"><a href="#cb27-216" aria-hidden="true" tabindex="-1"></a>The <span class="in">`AS`</span> keyword allows us to give a column a new name (called an **alias**) after it has been <span class="in">`SELECT`</span>ed. The general syntax is:</span>
+<span id="cb27-217"><a href="#cb27-217" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-218"><a href="#cb27-218" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT column_in_input_table AS new_name_in_output_table</span></span>
+<span id="cb27-219"><a href="#cb27-219" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-222"><a href="#cb27-222" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-223"><a href="#cb27-223" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-224"><a href="#cb27-224" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-225"><a href="#cb27-225" aria-hidden="true" tabindex="-1"></a>SELECT cute AS cuteness, year AS birth</span>
+<span id="cb27-226"><a href="#cb27-226" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
+<span id="cb27-227"><a href="#cb27-227" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-228"><a href="#cb27-228" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-229"><a href="#cb27-229" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Uniqueness with `DISTINCT`</span></span>
+<span id="cb27-230"><a href="#cb27-230" aria-hidden="true" tabindex="-1"></a>To <span class="in">`SELECT`</span> only the *unique* values in a column, we use the <span class="in">`DISTINCT`</span> keyword. This will cause any any duplicate entries in a column to be removed. If we want to find only the unique years in <span class="in">`Dragon`</span>, without any repeats, we would write:</span>
+<span id="cb27-231"><a href="#cb27-231" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-234"><a href="#cb27-234" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-235"><a href="#cb27-235" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-236"><a href="#cb27-236" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-237"><a href="#cb27-237" aria-hidden="true" tabindex="-1"></a>SELECT DISTINCT year</span>
+<span id="cb27-238"><a href="#cb27-238" aria-hidden="true" tabindex="-1"></a>FROM Dragon<span class="op">;</span></span>
+<span id="cb27-239"><a href="#cb27-239" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-240"><a href="#cb27-240" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-241"><a href="#cb27-241" aria-hidden="true" tabindex="-1"></a><span class="fu">### Applying `WHERE` Conditions</span></span>
+<span id="cb27-242"><a href="#cb27-242" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-243"><a href="#cb27-243" aria-hidden="true" tabindex="-1"></a>The <span class="in">`WHERE`</span> keyword is used to select only some rows of a table, filtered on a given Boolean condition. </span>
+<span id="cb27-244"><a href="#cb27-244" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-247"><a href="#cb27-247" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-248"><a href="#cb27-248" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-249"><a href="#cb27-249" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-250"><a href="#cb27-250" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb27-251"><a href="#cb27-251" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-252"><a href="#cb27-252" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span><span class="op">;</span></span>
+<span id="cb27-253"><a href="#cb27-253" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-254"><a href="#cb27-254" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-255"><a href="#cb27-255" aria-hidden="true" tabindex="-1"></a>We can add complexity to the <span class="in">`WHERE`</span> condition using the keywords <span class="in">`AND`</span>, <span class="in">`OR`</span>, and <span class="in">`NOT`</span>, much like we would in Python.</span>
+<span id="cb27-256"><a href="#cb27-256" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-259"><a href="#cb27-259" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-260"><a href="#cb27-260" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-261"><a href="#cb27-261" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-262"><a href="#cb27-262" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb27-263"><a href="#cb27-263" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-264"><a href="#cb27-264" aria-hidden="true" tabindex="-1"></a>WHERE cute <span class="op">&gt;</span> <span class="dv">0</span> OR year <span class="op">&gt;</span> <span class="dv">2013</span><span class="op">;</span></span>
+<span id="cb27-265"><a href="#cb27-265" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-266"><a href="#cb27-266" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-267"><a href="#cb27-267" aria-hidden="true" tabindex="-1"></a>To spare ourselves needing to write complicated logical expressions by combining several conditions, we can also filter for entries that are <span class="in">`IN`</span> a specified list of values. This is similar to the use of <span class="in">`in`</span> or <span class="in">`.isin`</span> in Python.</span>
+<span id="cb27-268"><a href="#cb27-268" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-271"><a href="#cb27-271" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-272"><a href="#cb27-272" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-273"><a href="#cb27-273" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-274"><a href="#cb27-274" aria-hidden="true" tabindex="-1"></a>SELECT name, year</span>
+<span id="cb27-275"><a href="#cb27-275" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-276"><a href="#cb27-276" aria-hidden="true" tabindex="-1"></a>WHERE name IN (<span class="st">'hiccup'</span>, <span class="st">'puff'</span>)<span class="op">;</span></span>
+<span id="cb27-277"><a href="#cb27-277" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-278"><a href="#cb27-278" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-279"><a href="#cb27-279" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Strings in SQL</span></span>
+<span id="cb27-280"><a href="#cb27-280" aria-hidden="true" tabindex="-1"></a>In <span class="in">`Python`</span>, there is no distinction between double <span class="in">`""`</span> and single quotes <span class="in">`''`</span>. SQL, on the other hand, distinguishes double quotes <span class="in">`""`</span> as *column names* and single quotes `''` as *strings*. For example, we can make the call</span>
+<span id="cb27-281"><a href="#cb27-281" aria-hidden="true" tabindex="-1"></a>  </span>
+<span id="cb27-282"><a href="#cb27-282" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT "birth weight"</span></span>
+<span id="cb27-283"><a href="#cb27-283" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM patient</span></span>
+<span id="cb27-284"><a href="#cb27-284" aria-hidden="true" tabindex="-1"></a><span class="in">    WHERE "first name" = 'Joey'</span></span>
+<span id="cb27-285"><a href="#cb27-285" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-286"><a href="#cb27-286" aria-hidden="true" tabindex="-1"></a>to select the column <span class="in">`"birth weight"`</span> from the <span class="in">`patient`</span> table and only select rows where the column <span class="in">`"first name"`</span> is equal to <span class="in">`'Joey'`</span>.</span>
+<span id="cb27-287"><a href="#cb27-287" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-288"><a href="#cb27-288" aria-hidden="true" tabindex="-1"></a><span class="fu">#### `WHERE` WITH `NULL` Values</span></span>
+<span id="cb27-289"><a href="#cb27-289" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-290"><a href="#cb27-290" aria-hidden="true" tabindex="-1"></a>You may have noticed earlier that our table actually has a missing value. In SQL, missing data is given the special value <span class="in">`NULL`</span>. <span class="in">`NULL`</span> behaves in a fundamentally different way to other data types. We can't use the typical operators (=, &gt;, and &lt;) on <span class="in">`NULL`</span> values (in fact, <span class="in">`NULL == NULL`</span> returns <span class="in">`False`</span>!). Instead, we check to see if a value <span class="in">`IS`</span> or <span class="in">`IS NOT`</span> <span class="in">`NULL`</span>.</span>
+<span id="cb27-291"><a href="#cb27-291" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-294"><a href="#cb27-294" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-295"><a href="#cb27-295" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-296"><a href="#cb27-296" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-297"><a href="#cb27-297" aria-hidden="true" tabindex="-1"></a>SELECT name, cute</span>
+<span id="cb27-298"><a href="#cb27-298" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-299"><a href="#cb27-299" aria-hidden="true" tabindex="-1"></a>WHERE cute IS NOT NULL<span class="op">;</span></span>
+<span id="cb27-300"><a href="#cb27-300" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-301"><a href="#cb27-301" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-302"><a href="#cb27-302" aria-hidden="true" tabindex="-1"></a><span class="fu">### Sorting and Restricting Output</span></span>
+<span id="cb27-303"><a href="#cb27-303" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-304"><a href="#cb27-304" aria-hidden="true" tabindex="-1"></a><span class="fu">#### Sorting with  `ORDER BY`</span></span>
+<span id="cb27-305"><a href="#cb27-305" aria-hidden="true" tabindex="-1"></a>What if we want the output table to appear in a certain order? The <span class="in">`ORDER BY`</span> keyword behaves similarly to <span class="in">`.sort_values()`</span> in <span class="in">`pandas`</span>. </span>
+<span id="cb27-306"><a href="#cb27-306" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-309"><a href="#cb27-309" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-310"><a href="#cb27-310" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-311"><a href="#cb27-311" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-312"><a href="#cb27-312" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-313"><a href="#cb27-313" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-314"><a href="#cb27-314" aria-hidden="true" tabindex="-1"></a>ORDER BY cute<span class="op">;</span></span>
+<span id="cb27-315"><a href="#cb27-315" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-316"><a href="#cb27-316" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-317"><a href="#cb27-317" aria-hidden="true" tabindex="-1"></a>By default, <span class="in">`ORDER BY`</span> will display results in ascending order (<span class="in">`ASC`</span>) with the lowest values at the top of the table. To sort in descending order, we use the <span class="in">`DESC`</span> keyword after specifying the column to be used for ordering.</span>
+<span id="cb27-318"><a href="#cb27-318" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-321"><a href="#cb27-321" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-322"><a href="#cb27-322" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-323"><a href="#cb27-323" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-324"><a href="#cb27-324" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-325"><a href="#cb27-325" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-326"><a href="#cb27-326" aria-hidden="true" tabindex="-1"></a>ORDER BY cute DESC<span class="op">;</span></span>
+<span id="cb27-327"><a href="#cb27-327" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-328"><a href="#cb27-328" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-329"><a href="#cb27-329" aria-hidden="true" tabindex="-1"></a>We can also tell SQL to <span class="in">`ORDER BY`</span> two columns at once. This will sort the table by the first listed column, then use the values in the second listed column to break any ties.</span>
+<span id="cb27-330"><a href="#cb27-330" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-333"><a href="#cb27-333" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-334"><a href="#cb27-334" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-335"><a href="#cb27-335" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-336"><a href="#cb27-336" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-337"><a href="#cb27-337" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-338"><a href="#cb27-338" aria-hidden="true" tabindex="-1"></a>ORDER BY year, cute DESC<span class="op">;</span></span>
+<span id="cb27-339"><a href="#cb27-339" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-340"><a href="#cb27-340" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-341"><a href="#cb27-341" aria-hidden="true" tabindex="-1"></a>Note that in this example, <span class="in">`year`</span> is sorted in ascending order and <span class="in">`cute`</span> in descending order. If you want <span class="in">`year`</span> to be ordered in descending order as well, you need to specify <span class="in">`year DESC, cute DESC;`</span>.</span>
+<span id="cb27-342"><a href="#cb27-342" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-343"><a href="#cb27-343" aria-hidden="true" tabindex="-1"></a><span class="fu">#### `LIMIT` vs. `OFFSET`</span></span>
+<span id="cb27-344"><a href="#cb27-344" aria-hidden="true" tabindex="-1"></a>In many instances, we are only concerned with a certain number of rows in the output table (for example, wanting to find the first two dragons in the table). The <span class="in">`LIMIT`</span> keyword restricts the output to a specified number of rows. It serves a function similar to that of <span class="in">`.head()`</span> in <span class="in">`pandas`</span>.</span>
+<span id="cb27-345"><a href="#cb27-345" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-348"><a href="#cb27-348" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-349"><a href="#cb27-349" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-350"><a href="#cb27-350" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-351"><a href="#cb27-351" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-352"><a href="#cb27-352" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-353"><a href="#cb27-353" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span><span class="op">;</span></span>
+<span id="cb27-354"><a href="#cb27-354" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-355"><a href="#cb27-355" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-356"><a href="#cb27-356" aria-hidden="true" tabindex="-1"></a>The <span class="in">`OFFSET`</span> keyword indicates the index at which <span class="in">`LIMIT`</span> should start. In other words, we can use <span class="in">`OFFSET`</span> to shift where the <span class="in">`LIMIT`</span>ing begins by a specified number of rows. For example, we might care about the dragons that are at positions 2 and 3 in the table. </span>
+<span id="cb27-357"><a href="#cb27-357" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-360"><a href="#cb27-360" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb27-361"><a href="#cb27-361" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb27-362"><a href="#cb27-362" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb27-363"><a href="#cb27-363" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb27-364"><a href="#cb27-364" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb27-365"><a href="#cb27-365" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">2</span></span>
+<span id="cb27-366"><a href="#cb27-366" aria-hidden="true" tabindex="-1"></a>OFFSET <span class="dv">1</span><span class="op">;</span></span>
+<span id="cb27-367"><a href="#cb27-367" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb27-368"><a href="#cb27-368" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-369"><a href="#cb27-369" aria-hidden="true" tabindex="-1"></a>With these keywords in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
+<span id="cb27-370"><a href="#cb27-370" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-371"><a href="#cb27-371" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
+<span id="cb27-372"><a href="#cb27-372" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
+<span id="cb27-373"><a href="#cb27-373" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
+<span id="cb27-374"><a href="#cb27-374" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
+<span id="cb27-375"><a href="#cb27-375" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
+<span id="cb27-376"><a href="#cb27-376" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
+<span id="cb27-377"><a href="#cb27-377" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-378"><a href="#cb27-378" aria-hidden="true" tabindex="-1"></a><span class="fu">## Summary</span></span>
+<span id="cb27-379"><a href="#cb27-379" aria-hidden="true" tabindex="-1"></a>Let's summarize what we've learned so far. We know that <span class="in">`SELECT`</span> and <span class="in">`FROM`</span> are the fundamental building blocks of any SQL query. We can augment these two keywords with additional clauses to refine the data in our output table. </span>
+<span id="cb27-380"><a href="#cb27-380" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-381"><a href="#cb27-381" aria-hidden="true" tabindex="-1"></a>Any clauses that we include must follow a strict ordering within the query:</span>
+<span id="cb27-382"><a href="#cb27-382" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb27-383"><a href="#cb27-383" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
+<span id="cb27-384"><a href="#cb27-384" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
+<span id="cb27-385"><a href="#cb27-385" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
+<span id="cb27-386"><a href="#cb27-386" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
+<span id="cb27-387"><a href="#cb27-387" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
+<span id="cb27-388"><a href="#cb27-388" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;]</span></span>
+<span id="cb27-389"><a href="#cb27-389" aria-hidden="true" tabindex="-1"></a><span class="in">    </span></span>
+<span id="cb27-390"><a href="#cb27-390" aria-hidden="true" tabindex="-1"></a>Here, any clause contained in square brackets <span class="in">`[ ]`</span> is optional —— we only need to use the keyword if it is relevant to the table operation we want to perform. Also note that by convention, we use all caps for keywords in SQL statements and use newlines to make code more readable.</span>
 </code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
 </div> <!-- /content -->
diff --git a/docs/sql_II/sql_II.html b/docs/sql_II/sql_II.html
index 0dd27558..ad5f808e 100644
--- a/docs/sql_II/sql_II.html
+++ b/docs/sql_II/sql_II.html
@@ -455,17 +455,17 @@ <h2 data-number="21.1" class="anchored" data-anchor-id="aggregating-with-group-b
 </thead>
 <tbody>
 <tr class="odd">
+<td>appetizer</td>
+<td>12</td>
+</tr>
+<tr class="even">
 <td>entree</td>
 <td>30</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>dessert</td>
 <td>5</td>
 </tr>
-<tr class="odd">
-<td>appetizer</td>
-<td>12</td>
-</tr>
 </tbody>
 </table>
 
@@ -522,3783 +522,225 @@ <h2 data-number="21.1" class="anchored" data-anchor-id="aggregating-with-group-b
 <td>potsticker</td>
 </tr>
 </tbody>
-</table>
-
-<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
-</div>
-</div>
-<p>To count the number of rows associated with each group, we use the <code>COUNT</code> keyword. Calling <code>COUNT(*)</code> will compute the total number of rows in each group, including rows with null values. Its <code>pandas</code> equivalent is <code>.groupby().size()</code>.</p>
-<p>Recall the <code>Dragon</code> table from the previous lecture:</p>
-<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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="op">%%</span>sql</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<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">
-<span style="None">Running query in 'basic'</span>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="7">
-<table data-quarto-postprocess="true" class="table table-sm table-striped small">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th">name</th>
-<th data-quarto-table-cell-role="th">year</th>
-<th data-quarto-table-cell-role="th">cute</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>hiccup</td>
-<td>2010</td>
-<td>10</td>
-</tr>
-<tr class="even">
-<td>drogon</td>
-<td>2011</td>
-<td>-100</td>
-</tr>
-<tr class="odd">
-<td>dragon 2</td>
-<td>2019</td>
-<td>0</td>
-</tr>
-<tr class="even">
-<td>puff</td>
-<td>2010</td>
-<td>100</td>
-</tr>
-<tr class="odd">
-<td>smaug</td>
-<td>2011</td>
-<td>None</td>
-</tr>
-</tbody>
-</table>
-
-<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
-</div>
-</div>
-<p>Notice that <code>COUNT(*)</code> and <code>COUNT(cute)</code> result in different outputs.</p>
-<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(<span class="op">*</span>)</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>GROUP BY year<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">
-<span style="None">Running query in 'basic'</span>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="8">
-<table data-quarto-postprocess="true" class="table table-sm table-striped small">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th">year</th>
-<th data-quarto-table-cell-role="th">count_star()</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>2010</td>
-<td>2</td>
-</tr>
-<tr class="even">
-<td>2011</td>
-<td>2</td>
-</tr>
-<tr class="odd">
-<td>2019</td>
-<td>1</td>
-</tr>
-</tbody>
-</table>
-
-<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
-</div>
-</div>
-<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="9">
-<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><span class="op">%%</span>sql</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(cute)</span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>GROUP BY year<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">
-<span style="None">Running query in 'basic'</span>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="9">
-<table data-quarto-postprocess="true" class="table table-sm table-striped small">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th">year</th>
-<th data-quarto-table-cell-role="th">count(cute)</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>2010</td>
-<td>2</td>
-</tr>
-<tr class="even">
-<td>2011</td>
-<td>1</td>
-</tr>
-<tr class="odd">
-<td>2019</td>
-<td>1</td>
-</tr>
-</tbody>
-</table>
-
-<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
-</div>
-</div>
-<p>With this definition of <code>GROUP BY</code> in hand, let’s update our SQL order of operations. Remember: <em>every</em> SQL query must list clauses in this order.</p>
-<pre><code>SELECT &lt;column expression list&gt;
-FROM &lt;table&gt;
-[WHERE &lt;predicate&gt;]
-[GROUP BY &lt;column list&gt;]
-[ORDER BY &lt;column list&gt;]
-[LIMIT &lt;number of rows&gt;]
-[OFFSET &lt;number of rows&gt;];</code></pre>
-<p>Note that we can use the <code>AS</code> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<code>MAX</code>, <code>MIN</code>, etc.).</p>
-</section>
-<section id="filtering-groups" class="level2" data-number="21.2">
-<h2 data-number="21.2" class="anchored" data-anchor-id="filtering-groups"><span class="header-section-number">21.2</span> Filtering Groups</h2>
-<p>Now, what if we only want groups that meet a certain condition? <code>HAVING</code> filters groups by applying some condition across all rows in each group. We interpret it as a way to keep only the groups <code>HAVING</code> some condition. Note the difference between <code>WHERE</code> and <code>HAVING</code>: we use <code>WHERE</code> to filter rows, whereas we use <code>HAVING</code> to filter <em>groups</em>. <code>WHERE</code> precedes <code>HAVING</code> in terms of how SQL executes a query.</p>
-<p>Let’s take a look at the <code>Dish</code> table to see how we can use <code>HAVING</code>. Say we want to group dishes with a cost greater than 4 by <code>type</code> and only keep groups where the max cost is less than 10.</p>
-<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, COUNT(<span class="op">*</span>)</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>WHERE cost <span class="op">&gt;</span> <span class="dv">4</span></span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span></span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>HAVING MAX(cost) <span class="op">&lt;</span>  <span class="dv">10</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">
-<span style="None">Running query in 'basic'</span>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<table data-quarto-postprocess="true" class="table table-sm table-striped small">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th">type</th>
-<th data-quarto-table-cell-role="th">count_star()</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>dessert</td>
-<td>1</td>
-</tr>
-</tbody>
-</table>
-
-<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
-</div>
-</div>
-<p>Here, we first use <code>WHERE</code> to filter for rows with a cost greater than 4. We then group our values by <code>type</code> before applying the <code>HAVING</code> operator. With <code>HAVING</code>, we can filter our groups based on if the max cost is less than 10.</p>
-</section>
-<section id="summary-sql" class="level2" data-number="21.3">
-<h2 data-number="21.3" class="anchored" data-anchor-id="summary-sql"><span class="header-section-number">21.3</span> Summary: SQL</h2>
-<p>With this definition of <code>GROUP BY</code> and <code>HAVING</code> in hand, let’s update our SQL order of operations. Remember: <em>every</em> SQL query must list clauses in this order.</p>
-<pre><code>SELECT &lt;column expression list&gt;
-FROM &lt;table&gt;
-[WHERE &lt;predicate&gt;]
-[GROUP BY &lt;column list&gt;]
-[ORDER BY &lt;column list&gt;]
-[LIMIT &lt;number of rows&gt;]
-[OFFSET &lt;number of rows&gt;];</code></pre>
-<p>Note that we can use the <code>AS</code> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<code>MAX</code>, <code>MIN</code>, etc.).</p>
-</section>
-<section id="eda-in-sql" class="level2" data-number="21.4">
-<h2 data-number="21.4" class="anchored" data-anchor-id="eda-in-sql"><span class="header-section-number">21.4</span> EDA in SQL</h2>
-<p>In the last lecture, we mostly worked under the assumption that our data had already been cleaned. However, as we saw in our first pass through the data science lifecycle, we’re very unlikely to be given data that is free of formatting issues. With this in mind, we’ll want to learn how to clean and transform data in SQL.</p>
-<p>Our typical workflow when working with “big data” is:</p>
-<ol type="1">
-<li>Use SQL to query data from a database</li>
-<li>Use Python (with <code>pandas</code>) to analyze this data in detail</li>
-</ol>
-<p>We can, however, still perform simple data cleaning and re-structuring using SQL directly. To do so, we’ll use the <code>Title</code> table from the <code>imdb_duck</code> database, which contains information about movies and actors.</p>
-<p>Let’s load in the <code>imdb_duck</code> database.</p>
-<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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="im">import</span> os</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>os.environ[<span class="st">"TQDM_DISABLE"</span>] <span class="op">=</span> <span class="st">"1"</span></span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> os.path.exists(<span class="st">"/home/jovyan/shared/sql/imdb_duck.db"</span>):</span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:////home/jovyan/shared/sql/imdb_duck.db"</span></span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="cf">elif</span> os.path.exists(<span class="st">"data/imdb_duck.db"</span>):</span>
-<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span>  <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
-<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
-<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>    <span class="im">import</span> gdown</span>
-<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>    url <span class="op">=</span> <span class="st">'https://drive.google.com/uc?id=10tKOHGLt9QoOgq5Ii-FhxpB9lDSQgl1O'</span></span>
-<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>    output_path <span class="op">=</span> <span class="st">'data/imdb_duck.db'</span></span>
-<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>    gdown.download(url, output_path, quiet<span class="op">=</span><span class="va">False</span>)</span>
-<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:///data/imdb_duck.db"</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">
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-To: /Users/Ishani/Desktop/course-notes/sql_II/data/imdb_duck.db</code></pre>
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-<pre><code>  5%|█████▎                                                                                                         | 129M/2.71G [00:10&lt;03:57, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▍                                                                                                         | 132M/2.71G [00:11&lt;03:06, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▍                                                                                                         | 134M/2.71G [00:11&lt;03:28, 12.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▌                                                                                                         | 136M/2.71G [00:11&lt;03:05, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▋                                                                                                         | 139M/2.71G [00:11&lt;02:38, 16.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▊                                                                                                         | 141M/2.71G [00:11&lt;03:25, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▊                                                                                                         | 143M/2.71G [00:11&lt;03:30, 12.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|█████▉                                                                                                         | 146M/2.71G [00:12&lt;02:53, 14.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  5%|██████                                                                                                         | 148M/2.71G [00:12&lt;03:33, 12.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▏                                                                                                        | 150M/2.71G [00:12&lt;03:40, 11.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▏                                                                                                        | 152M/2.71G [00:12&lt;03:55, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▎                                                                                                        | 153M/2.71G [00:12&lt;04:01, 10.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▎                                                                                                        | 156M/2.71G [00:12&lt;03:09, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▍                                                                                                        | 157M/2.71G [00:13&lt;03:23, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▌                                                                                                        | 160M/2.71G [00:13&lt;03:33, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▋                                                                                                        | 162M/2.71G [00:13&lt;03:09, 13.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▋                                                                                                        | 164M/2.71G [00:13&lt;03:34, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▊                                                                                                        | 165M/2.71G [00:13&lt;03:22, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▊                                                                                                        | 167M/2.71G [00:13&lt;04:08, 10.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▉                                                                                                        | 168M/2.71G [00:14&lt;03:55, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|██████▉                                                                                                        | 171M/2.71G [00:14&lt;03:09, 13.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|███████                                                                                                        | 172M/2.71G [00:14&lt;04:09, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|███████                                                                                                        | 174M/2.71G [00:14&lt;04:15, 9.92MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  6%|███████▏                                                                                                       | 176M/2.71G [00:15&lt;05:38, 7.50MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▏                                                                                                       | 177M/2.71G [00:15&lt;05:24, 7.82MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▎                                                                                                       | 178M/2.71G [00:15&lt;05:40, 7.45MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▎                                                                                                       | 179M/2.71G [00:15&lt;06:47, 6.22MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▎                                                                                                       | 180M/2.71G [00:15&lt;06:57, 6.07MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▍                                                                                                       | 181M/2.71G [00:15&lt;07:53, 5.34MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▍                                                                                                       | 183M/2.71G [00:16&lt;05:27, 7.73MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▌                                                                                                       | 184M/2.71G [00:16&lt;05:33, 7.57MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▋                                                                                                       | 187M/2.71G [00:16&lt;05:37, 7.49MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▋                                                                                                       | 189M/2.71G [00:16&lt;04:06, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▊                                                                                                       | 191M/2.71G [00:16&lt;04:38, 9.05MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▊                                                                                                       | 192M/2.71G [00:17&lt;05:53, 7.12MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|███████▉                                                                                                       | 194M/2.71G [00:17&lt;07:33, 5.55MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|████████                                                                                                       | 196M/2.71G [00:18&lt;08:36, 4.87MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|████████                                                                                                       | 198M/2.71G [00:18&lt;06:38, 6.31MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|████████▏                                                                                                      | 199M/2.71G [00:18&lt;08:39, 4.84MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  7%|████████▎                                                                                                      | 202M/2.71G [00:19&lt;08:29, 4.93MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▍                                                                                                      | 206M/2.71G [00:19&lt;05:16, 7.93MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▍                                                                                                      | 207M/2.71G [00:19&lt;06:25, 6.50MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▌                                                                                                      | 209M/2.71G [00:20&lt;07:59, 5.22MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▋                                                                                                      | 212M/2.71G [00:20&lt;05:00, 8.32MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▊                                                                                                      | 214M/2.71G [00:20&lt;05:55, 7.03MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|████████▉                                                                                                      | 218M/2.71G [00:21&lt;06:15, 6.65MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|█████████                                                                                                      | 221M/2.71G [00:21&lt;04:34, 9.09MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|█████████                                                                                                      | 223M/2.71G [00:21&lt;05:29, 7.55MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|█████████▏                                                                                                     | 226M/2.71G [00:22&lt;04:04, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  8%|█████████▎                                                                                                     | 228M/2.71G [00:22&lt;05:21, 7.72MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▍                                                                                                     | 231M/2.71G [00:22&lt;04:00, 10.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▌                                                                                                     | 233M/2.71G [00:23&lt;04:59, 8.27MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▌                                                                                                     | 235M/2.71G [00:23&lt;06:36, 6.25MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▋                                                                                                     | 237M/2.71G [00:23&lt;05:23, 7.66MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▊                                                                                                     | 239M/2.71G [00:23&lt;05:51, 7.05MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▊                                                                                                     | 241M/2.71G [00:24&lt;04:22, 9.42MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|█████████▉                                                                                                     | 244M/2.71G [00:24&lt;03:17, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|██████████                                                                                                     | 246M/2.71G [00:24&lt;03:47, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|██████████▏                                                                                                    | 249M/2.71G [00:24&lt;03:06, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|██████████▎                                                                                                    | 251M/2.71G [00:24&lt;02:52, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|██████████▍                                                                                                    | 255M/2.71G [00:24&lt;02:10, 18.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>  9%|██████████▌                                                                                                    | 257M/2.71G [00:24&lt;02:30, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|██████████▌                                                                                                    | 260M/2.71G [00:25&lt;02:31, 16.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|██████████▋                                                                                                    | 262M/2.71G [00:25&lt;03:22, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|██████████▊                                                                                                    | 265M/2.71G [00:25&lt;02:51, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|██████████▉                                                                                                    | 267M/2.71G [00:25&lt;02:38, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████                                                                                                    | 269M/2.71G [00:25&lt;02:28, 16.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████                                                                                                    | 271M/2.71G [00:25&lt;03:04, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████▏                                                                                                   | 273M/2.71G [00:26&lt;02:48, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████▎                                                                                                   | 275M/2.71G [00:26&lt;04:06, 9.90MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████▍                                                                                                   | 279M/2.71G [00:26&lt;02:53, 14.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 10%|███████████▌                                                                                                   | 283M/2.71G [00:26&lt;02:31, 16.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|███████████▋                                                                                                   | 287M/2.71G [00:27&lt;02:29, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|███████████▊                                                                                                   | 289M/2.71G [00:27&lt;02:22, 17.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|███████████▉                                                                                                   | 291M/2.71G [00:27&lt;02:31, 16.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|███████████▉                                                                                                   | 293M/2.71G [00:27&lt;03:01, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████                                                                                                   | 295M/2.71G [00:27&lt;02:53, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▏                                                                                                  | 298M/2.71G [00:27&lt;02:39, 15.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▎                                                                                                  | 300M/2.71G [00:27&lt;02:31, 15.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▎                                                                                                  | 302M/2.71G [00:28&lt;03:04, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▍                                                                                                  | 304M/2.71G [00:28&lt;03:11, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▍                                                                                                  | 305M/2.71G [00:28&lt;04:24, 9.09MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▌                                                                                                  | 308M/2.71G [00:28&lt;03:25, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 11%|████████████▋                                                                                                  | 310M/2.71G [00:28&lt;02:58, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|████████████▊                                                                                                  | 313M/2.71G [00:29&lt;03:17, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|████████████▉                                                                                                  | 315M/2.71G [00:29&lt;03:14, 12.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████                                                                                                  | 319M/2.71G [00:29&lt;02:22, 16.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▏                                                                                                 | 321M/2.71G [00:29&lt;03:00, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▏                                                                                                 | 323M/2.71G [00:29&lt;02:52, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▎                                                                                                 | 325M/2.71G [00:29&lt;02:42, 14.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▍                                                                                                 | 327M/2.71G [00:30&lt;03:40, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▍                                                                                                 | 329M/2.71G [00:30&lt;03:34, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▌                                                                                                 | 331M/2.71G [00:30&lt;04:38, 8.56MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▌                                                                                                 | 332M/2.71G [00:30&lt;04:27, 8.90MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▋                                                                                                 | 334M/2.71G [00:31&lt;05:29, 7.21MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▋                                                                                                 | 335M/2.71G [00:31&lt;05:12, 7.60MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▊                                                                                                 | 336M/2.71G [00:31&lt;05:10, 7.64MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▊                                                                                                 | 337M/2.71G [00:31&lt;06:50, 5.79MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 12%|█████████████▊                                                                                                 | 338M/2.71G [00:31&lt;06:19, 6.26MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|█████████████▉                                                                                                 | 339M/2.71G [00:31&lt;05:44, 6.89MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|█████████████▉                                                                                                 | 340M/2.71G [00:32&lt;07:29, 5.27MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|█████████████▉                                                                                                 | 341M/2.71G [00:32&lt;07:07, 5.55MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|██████████████                                                                                                 | 342M/2.71G [00:32&lt;09:13, 4.28MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|██████████████                                                                                                 | 343M/2.71G [00:33&lt;08:23, 4.71MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|██████████████                                                                                                 | 344M/2.71G [00:33&lt;08:53, 4.44MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|██████████████▏                                                                                                | 346M/2.71G [00:33&lt;07:35, 5.20MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
-<pre><code> 13%|██████████████▏                                                                                                | 347M/2.71G [00:33&lt;09:48, 4.02MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
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-<pre><code> 13%|██████████████▊                                                                                                | 363M/2.71G [00:36&lt;04:29, 8.70MB/s]</code></pre>
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-<pre><code> 13%|██████████████▉                                                                                                | 365M/2.71G [00:36&lt;03:55, 9.95MB/s]</code></pre>
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-<pre><code> 14%|██████████████▉                                                                                                | 366M/2.71G [00:36&lt;04:40, 8.36MB/s]</code></pre>
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-<pre><code> 14%|███████████████                                                                                                | 369M/2.71G [00:36&lt;03:39, 10.7MB/s]</code></pre>
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-<pre><code> 14%|███████████████▏                                                                                               | 371M/2.71G [00:36&lt;03:09, 12.3MB/s]</code></pre>
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-<pre><code> 14%|███████████████▏                                                                                               | 372M/2.71G [00:36&lt;04:31, 8.62MB/s]</code></pre>
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-<pre><code> 14%|███████████████▎                                                                                               | 374M/2.71G [00:37&lt;03:59, 9.77MB/s]</code></pre>
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-<pre><code> 14%|███████████████▍                                                                                               | 376M/2.71G [00:37&lt;03:22, 11.5MB/s]</code></pre>
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-<pre><code> 14%|███████████████▍                                                                                               | 377M/2.71G [00:37&lt;04:17, 9.07MB/s]</code></pre>
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-<pre><code> 14%|███████████████▌                                                                                               | 380M/2.71G [00:37&lt;03:31, 11.0MB/s]</code></pre>
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-<pre><code> 14%|███████████████▌                                                                                               | 382M/2.71G [00:37&lt;03:03, 12.7MB/s]</code></pre>
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-<pre><code> 14%|███████████████▋                                                                                               | 384M/2.71G [00:38&lt;03:52, 10.0MB/s]</code></pre>
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-<pre><code> 14%|███████████████▊                                                                                               | 386M/2.71G [00:38&lt;03:17, 11.8MB/s]</code></pre>
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-<pre><code> 14%|███████████████▉                                                                                               | 388M/2.71G [00:38&lt;02:55, 13.2MB/s]</code></pre>
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-<pre><code> 14%|███████████████▉                                                                                               | 390M/2.71G [00:38&lt;03:42, 10.4MB/s]</code></pre>
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-<pre><code> 14%|████████████████                                                                                               | 393M/2.71G [00:38&lt;02:51, 13.6MB/s]</code></pre>
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-<pre><code> 15%|████████████████▏                                                                                              | 396M/2.71G [00:38&lt;02:30, 15.4MB/s]</code></pre>
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-<pre><code> 15%|████████████████▎                                                                                              | 398M/2.71G [00:39&lt;03:14, 11.9MB/s]</code></pre>
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-<pre><code> 15%|████████████████▎                                                                                              | 400M/2.71G [00:39&lt;03:10, 12.2MB/s]</code></pre>
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-<pre><code> 15%|████████████████▍                                                                                              | 402M/2.71G [00:39&lt;02:55, 13.1MB/s]</code></pre>
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-<pre><code> 15%|████████████████▌                                                                                              | 404M/2.71G [00:39&lt;03:33, 10.8MB/s]</code></pre>
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-<pre><code> 15%|████████████████▌                                                                                              | 405M/2.71G [00:39&lt;03:27, 11.1MB/s]</code></pre>
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-<pre><code> 15%|████████████████▋                                                                                              | 407M/2.71G [00:39&lt;03:27, 11.1MB/s]</code></pre>
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-<pre><code> 15%|████████████████▋                                                                                              | 408M/2.71G [00:40&lt;04:12, 9.13MB/s]</code></pre>
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-<pre><code> 15%|████████████████▊                                                                                              | 410M/2.71G [00:40&lt;03:49, 10.0MB/s]</code></pre>
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-<pre><code> 15%|████████████████▊                                                                                              | 412M/2.71G [00:40&lt;03:34, 10.7MB/s]</code></pre>
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-<pre><code> 15%|████████████████▉                                                                                              | 413M/2.71G [00:40&lt;04:29, 8.53MB/s]</code></pre>
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-<pre><code> 15%|█████████████████                                                                                              | 416M/2.71G [00:40&lt;03:18, 11.6MB/s]</code></pre>
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-<pre><code> 15%|█████████████████                                                                                              | 418M/2.71G [00:40&lt;02:58, 12.8MB/s]</code></pre>
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-<pre><code> 15%|█████████████████▏                                                                                             | 419M/2.71G [00:41&lt;03:11, 12.0MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▏                                                                                             | 421M/2.71G [00:41&lt;03:18, 11.6MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▎                                                                                             | 423M/2.71G [00:41&lt;02:50, 13.4MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▌                                                                                             | 428M/2.71G [00:41&lt;03:16, 11.6MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▋                                                                                             | 431M/2.71G [00:41&lt;02:30, 15.2MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▋                                                                                             | 434M/2.71G [00:41&lt;02:22, 16.0MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▊                                                                                             | 436M/2.71G [00:42&lt;03:20, 11.4MB/s]</code></pre>
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-<pre><code> 16%|█████████████████▉                                                                                             | 438M/2.71G [00:42&lt;02:54, 13.1MB/s]</code></pre>
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-<pre><code> 16%|██████████████████▏                                                                                            | 444M/2.71G [00:42&lt;02:55, 12.9MB/s]</code></pre>
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-<pre><code> 16%|██████████████████▎                                                                                            | 447M/2.71G [00:43&lt;02:33, 14.7MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▎                                                                                            | 449M/2.71G [00:43&lt;03:09, 11.9MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▍                                                                                            | 451M/2.71G [00:43&lt;02:47, 13.5MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▌                                                                                            | 453M/2.71G [00:43&lt;02:43, 13.8MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▌                                                                                            | 455M/2.71G [00:43&lt;03:26, 10.9MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▋                                                                                            | 457M/2.71G [00:43&lt;02:47, 13.5MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▊                                                                                            | 459M/2.71G [00:44&lt;02:47, 13.4MB/s]</code></pre>
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-<pre><code> 17%|██████████████████▉                                                                                            | 462M/2.71G [00:44&lt;03:04, 12.2MB/s]</code></pre>
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-<pre><code> 17%|███████████████████▏                                                                                           | 469M/2.71G [00:44&lt;03:18, 11.3MB/s]</code></pre>
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-<pre><code> 17%|███████████████████▏                                                                                           | 470M/2.71G [00:45&lt;03:46, 9.90MB/s]</code></pre>
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-<pre><code> 17%|███████████████████▎                                                                                           | 472M/2.71G [00:45&lt;03:26, 10.8MB/s]</code></pre>
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-<pre><code> 17%|███████████████████▍                                                                                           | 474M/2.71G [00:45&lt;03:31, 10.6MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▌                                                                                           | 477M/2.71G [00:45&lt;02:55, 12.7MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▌                                                                                           | 479M/2.71G [00:45&lt;02:27, 15.2MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▋                                                                                           | 481M/2.71G [00:45&lt;03:00, 12.4MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▊                                                                                           | 483M/2.71G [00:45&lt;02:42, 13.7MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▉                                                                                           | 486M/2.71G [00:46&lt;02:16, 16.3MB/s]</code></pre>
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-<pre><code> 18%|███████████████████▉                                                                                           | 488M/2.71G [00:46&lt;02:36, 14.2MB/s]</code></pre>
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-<pre><code> 18%|████████████████████▏                                                                                          | 493M/2.71G [00:46&lt;02:22, 15.5MB/s]</code></pre>
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-<pre><code> 18%|████████████████████▎                                                                                          | 495M/2.71G [00:46&lt;02:16, 16.2MB/s]</code></pre>
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-<pre><code> 18%|████████████████████▎                                                                                          | 497M/2.71G [00:46&lt;02:47, 13.2MB/s]</code></pre>
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-<pre><code> 18%|████████████████████▍                                                                                          | 499M/2.71G [00:47&lt;02:31, 14.6MB/s]</code></pre>
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-<pre><code> 18%|████████████████████▌                                                                                          | 501M/2.71G [00:47&lt;02:25, 15.2MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▌                                                                                          | 503M/2.71G [00:47&lt;02:39, 13.8MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▋                                                                                          | 505M/2.71G [00:47&lt;04:02, 9.09MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▋                                                                                          | 506M/2.71G [00:48&lt;05:52, 6.26MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▊                                                                                          | 508M/2.71G [00:48&lt;05:37, 6.54MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▊                                                                                          | 509M/2.71G [00:48&lt;05:15, 6.98MB/s]</code></pre>
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-<pre><code> 19%|████████████████████▉                                                                                          | 512M/2.71G [00:48&lt;03:58, 9.22MB/s]</code></pre>
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-<pre><code> 19%|█████████████████████                                                                                          | 515M/2.71G [00:48&lt;02:43, 13.4MB/s]</code></pre>
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-<pre><code> 19%|█████████████████████▏                                                                                         | 517M/2.71G [00:49&lt;03:06, 11.8MB/s]</code></pre>
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-<pre><code> 19%|█████████████████████▎                                                                                         | 521M/2.71G [00:49&lt;02:15, 16.2MB/s]</code></pre>
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-<pre><code> 19%|█████████████████████▍                                                                                         | 524M/2.71G [00:49&lt;01:54, 19.0MB/s]</code></pre>
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-<pre><code> 19%|█████████████████████▌                                                                                         | 527M/2.71G [00:49&lt;02:43, 13.4MB/s]</code></pre>
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-<pre><code> 20%|█████████████████████▋                                                                                         | 530M/2.71G [00:49&lt;02:24, 15.1MB/s]</code></pre>
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-<pre><code> 20%|█████████████████████▊                                                                                         | 532M/2.71G [00:49&lt;02:32, 14.3MB/s]</code></pre>
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-<pre><code> 20%|█████████████████████▉                                                                                         | 536M/2.71G [00:50&lt;02:23, 15.2MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████                                                                                         | 540M/2.71G [00:50&lt;02:40, 13.5MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▏                                                                                        | 542M/2.71G [00:50&lt;02:45, 13.1MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▎                                                                                        | 544M/2.71G [00:50&lt;02:18, 15.7MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▍                                                                                        | 547M/2.71G [00:50&lt;02:14, 16.1MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▍                                                                                        | 549M/2.71G [00:50&lt;02:07, 17.0MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▌                                                                                        | 551M/2.71G [00:51&lt;02:36, 13.9MB/s]</code></pre>
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-<pre><code> 20%|██████████████████████▋                                                                                        | 555M/2.71G [00:51&lt;01:51, 19.4MB/s]</code></pre>
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-<pre><code> 21%|██████████████████████▊                                                                                        | 558M/2.71G [00:51&lt;01:48, 19.9MB/s]</code></pre>
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-<pre><code> 36%|████████████████████████████████████████▎                                                                      | 985M/2.71G [01:25&lt;02:21, 12.2MB/s]</code></pre>
-</div>
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-<pre><code> 36%|████████████████████████████████████████▎                                                                      | 986M/2.71G [01:25&lt;02:52, 10.0MB/s]</code></pre>
-</div>
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-<pre><code> 36%|████████████████████████████████████████▍                                                                      | 988M/2.71G [01:26&lt;03:19, 8.66MB/s]</code></pre>
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-<pre><code> 36%|████████████████████████████████████████▌                                                                      | 990M/2.71G [01:26&lt;02:54, 9.86MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|████████████████████████████████████████▊                                                                      | 996M/2.71G [01:26&lt;01:30, 19.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|████████████████████████████████████████▊                                                                      | 999M/2.71G [01:26&lt;01:51, 15.4MB/s]</code></pre>
-</div>
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-<pre><code> 37%|████████████████████████████████████████▌                                                                     | 1.00G/2.71G [01:26&lt;01:53, 15.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|████████████████████████████████████████▊                                                                     | 1.01G/2.71G [01:27&lt;01:25, 20.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|████████████████████████████████████████▊                                                                     | 1.01G/2.71G [01:27&lt;01:59, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|████████████████████████████████████████▉                                                                     | 1.01G/2.71G [01:27&lt;02:31, 11.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|█████████████████████████████████████████                                                                     | 1.01G/2.71G [01:27&lt;02:25, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|█████████████████████████████████████████                                                                     | 1.01G/2.71G [01:27&lt;02:08, 13.2MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
-<pre><code> 37%|█████████████████████████████████████████▏                                                                    | 1.02G/2.71G [01:28&lt;02:02, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▎                                                                    | 1.02G/2.71G [01:28&lt;02:34, 11.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▍                                                                    | 1.02G/2.71G [01:28&lt;02:02, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▌                                                                    | 1.02G/2.71G [01:28&lt;01:47, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▋                                                                    | 1.03G/2.71G [01:28&lt;02:06, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▊                                                                    | 1.03G/2.71G [01:29&lt;01:30, 18.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|█████████████████████████████████████████▉                                                                    | 1.03G/2.71G [01:29&lt;01:23, 20.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|██████████████████████████████████████████                                                                    | 1.04G/2.71G [01:29&lt;01:52, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|██████████████████████████████████████████▏                                                                   | 1.04G/2.71G [01:29&lt;01:32, 18.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 38%|██████████████████████████████████████████▎                                                                   | 1.04G/2.71G [01:29&lt;01:27, 19.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▍                                                                   | 1.05G/2.71G [01:29&lt;01:46, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▌                                                                   | 1.05G/2.71G [01:30&lt;01:43, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▋                                                                   | 1.05G/2.71G [01:30&lt;01:22, 20.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▊                                                                   | 1.06G/2.71G [01:30&lt;01:45, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▉                                                                   | 1.06G/2.71G [01:30&lt;01:40, 16.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|██████████████████████████████████████████▉                                                                   | 1.06G/2.71G [01:30&lt;01:37, 17.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|███████████████████████████████████████████                                                                   | 1.06G/2.71G [01:30&lt;01:48, 15.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|███████████████████████████████████████████▏                                                                  | 1.07G/2.71G [01:31&lt;01:43, 15.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|███████████████████████████████████████████▎                                                                  | 1.07G/2.71G [01:31&lt;01:26, 18.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 39%|███████████████████████████████████████████▍                                                                  | 1.07G/2.71G [01:31&lt;01:58, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 40%|███████████████████████████████████████████▌                                                                  | 1.07G/2.71G [01:31&lt;01:44, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 40%|███████████████████████████████████████████▋                                                                  | 1.08G/2.71G [01:31&lt;01:41, 16.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 40%|███████████████████████████████████████████▋                                                                  | 1.08G/2.71G [01:31&lt;01:42, 15.9MB/s]</code></pre>
-</div>
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-<pre><code> 40%|███████████████████████████████████████████▊                                                                  | 1.08G/2.71G [01:32&lt;02:01, 13.4MB/s]</code></pre>
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-<pre><code> 40%|███████████████████████████████████████████▉                                                                  | 1.08G/2.71G [01:32&lt;01:51, 14.6MB/s]</code></pre>
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-<pre><code> 40%|███████████████████████████████████████████▉                                                                  | 1.08G/2.71G [01:32&lt;02:29, 10.9MB/s]</code></pre>
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-<pre><code> 40%|████████████████████████████████████████████                                                                  | 1.09G/2.71G [01:32&lt;02:00, 13.4MB/s]</code></pre>
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-<pre><code> 40%|████████████████████████████████████████████▏                                                                 | 1.09G/2.71G [01:32&lt;01:58, 13.7MB/s]</code></pre>
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-<pre><code> 40%|████████████████████████████████████████████▎                                                                 | 1.09G/2.71G [01:33&lt;01:53, 14.2MB/s]</code></pre>
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-<pre><code> 40%|████████████████████████████████████████████▍                                                                 | 1.10G/2.71G [01:33&lt;01:42, 15.8MB/s]</code></pre>
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-<pre><code> 40%|████████████████████████████████████████████▌                                                                 | 1.10G/2.71G [01:33&lt;01:38, 16.3MB/s]</code></pre>
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-<pre><code> 41%|████████████████████████████████████████████▋                                                                 | 1.10G/2.71G [01:33&lt;01:41, 15.8MB/s]</code></pre>
-</div>
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-<pre><code> 41%|████████████████████████████████████████████▋                                                                 | 1.10G/2.71G [01:33&lt;02:05, 12.8MB/s]</code></pre>
-</div>
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-<pre><code> 41%|████████████████████████████████████████████▊                                                                 | 1.11G/2.71G [01:33&lt;01:35, 16.9MB/s]</code></pre>
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-<pre><code> 41%|████████████████████████████████████████████▉                                                                 | 1.11G/2.71G [01:34&lt;02:13, 12.0MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████                                                                 | 1.11G/2.71G [01:34&lt;02:10, 12.2MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████                                                                 | 1.11G/2.71G [01:34&lt;02:09, 12.4MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▏                                                                | 1.11G/2.71G [01:34&lt;02:47, 9.56MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▏                                                                | 1.11G/2.71G [01:34&lt;02:33, 10.4MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▎                                                                | 1.12G/2.71G [01:34&lt;02:30, 10.6MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▎                                                                | 1.12G/2.71G [01:35&lt;03:04, 8.63MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▍                                                                | 1.12G/2.71G [01:35&lt;02:27, 10.8MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▍                                                                | 1.12G/2.71G [01:35&lt;02:17, 11.6MB/s]</code></pre>
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-<pre><code> 41%|█████████████████████████████████████████████▌                                                                | 1.12G/2.71G [01:35&lt;02:00, 13.2MB/s]</code></pre>
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-<pre><code> 42%|█████████████████████████████████████████████▋                                                                | 1.13G/2.71G [01:35&lt;01:49, 14.4MB/s]</code></pre>
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-<pre><code> 42%|█████████████████████████████████████████████▊                                                                | 1.13G/2.71G [01:35&lt;01:59, 13.2MB/s]</code></pre>
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-<pre><code> 42%|█████████████████████████████████████████████▉                                                                | 1.13G/2.71G [01:35&lt;01:17, 20.4MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████                                                                | 1.14G/2.71G [01:36&lt;01:31, 17.3MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▏                                                               | 1.14G/2.71G [01:36&lt;01:22, 19.1MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▎                                                               | 1.14G/2.71G [01:36&lt;01:10, 22.3MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▍                                                               | 1.15G/2.71G [01:36&lt;01:45, 14.9MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▌                                                               | 1.15G/2.71G [01:36&lt;01:38, 15.8MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▋                                                               | 1.15G/2.71G [01:37&lt;01:43, 15.1MB/s]</code></pre>
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-<pre><code> 42%|██████████████████████████████████████████████▋                                                               | 1.15G/2.71G [01:37&lt;02:23, 10.9MB/s]</code></pre>
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-<pre><code> 43%|██████████████████████████████████████████████▊                                                               | 1.15G/2.71G [01:37&lt;02:25, 10.7MB/s]</code></pre>
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-<pre><code> 43%|██████████████████████████████████████████████▊                                                               | 1.16G/2.71G [01:37&lt;03:02, 8.55MB/s]</code></pre>
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-<pre><code> 43%|██████████████████████████████████████████████▉                                                               | 1.16G/2.71G [01:38&lt;02:57, 8.76MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████                                                               | 1.16G/2.71G [01:38&lt;02:19, 11.1MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████                                                               | 1.16G/2.71G [01:38&lt;02:29, 10.4MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▏                                                              | 1.16G/2.71G [01:38&lt;02:13, 11.6MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▍                                                              | 1.17G/2.71G [01:38&lt;01:31, 16.8MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▍                                                              | 1.17G/2.71G [01:38&lt;01:37, 15.8MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▌                                                              | 1.17G/2.71G [01:39&lt;01:39, 15.4MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▋                                                              | 1.18G/2.71G [01:39&lt;01:27, 17.5MB/s]</code></pre>
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-<pre><code> 43%|███████████████████████████████████████████████▊                                                              | 1.18G/2.71G [01:39&lt;01:44, 14.7MB/s]</code></pre>
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-<pre><code> 44%|███████████████████████████████████████████████▉                                                              | 1.18G/2.71G [01:39&lt;01:38, 15.5MB/s]</code></pre>
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-<pre><code> 44%|███████████████████████████████████████████████▉                                                              | 1.18G/2.71G [01:39&lt;02:24, 10.5MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████                                                              | 1.19G/2.71G [01:40&lt;02:00, 12.7MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▏                                                             | 1.19G/2.71G [01:40&lt;01:55, 13.2MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▏                                                             | 1.19G/2.71G [01:40&lt;02:18, 11.0MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▎                                                             | 1.19G/2.71G [01:40&lt;02:31, 10.1MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▎                                                             | 1.19G/2.71G [01:41&lt;03:56, 6.43MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▍                                                             | 1.19G/2.71G [01:41&lt;04:00, 6.31MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▌                                                             | 1.20G/2.71G [01:41&lt;02:59, 8.44MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▊                                                             | 1.20G/2.71G [01:41&lt;02:15, 11.2MB/s]</code></pre>
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-<pre><code> 44%|████████████████████████████████████████████████▊                                                             | 1.20G/2.71G [01:42&lt;02:20, 10.7MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████                                                             | 1.21G/2.71G [01:43&lt;03:21, 7.44MB/s]</code></pre>
-</div>
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-<pre><code> 45%|█████████████████████████████████████████████████▏                                                            | 1.21G/2.71G [01:43&lt;02:19, 10.7MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▎                                                            | 1.22G/2.71G [01:43&lt;02:14, 11.2MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▍                                                            | 1.22G/2.71G [01:43&lt;01:37, 15.2MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▌                                                            | 1.22G/2.71G [01:43&lt;01:41, 14.6MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▌                                                            | 1.22G/2.71G [01:43&lt;01:36, 15.4MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▊                                                            | 1.23G/2.71G [01:43&lt;01:15, 19.7MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▊                                                            | 1.23G/2.71G [01:44&lt;01:43, 14.3MB/s]</code></pre>
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-<pre><code> 45%|█████████████████████████████████████████████████▉                                                            | 1.23G/2.71G [01:44&lt;01:26, 17.1MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████                                                            | 1.23G/2.71G [01:44&lt;01:26, 17.1MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▏                                                           | 1.24G/2.71G [01:44&lt;01:54, 12.9MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▏                                                           | 1.24G/2.71G [01:44&lt;01:48, 13.6MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▎                                                           | 1.24G/2.71G [01:44&lt;01:39, 14.8MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▍                                                           | 1.24G/2.71G [01:45&lt;01:48, 13.6MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▍                                                           | 1.24G/2.71G [01:45&lt;02:05, 11.7MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▌                                                           | 1.25G/2.71G [01:45&lt;01:53, 13.0MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▋                                                           | 1.25G/2.71G [01:45&lt;01:54, 12.8MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▋                                                           | 1.25G/2.71G [01:45&lt;02:14, 10.9MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▊                                                           | 1.25G/2.71G [01:45&lt;02:02, 11.9MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▉                                                           | 1.25G/2.71G [01:46&lt;02:02, 11.9MB/s]</code></pre>
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-<pre><code> 46%|██████████████████████████████████████████████████▉                                                           | 1.26G/2.71G [01:46&lt;02:18, 10.5MB/s]</code></pre>
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-<pre><code> 46%|███████████████████████████████████████████████████                                                           | 1.26G/2.71G [01:46&lt;02:00, 12.1MB/s]</code></pre>
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-<pre><code> 46%|███████████████████████████████████████████████████                                                           | 1.26G/2.71G [01:46&lt;01:56, 12.5MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▏                                                          | 1.26G/2.71G [01:46&lt;02:24, 10.0MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▏                                                          | 1.26G/2.71G [01:46&lt;02:01, 11.9MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▎                                                          | 1.27G/2.71G [01:46&lt;01:50, 13.1MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▍                                                          | 1.27G/2.71G [01:47&lt;02:00, 12.0MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▍                                                          | 1.27G/2.71G [01:47&lt;02:09, 11.2MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▌                                                          | 1.27G/2.71G [01:47&lt;01:59, 12.1MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▌                                                          | 1.27G/2.71G [01:47&lt;01:44, 13.8MB/s]</code></pre>
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-<pre><code> 47%|███████████████████████████████████████████████████▉                                                          | 1.28G/2.71G [01:48&lt;02:11, 10.9MB/s]</code></pre>
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-<pre><code> 47%|████████████████████████████████████████████████████▏                                                         | 1.29G/2.71G [01:48&lt;02:14, 10.6MB/s]</code></pre>
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-<pre><code> 48%|████████████████████████████████████████████████████▎                                                         | 1.29G/2.71G [01:49&lt;01:39, 14.3MB/s]</code></pre>
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-<pre><code> 48%|████████████████████████████████████████████████████▍                                                         | 1.29G/2.71G [01:49&lt;02:09, 11.0MB/s]</code></pre>
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-<pre><code> 48%|████████████████████████████████████████████████████▌                                                         | 1.29G/2.71G [01:49&lt;02:02, 11.6MB/s]</code></pre>
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-<pre><code> 48%|████████████████████████████████████████████████████▌                                                         | 1.30G/2.71G [01:49&lt;01:45, 13.4MB/s]</code></pre>
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-<pre><code> 50%|███████████████████████████████████████████████████████▎                                                      | 1.36G/2.71G [01:56&lt;02:01, 11.1MB/s]</code></pre>
-</div>
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-<pre><code> 50%|███████████████████████████████████████████████████████▎                                                      | 1.36G/2.71G [01:56&lt;02:16, 9.90MB/s]</code></pre>
-</div>
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-<pre><code> 50%|███████████████████████████████████████████████████████▍                                                      | 1.37G/2.71G [01:56&lt;02:16, 9.88MB/s]</code></pre>
-</div>
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-<pre><code> 50%|███████████████████████████████████████████████████████▍                                                      | 1.37G/2.71G [01:56&lt;01:54, 11.7MB/s]</code></pre>
-</div>
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-<pre><code> 51%|███████████████████████████████████████████████████████▌                                                      | 1.37G/2.71G [01:56&lt;01:42, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|███████████████████████████████████████████████████████▋                                                      | 1.37G/2.71G [01:57&lt;02:03, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|███████████████████████████████████████████████████████▋                                                      | 1.37G/2.71G [01:57&lt;01:58, 11.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|███████████████████████████████████████████████████████▊                                                      | 1.38G/2.71G [01:57&lt;01:44, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|███████████████████████████████████████████████████████▉                                                      | 1.38G/2.71G [01:57&lt;01:53, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|███████████████████████████████████████████████████████▉                                                      | 1.38G/2.71G [01:57&lt;02:02, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|████████████████████████████████████████████████████████                                                      | 1.38G/2.71G [01:57&lt;01:27, 15.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|████████████████████████████████████████████████████████▏                                                     | 1.38G/2.71G [01:58&lt;01:36, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|████████████████████████████████████████████████████████▏                                                     | 1.39G/2.71G [01:58&lt;01:41, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|████████████████████████████████████████████████████████▍                                                     | 1.39G/2.71G [01:58&lt;01:11, 18.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 51%|████████████████████████████████████████████████████████▌                                                     | 1.40G/2.71G [01:58&lt;01:58, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|████████████████████████████████████████████████████████▊                                                     | 1.40G/2.71G [01:59&lt;01:17, 16.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|████████████████████████████████████████████████████████▉                                                     | 1.40G/2.71G [01:59&lt;02:00, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████                                                     | 1.41G/2.71G [01:59&lt;01:53, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▏                                                    | 1.41G/2.71G [01:59&lt;01:33, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▎                                                    | 1.41G/2.71G [02:00&lt;01:19, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▍                                                    | 1.42G/2.71G [02:00&lt;01:09, 18.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▌                                                    | 1.42G/2.71G [02:00&lt;01:16, 16.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▌                                                    | 1.42G/2.71G [02:00&lt;01:17, 16.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 52%|█████████████████████████████████████████████████████████▋                                                    | 1.42G/2.71G [02:00&lt;01:30, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|█████████████████████████████████████████████████████████▊                                                    | 1.43G/2.71G [02:00&lt;01:28, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|█████████████████████████████████████████████████████████▉                                                    | 1.43G/2.71G [02:01&lt;01:40, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|█████████████████████████████████████████████████████████▉                                                    | 1.43G/2.71G [02:01&lt;01:36, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|██████████████████████████████████████████████████████████                                                    | 1.43G/2.71G [02:01&lt;01:57, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|██████████████████████████████████████████████████████████▏                                                   | 1.43G/2.71G [02:01&lt;01:22, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|██████████████████████████████████████████████████████████▏                                                   | 1.44G/2.71G [02:01&lt;01:25, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|██████████████████████████████████████████████████████████▎                                                   | 1.44G/2.71G [02:01&lt;01:40, 12.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 53%|██████████████████████████████████████████████████████████▍                                                   | 1.44G/2.71G [02:02&lt;01:18, 16.2MB/s]</code></pre>
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-<pre><code> 53%|██████████████████████████████████████████████████████████▌                                                   | 1.44G/2.71G [02:02&lt;01:18, 16.2MB/s]</code></pre>
-</div>
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-<pre><code> 53%|██████████████████████████████████████████████████████████▋                                                   | 1.45G/2.71G [02:02&lt;01:31, 13.8MB/s]</code></pre>
-</div>
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-<pre><code> 53%|██████████████████████████████████████████████████████████▋                                                   | 1.45G/2.71G [02:02&lt;01:25, 14.8MB/s]</code></pre>
-</div>
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-<pre><code> 53%|██████████████████████████████████████████████████████████▊                                                   | 1.45G/2.71G [02:02&lt;01:22, 15.2MB/s]</code></pre>
-</div>
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-<pre><code> 54%|██████████████████████████████████████████████████████████▉                                                   | 1.45G/2.71G [02:02&lt;01:52, 11.2MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████                                                   | 1.45G/2.71G [02:03&lt;01:45, 11.9MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████                                                   | 1.46G/2.71G [02:03&lt;01:39, 12.6MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▏                                                  | 1.46G/2.71G [02:03&lt;01:55, 10.8MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▎                                                  | 1.46G/2.71G [02:03&lt;01:33, 13.4MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▎                                                  | 1.46G/2.71G [02:03&lt;01:46, 11.7MB/s]</code></pre>
-</div>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▍                                                  | 1.47G/2.71G [02:04&lt;01:25, 14.5MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▌                                                  | 1.47G/2.71G [02:04&lt;01:19, 15.7MB/s]</code></pre>
-</div>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▋                                                  | 1.47G/2.71G [02:04&lt;01:11, 17.2MB/s]</code></pre>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▊                                                  | 1.47G/2.71G [02:04&lt;01:39, 12.4MB/s]</code></pre>
-</div>
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-<pre><code> 54%|███████████████████████████████████████████████████████████▉                                                  | 1.48G/2.71G [02:04&lt;01:20, 15.4MB/s]</code></pre>
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-<pre><code> 55%|████████████████████████████████████████████████████████████                                                  | 1.48G/2.71G [02:04&lt;01:15, 16.2MB/s]</code></pre>
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-<pre><code> 55%|████████████████████████████████████████████████████████████                                                  | 1.48G/2.71G [02:05&lt;01:36, 12.7MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▏                                                 | 1.48G/2.71G [02:05&lt;01:35, 12.9MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▏                                                 | 1.48G/2.71G [02:05&lt;01:32, 13.2MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▎                                                 | 1.49G/2.71G [02:05&lt;01:31, 13.4MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▎                                                 | 1.49G/2.71G [02:05&lt;01:58, 10.3MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▍                                                 | 1.49G/2.71G [02:05&lt;01:54, 10.7MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▌                                                 | 1.49G/2.71G [02:05&lt;01:27, 13.9MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▌                                                 | 1.49G/2.71G [02:06&lt;01:41, 12.0MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▋                                                 | 1.50G/2.71G [02:06&lt;01:34, 12.9MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▊                                                 | 1.50G/2.71G [02:06&lt;01:12, 16.8MB/s]</code></pre>
-</div>
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-<pre><code> 55%|████████████████████████████████████████████████████████████▉                                                 | 1.50G/2.71G [02:06&lt;01:37, 12.5MB/s]</code></pre>
-</div>
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-<pre><code> 55%|█████████████████████████████████████████████████████████████                                                 | 1.50G/2.71G [02:06&lt;01:24, 14.3MB/s]</code></pre>
-</div>
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-<pre><code> 56%|█████████████████████████████████████████████████████████████▏                                                | 1.51G/2.71G [02:06&lt;01:08, 17.5MB/s]</code></pre>
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-<pre><code> 56%|█████████████████████████████████████████████████████████████▏                                                | 1.51G/2.71G [02:07&lt;01:13, 16.3MB/s]</code></pre>
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-</div>
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-<pre><code> 56%|█████████████████████████████████████████████████████████████▍                                                | 1.51G/2.71G [02:07&lt;01:25, 14.0MB/s]</code></pre>
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-<pre><code> 56%|█████████████████████████████████████████████████████████████▌                                                | 1.52G/2.71G [02:08&lt;02:51, 6.97MB/s]</code></pre>
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-<pre><code> 56%|█████████████████████████████████████████████████████████████▋                                                | 1.52G/2.71G [02:08&lt;02:04, 9.52MB/s]</code></pre>
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-<pre><code> 57%|██████████████████████████████████████████████████████████████▎                                               | 1.54G/2.71G [02:09&lt;01:20, 14.5MB/s]</code></pre>
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-<pre><code> 57%|██████████████████████████████████████████████████████████████▌                                               | 1.54G/2.71G [02:09&lt;01:21, 14.4MB/s]</code></pre>
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-<pre><code> 57%|██████████████████████████████████████████████████████████████▌                                               | 1.54G/2.71G [02:09&lt;01:19, 14.6MB/s]</code></pre>
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-<pre><code> 57%|██████████████████████████████████████████████████████████████▊                                               | 1.55G/2.71G [02:10&lt;01:23, 14.0MB/s]</code></pre>
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-<pre><code> 57%|██████████████████████████████████████████████████████████████▉                                               | 1.55G/2.71G [02:10&lt;01:18, 14.8MB/s]</code></pre>
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-<pre><code> 57%|███████████████████████████████████████████████████████████████                                               | 1.55G/2.71G [02:10&lt;01:38, 11.8MB/s]</code></pre>
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-<pre><code> 57%|███████████████████████████████████████████████████████████████▏                                              | 1.56G/2.71G [02:10&lt;01:24, 13.7MB/s]</code></pre>
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-<pre><code> 57%|███████████████████████████████████████████████████████████████▏                                              | 1.56G/2.71G [02:11&lt;01:58, 9.70MB/s]</code></pre>
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-<pre><code> 58%|███████████████████████████████████████████████████████████████▍                                              | 1.56G/2.71G [02:11&lt;01:16, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▌                                              | 1.57G/2.71G [02:11&lt;01:21, 14.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▌                                              | 1.57G/2.71G [02:11&lt;01:32, 12.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▋                                              | 1.57G/2.71G [02:12&lt;01:35, 12.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▊                                              | 1.57G/2.71G [02:12&lt;02:02, 9.31MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▊                                              | 1.57G/2.71G [02:12&lt;01:53, 10.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▉                                              | 1.58G/2.71G [02:12&lt;01:44, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|███████████████████████████████████████████████████████████████▉                                              | 1.58G/2.71G [02:12&lt;02:06, 9.01MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████                                              | 1.58G/2.71G [02:13&lt;01:52, 10.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████                                              | 1.58G/2.71G [02:13&lt;01:45, 10.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████▏                                             | 1.58G/2.71G [02:13&lt;02:27, 7.69MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████▏                                             | 1.58G/2.71G [02:13&lt;02:22, 7.92MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████▎                                             | 1.58G/2.71G [02:13&lt;02:03, 9.15MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 58%|████████████████████████████████████████████████████████████████▎                                             | 1.59G/2.71G [02:13&lt;01:48, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▍                                             | 1.59G/2.71G [02:14&lt;01:53, 9.87MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▌                                             | 1.59G/2.71G [02:14&lt;01:17, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▌                                             | 1.59G/2.71G [02:14&lt;01:14, 15.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▋                                             | 1.59G/2.71G [02:14&lt;01:38, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▊                                             | 1.60G/2.71G [02:14&lt;01:24, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|████████████████████████████████████████████████████████████████▉                                             | 1.60G/2.71G [02:14&lt;01:06, 16.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|█████████████████████████████████████████████████████████████████                                             | 1.60G/2.71G [02:15&lt;01:16, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|█████████████████████████████████████████████████████████████████▏                                            | 1.61G/2.71G [02:15&lt;01:06, 16.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|█████████████████████████████████████████████████████████████████▏                                            | 1.61G/2.71G [02:15&lt;01:03, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|█████████████████████████████████████████████████████████████████▎                                            | 1.61G/2.71G [02:15&lt;01:22, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 59%|█████████████████████████████████████████████████████████████████▍                                            | 1.61G/2.71G [02:15&lt;01:10, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▌                                            | 1.62G/2.71G [02:15&lt;01:23, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▌                                            | 1.62G/2.71G [02:16&lt;02:00, 9.07MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▋                                            | 1.62G/2.71G [02:16&lt;01:57, 9.31MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▋                                            | 1.62G/2.71G [02:16&lt;01:50, 9.86MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▊                                            | 1.62G/2.71G [02:16&lt;02:06, 8.60MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|█████████████████████████████████████████████████████████████████▉                                            | 1.63G/2.71G [02:17&lt;02:22, 7.62MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████                                            | 1.63G/2.71G [02:17&lt;02:46, 6.51MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▏                                           | 1.63G/2.71G [02:17&lt;02:10, 8.26MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▏                                           | 1.63G/2.71G [02:18&lt;02:40, 6.73MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▎                                           | 1.64G/2.71G [02:18&lt;02:58, 6.04MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▍                                           | 1.64G/2.71G [02:18&lt;02:33, 7.01MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▍                                           | 1.64G/2.71G [02:19&lt;03:22, 5.30MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 60%|██████████████████████████████████████████████████████████████████▌                                           | 1.64G/2.71G [02:19&lt;03:31, 5.07MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|██████████████████████████████████████████████████████████████████▋                                           | 1.64G/2.71G [02:20&lt;02:13, 8.01MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|██████████████████████████████████████████████████████████████████▋                                           | 1.65G/2.71G [02:20&lt;02:43, 6.54MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|██████████████████████████████████████████████████████████████████▊                                           | 1.65G/2.71G [02:21&lt;03:35, 4.95MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|██████████████████████████████████████████████████████████████████▉                                           | 1.65G/2.71G [02:21&lt;02:59, 5.91MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|███████████████████████████████████████████████████████████████████                                           | 1.65G/2.71G [02:22&lt;03:07, 5.64MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|███████████████████████████████████████████████████████████████████▏                                          | 1.66G/2.71G [02:22&lt;02:09, 8.12MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|███████████████████████████████████████████████████████████████████▎                                          | 1.66G/2.71G [02:22&lt;02:36, 6.73MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|███████████████████████████████████████████████████████████████████▍                                          | 1.66G/2.71G [02:22&lt;02:28, 7.05MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 61%|███████████████████████████████████████████████████████████████████▌                                          | 1.67G/2.71G [02:23&lt;01:45, 9.90MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|███████████████████████████████████████████████████████████████████▋                                          | 1.67G/2.71G [02:23&lt;01:18, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|███████████████████████████████████████████████████████████████████▊                                          | 1.67G/2.71G [02:23&lt;01:29, 11.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|███████████████████████████████████████████████████████████████████▉                                          | 1.67G/2.71G [02:23&lt;01:22, 12.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|███████████████████████████████████████████████████████████████████▉                                          | 1.68G/2.71G [02:23&lt;01:49, 9.42MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▏                                         | 1.68G/2.71G [02:24&lt;01:08, 15.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▎                                         | 1.68G/2.71G [02:24&lt;01:30, 11.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▍                                         | 1.69G/2.71G [02:24&lt;01:49, 9.38MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▌                                         | 1.69G/2.71G [02:25&lt;01:30, 11.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▌                                         | 1.69G/2.71G [02:25&lt;02:01, 8.43MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 62%|████████████████████████████████████████████████████████████████████▋                                         | 1.70G/2.71G [02:25&lt;01:32, 11.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|████████████████████████████████████████████████████████████████████▉                                         | 1.70G/2.71G [02:26&lt;01:37, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████                                         | 1.70G/2.71G [02:26&lt;01:18, 12.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████                                         | 1.70G/2.71G [02:26&lt;01:11, 14.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▏                                        | 1.71G/2.71G [02:26&lt;01:31, 11.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▎                                        | 1.71G/2.71G [02:26&lt;01:42, 9.83MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▎                                        | 1.71G/2.71G [02:27&lt;01:57, 8.56MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▍                                        | 1.71G/2.71G [02:27&lt;01:53, 8.85MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▍                                        | 1.71G/2.71G [02:27&lt;01:50, 9.02MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▌                                        | 1.71G/2.71G [02:27&lt;01:48, 9.22MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▋                                        | 1.72G/2.71G [02:27&lt;01:11, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 63%|█████████████████████████████████████████████████████████████████████▊                                        | 1.72G/2.71G [02:28&lt;01:46, 9.34MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|█████████████████████████████████████████████████████████████████████▊                                        | 1.72G/2.71G [02:28&lt;01:28, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|█████████████████████████████████████████████████████████████████████▉                                        | 1.73G/2.71G [02:28&lt;01:44, 9.43MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▏                                       | 1.73G/2.71G [02:28&lt;01:11, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▏                                       | 1.73G/2.71G [02:28&lt;01:07, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▎                                       | 1.73G/2.71G [02:29&lt;01:25, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▍                                       | 1.74G/2.71G [02:29&lt;01:21, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▌                                       | 1.74G/2.71G [02:29&lt;01:00, 16.0MB/s]</code></pre>
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-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▋                                       | 1.74G/2.71G [02:29&lt;01:17, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▋                                       | 1.74G/2.71G [02:29&lt;01:17, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▊                                       | 1.75G/2.71G [02:30&lt;01:37, 9.91MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 64%|██████████████████████████████████████████████████████████████████████▉                                       | 1.75G/2.71G [02:30&lt;01:28, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|██████████████████████████████████████████████████████████████████████▉                                       | 1.75G/2.71G [02:30&lt;01:28, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████                                       | 1.75G/2.71G [02:30&lt;01:42, 9.38MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████                                       | 1.75G/2.71G [02:30&lt;01:32, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▏                                      | 1.75G/2.71G [02:30&lt;01:24, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▏                                      | 1.76G/2.71G [02:31&lt;01:54, 8.34MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▎                                      | 1.76G/2.71G [02:31&lt;01:39, 9.58MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▎                                      | 1.76G/2.71G [02:31&lt;01:20, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▍                                      | 1.76G/2.71G [02:31&lt;01:09, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▌                                      | 1.76G/2.71G [02:31&lt;01:27, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▌                                      | 1.77G/2.71G [02:32&lt;01:22, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▊                                      | 1.77G/2.71G [02:32&lt;01:16, 12.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|███████████████████████████████████████████████████████████████████████▉                                      | 1.77G/2.71G [02:32&lt;01:02, 15.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 65%|████████████████████████████████████████████████████████████████████████                                      | 1.78G/2.71G [02:32&lt;00:54, 17.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████                                      | 1.78G/2.71G [02:32&lt;01:10, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▏                                     | 1.78G/2.71G [02:32&lt;00:57, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▎                                     | 1.78G/2.71G [02:33&lt;00:54, 17.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▍                                     | 1.79G/2.71G [02:33&lt;00:59, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▌                                     | 1.79G/2.71G [02:33&lt;00:59, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▋                                     | 1.79G/2.71G [02:33&lt;00:46, 19.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▊                                     | 1.79G/2.71G [02:33&lt;00:51, 17.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▊                                     | 1.80G/2.71G [02:33&lt;01:09, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|████████████████████████████████████████████████████████████████████████▉                                     | 1.80G/2.71G [02:34&lt;01:01, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 66%|█████████████████████████████████████████████████████████████████████████▏                                    | 1.80G/2.71G [02:34&lt;00:51, 17.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▏                                    | 1.81G/2.71G [02:34&lt;00:53, 16.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▎                                    | 1.81G/2.71G [02:34&lt;00:51, 17.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▍                                    | 1.81G/2.71G [02:34&lt;00:50, 17.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▌                                    | 1.81G/2.71G [02:34&lt;01:10, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▌                                    | 1.82G/2.71G [02:35&lt;01:05, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▋                                    | 1.82G/2.71G [02:35&lt;01:26, 10.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▊                                    | 1.82G/2.71G [02:35&lt;01:24, 10.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▉                                    | 1.82G/2.71G [02:35&lt;01:03, 14.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|█████████████████████████████████████████████████████████████████████████▉                                    | 1.82G/2.71G [02:35&lt;01:18, 11.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|██████████████████████████████████████████████████████████████████████████                                    | 1.83G/2.71G [02:36&lt;01:00, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 67%|██████████████████████████████████████████████████████████████████████████▏                                   | 1.83G/2.71G [02:36&lt;00:49, 17.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|██████████████████████████████████████████████████████████████████████████▎                                   | 1.83G/2.71G [02:36&lt;01:02, 14.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|██████████████████████████████████████████████████████████████████████████▍                                   | 1.84G/2.71G [02:36&lt;00:54, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|██████████████████████████████████████████████████████████████████████████▋                                   | 1.84G/2.71G [02:36&lt;00:39, 21.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|██████████████████████████████████████████████████████████████████████████▊                                   | 1.84G/2.71G [02:36&lt;00:41, 20.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|██████████████████████████████████████████████████████████████████████████▉                                   | 1.85G/2.71G [02:37&lt;00:50, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|███████████████████████████████████████████████████████████████████████████                                   | 1.85G/2.71G [02:37&lt;00:41, 21.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|███████████████████████████████████████████████████████████████████████████▏                                  | 1.85G/2.71G [02:37&lt;00:38, 22.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|███████████████████████████████████████████████████████████████████████████▏                                  | 1.86G/2.71G [02:37&lt;00:55, 15.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 68%|███████████████████████████████████████████████████████████████████████████▎                                  | 1.86G/2.71G [02:37&lt;00:57, 15.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▍                                  | 1.86G/2.71G [02:38&lt;01:12, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▍                                  | 1.86G/2.71G [02:38&lt;01:14, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▌                                  | 1.86G/2.71G [02:38&lt;01:13, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▌                                  | 1.86G/2.71G [02:38&lt;01:10, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▋                                  | 1.87G/2.71G [02:38&lt;01:11, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▊                                  | 1.87G/2.71G [02:38&lt;01:01, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▉                                  | 1.87G/2.71G [02:38&lt;00:48, 17.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|███████████████████████████████████████████████████████████████████████████▉                                  | 1.87G/2.71G [02:39&lt;01:04, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|████████████████████████████████████████████████████████████████████████████                                  | 1.88G/2.71G [02:39&lt;00:57, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|████████████████████████████████████████████████████████████████████████████▏                                 | 1.88G/2.71G [02:39&lt;00:57, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|████████████████████████████████████████████████████████████████████████████▎                                 | 1.88G/2.71G [02:39&lt;01:01, 13.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 69%|████████████████████████████████████████████████████████████████████████████▍                                 | 1.88G/2.71G [02:39&lt;00:52, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▌                                 | 1.89G/2.71G [02:39&lt;00:53, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▌                                 | 1.89G/2.71G [02:40&lt;01:02, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▋                                 | 1.89G/2.71G [02:40&lt;01:03, 12.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▊                                 | 1.89G/2.71G [02:40&lt;00:57, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▊                                 | 1.89G/2.71G [02:40&lt;01:44, 7.86MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▉                                 | 1.90G/2.71G [02:41&lt;02:32, 5.36MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|████████████████████████████████████████████████████████████████████████████▉                                 | 1.90G/2.71G [02:41&lt;02:03, 6.61MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|█████████████████████████████████████████████████████████████████████████████                                 | 1.90G/2.71G [02:41&lt;01:57, 6.94MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|█████████████████████████████████████████████████████████████████████████████▏                                | 1.90G/2.71G [02:41&lt;01:15, 10.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|█████████████████████████████████████████████████████████████████████████████▎                                | 1.91G/2.71G [02:41&lt;00:57, 14.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|█████████████████████████████████████████████████████████████████████████████▍                                | 1.91G/2.71G [02:42&lt;00:47, 17.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 70%|█████████████████████████████████████████████████████████████████████████████▌                                | 1.91G/2.71G [02:42&lt;01:02, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|█████████████████████████████████████████████████████████████████████████████▌                                | 1.91G/2.71G [02:42&lt;00:55, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|█████████████████████████████████████████████████████████████████████████████▋                                | 1.92G/2.71G [02:42&lt;00:50, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|█████████████████████████████████████████████████████████████████████████████▊                                | 1.92G/2.71G [02:42&lt;00:50, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|█████████████████████████████████████████████████████████████████████████████▉                                | 1.92G/2.71G [02:42&lt;00:44, 17.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████                                | 1.92G/2.71G [02:43&lt;00:45, 17.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████                                | 1.93G/2.71G [02:43&lt;00:59, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▏                               | 1.93G/2.71G [02:43&lt;01:00, 12.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▏                               | 1.93G/2.71G [02:43&lt;01:00, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▎                               | 1.93G/2.71G [02:43&lt;01:21, 9.62MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▎                               | 1.93G/2.71G [02:43&lt;01:09, 11.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▍                               | 1.94G/2.71G [02:44&lt;00:55, 14.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 71%|██████████████████████████████████████████████████████████████████████████████▋                               | 1.94G/2.71G [02:44&lt;00:56, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|██████████████████████████████████████████████████████████████████████████████▊                               | 1.94G/2.71G [02:44&lt;00:45, 16.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|██████████████████████████████████████████████████████████████████████████████▊                               | 1.94G/2.71G [02:44&lt;00:48, 16.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|██████████████████████████████████████████████████████████████████████████████▉                               | 1.95G/2.71G [02:44&lt;00:48, 15.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████                               | 1.95G/2.71G [02:44&lt;00:55, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████                               | 1.95G/2.71G [02:45&lt;00:50, 15.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▏                              | 1.95G/2.71G [02:45&lt;00:48, 15.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▎                              | 1.96G/2.71G [02:45&lt;01:01, 12.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▎                              | 1.96G/2.71G [02:45&lt;00:58, 12.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▍                              | 1.96G/2.71G [02:45&lt;01:04, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▍                              | 1.96G/2.71G [02:45&lt;01:09, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▌                              | 1.96G/2.71G [02:46&lt;01:08, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▋                              | 1.96G/2.71G [02:46&lt;00:59, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 72%|███████████████████████████████████████████████████████████████████████████████▋                              | 1.97G/2.71G [02:46&lt;01:11, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|███████████████████████████████████████████████████████████████████████████████▊                              | 1.97G/2.71G [02:46&lt;00:57, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|███████████████████████████████████████████████████████████████████████████████▉                              | 1.97G/2.71G [02:46&lt;00:49, 15.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████                              | 1.97G/2.71G [02:47&lt;01:04, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████                              | 1.97G/2.71G [02:47&lt;00:57, 12.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▏                             | 1.98G/2.71G [02:47&lt;00:52, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▏                             | 1.98G/2.71G [02:47&lt;00:53, 13.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▎                             | 1.98G/2.71G [02:47&lt;01:04, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▎                             | 1.98G/2.71G [02:47&lt;01:00, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▍                             | 1.98G/2.71G [02:47&lt;00:54, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▌                             | 1.99G/2.71G [02:48&lt;01:14, 9.77MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▌                             | 1.99G/2.71G [02:48&lt;01:21, 8.87MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▋                             | 1.99G/2.71G [02:48&lt;01:12, 10.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▋                             | 1.99G/2.71G [02:48&lt;01:18, 9.21MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 73%|████████████████████████████████████████████████████████████████████████████████▊                             | 1.99G/2.71G [02:48&lt;01:02, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|████████████████████████████████████████████████████████████████████████████████▊                             | 1.99G/2.71G [02:48&lt;01:01, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|████████████████████████████████████████████████████████████████████████████████▉                             | 2.00G/2.71G [02:48&lt;01:02, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|████████████████████████████████████████████████████████████████████████████████▉                             | 2.00G/2.71G [02:49&lt;01:02, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████                             | 2.00G/2.71G [02:49&lt;00:56, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▏                            | 2.00G/2.71G [02:49&lt;00:53, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▏                            | 2.00G/2.71G [02:49&lt;01:35, 7.43MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▎                            | 2.00G/2.71G [02:50&lt;01:53, 6.21MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▍                            | 2.01G/2.71G [02:50&lt;01:30, 7.80MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▍                            | 2.01G/2.71G [02:50&lt;01:33, 7.55MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▌                            | 2.01G/2.71G [02:50&lt;01:13, 9.54MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▋                            | 2.01G/2.71G [02:50&lt;00:57, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▊                            | 2.02G/2.71G [02:51&lt;01:09, 10.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▊                            | 2.02G/2.71G [02:51&lt;01:04, 10.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 74%|█████████████████████████████████████████████████████████████████████████████████▉                            | 2.02G/2.71G [02:51&lt;01:00, 11.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|█████████████████████████████████████████████████████████████████████████████████▉                            | 2.02G/2.71G [02:51&lt;01:13, 9.37MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████                            | 2.02G/2.71G [02:51&lt;01:06, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████                            | 2.02G/2.71G [02:51&lt;01:01, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▏                           | 2.03G/2.71G [02:52&lt;01:16, 8.98MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▎                           | 2.03G/2.71G [02:52&lt;00:57, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▎                           | 2.03G/2.71G [02:52&lt;00:59, 11.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▍                           | 2.03G/2.71G [02:52&lt;01:11, 9.55MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▍                           | 2.03G/2.71G [02:52&lt;01:06, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▌                           | 2.03G/2.71G [02:53&lt;01:06, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▌                           | 2.04G/2.71G [02:53&lt;01:46, 6.37MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▋                           | 2.04G/2.71G [02:53&lt;01:31, 7.40MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▋                           | 2.04G/2.71G [02:53&lt;01:42, 6.59MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▊                           | 2.04G/2.71G [02:54&lt;01:35, 7.06MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▊                           | 2.04G/2.71G [02:54&lt;01:34, 7.08MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▊                           | 2.04G/2.71G [02:54&lt;01:39, 6.76MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▉                           | 2.04G/2.71G [02:54&lt;01:36, 6.95MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|██████████████████████████████████████████████████████████████████████████████████▉                           | 2.05G/2.71G [02:54&lt;01:11, 9.33MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|███████████████████████████████████████████████████████████████████████████████████                           | 2.05G/2.71G [02:54&lt;01:36, 6.89MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 75%|███████████████████████████████████████████████████████████████████████████████████                           | 2.05G/2.71G [02:55&lt;01:28, 7.48MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████                           | 2.05G/2.71G [02:55&lt;01:24, 7.88MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▏                          | 2.05G/2.71G [02:55&lt;01:43, 6.42MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▏                          | 2.05G/2.71G [02:55&lt;01:25, 7.73MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▎                          | 2.05G/2.71G [02:55&lt;01:12, 9.08MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▍                          | 2.06G/2.71G [02:55&lt;01:04, 10.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▌                          | 2.06G/2.71G [02:56&lt;00:46, 14.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▋                          | 2.06G/2.71G [02:56&lt;00:39, 16.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▊                          | 2.07G/2.71G [02:56&lt;00:39, 16.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▊                          | 2.07G/2.71G [02:56&lt;00:39, 16.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|███████████████████████████████████████████████████████████████████████████████████▉                          | 2.07G/2.71G [02:56&lt;00:36, 17.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 76%|████████████████████████████████████████████████████████████████████████████████████                          | 2.07G/2.71G [02:56&lt;00:36, 17.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▏                         | 2.08G/2.71G [02:56&lt;00:36, 17.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▍                         | 2.08G/2.71G [02:57&lt;00:31, 20.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▌                         | 2.08G/2.71G [02:57&lt;00:34, 18.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▌                         | 2.09G/2.71G [02:57&lt;00:36, 17.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▋                         | 2.09G/2.71G [02:57&lt;00:34, 17.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▊                         | 2.09G/2.71G [02:57&lt;00:34, 18.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▉                         | 2.09G/2.71G [02:58&lt;00:43, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|████████████████████████████████████████████████████████████████████████████████████▉                         | 2.10G/2.71G [02:58&lt;00:42, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|█████████████████████████████████████████████████████████████████████████████████████                         | 2.10G/2.71G [02:58&lt;00:43, 14.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|█████████████████████████████████████████████████████████████████████████████████████▏                        | 2.10G/2.71G [02:58&lt;00:42, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 77%|█████████████████████████████████████████████████████████████████████████████████████▏                        | 2.10G/2.71G [02:58&lt;00:47, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▎                        | 2.10G/2.71G [02:58&lt;00:57, 10.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▍                        | 2.11G/2.71G [02:59&lt;01:09, 8.75MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▌                        | 2.11G/2.71G [02:59&lt;01:06, 9.03MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▌                        | 2.11G/2.71G [02:59&lt;00:57, 10.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▊                        | 2.12G/2.71G [02:59&lt;00:40, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|█████████████████████████████████████████████████████████████████████████████████████▉                        | 2.12G/2.71G [02:59&lt;00:38, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|██████████████████████████████████████████████████████████████████████████████████████                        | 2.12G/2.71G [03:00&lt;00:34, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|██████████████████████████████████████████████████████████████████████████████████████▏                       | 2.12G/2.71G [03:00&lt;00:30, 19.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 78%|██████████████████████████████████████████████████████████████████████████████████████▎                       | 2.13G/2.71G [03:00&lt;00:36, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▎                       | 2.13G/2.71G [03:00&lt;00:36, 15.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▍                       | 2.13G/2.71G [03:00&lt;00:34, 16.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▌                       | 2.13G/2.71G [03:00&lt;00:30, 18.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▋                       | 2.14G/2.71G [03:00&lt;00:27, 20.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▊                       | 2.14G/2.71G [03:01&lt;00:33, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|██████████████████████████████████████████████████████████████████████████████████████▉                       | 2.14G/2.71G [03:01&lt;00:28, 20.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|███████████████████████████████████████████████████████████████████████████████████████                       | 2.15G/2.71G [03:01&lt;00:24, 22.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|███████████████████████████████████████████████████████████████████████████████████████▏                      | 2.15G/2.71G [03:01&lt;00:40, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 79%|███████████████████████████████████████████████████████████████████████████████████████▎                      | 2.15G/2.71G [03:01&lt;00:32, 17.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|███████████████████████████████████████████████████████████████████████████████████████▌                      | 2.16G/2.71G [03:02&lt;00:30, 18.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|███████████████████████████████████████████████████████████████████████████████████████▋                      | 2.16G/2.71G [03:02&lt;00:24, 22.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|███████████████████████████████████████████████████████████████████████████████████████▉                      | 2.17G/2.71G [03:02&lt;00:22, 23.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|████████████████████████████████████████████████████████████████████████████████████████                      | 2.17G/2.71G [03:02&lt;00:33, 16.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|████████████████████████████████████████████████████████████████████████████████████████▏                     | 2.17G/2.71G [03:03&lt;00:33, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|████████████████████████████████████████████████████████████████████████████████████████▎                     | 2.18G/2.71G [03:03&lt;00:39, 13.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|████████████████████████████████████████████████████████████████████████████████████████▍                     | 2.18G/2.71G [03:03&lt;00:39, 13.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 80%|████████████████████████████████████████████████████████████████████████████████████████▌                     | 2.18G/2.71G [03:03&lt;00:38, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|████████████████████████████████████████████████████████████████████████████████████████▋                     | 2.19G/2.71G [03:03&lt;00:35, 14.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|████████████████████████████████████████████████████████████████████████████████████████▉                     | 2.19G/2.71G [03:04&lt;00:23, 21.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████                     | 2.19G/2.71G [03:04&lt;00:27, 18.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████▏                    | 2.20G/2.71G [03:04&lt;00:25, 20.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████▎                    | 2.20G/2.71G [03:04&lt;00:24, 20.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████▎                    | 2.20G/2.71G [03:04&lt;00:25, 20.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████▍                    | 2.21G/2.71G [03:04&lt;00:28, 17.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 81%|█████████████████████████████████████████████████████████████████████████████████████████▌                    | 2.21G/2.71G [03:05&lt;00:28, 18.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|█████████████████████████████████████████████████████████████████████████████████████████▋                    | 2.21G/2.71G [03:05&lt;00:25, 19.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|█████████████████████████████████████████████████████████████████████████████████████████▊                    | 2.21G/2.71G [03:05&lt;00:33, 15.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|█████████████████████████████████████████████████████████████████████████████████████████▉                    | 2.22G/2.71G [03:05&lt;00:27, 18.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|█████████████████████████████████████████████████████████████████████████████████████████▉                    | 2.22G/2.71G [03:05&lt;00:26, 18.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████                    | 2.22G/2.71G [03:05&lt;00:36, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████▏                   | 2.22G/2.71G [03:05&lt;00:31, 15.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████▎                   | 2.23G/2.71G [03:06&lt;00:27, 17.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████▍                   | 2.23G/2.71G [03:06&lt;00:32, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████▌                   | 2.23G/2.71G [03:06&lt;00:30, 15.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 82%|██████████████████████████████████████████████████████████████████████████████████████████▋                   | 2.24G/2.71G [03:06&lt;00:34, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|██████████████████████████████████████████████████████████████████████████████████████████▊                   | 2.24G/2.71G [03:06&lt;00:27, 16.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|██████████████████████████████████████████████████████████████████████████████████████████▉                   | 2.24G/2.71G [03:07&lt;00:22, 21.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████                   | 2.25G/2.71G [03:07&lt;00:21, 21.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▏                  | 2.25G/2.71G [03:07&lt;00:27, 16.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▎                  | 2.25G/2.71G [03:07&lt;00:28, 16.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▍                  | 2.25G/2.71G [03:08&lt;00:44, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▍                  | 2.26G/2.71G [03:08&lt;00:42, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▌                  | 2.26G/2.71G [03:08&lt;00:55, 8.24MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▋                  | 2.26G/2.71G [03:08&lt;00:42, 10.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 83%|███████████████████████████████████████████████████████████████████████████████████████████▊                  | 2.26G/2.71G [03:08&lt;00:37, 12.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|███████████████████████████████████████████████████████████████████████████████████████████▊                  | 2.26G/2.71G [03:09&lt;00:34, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|███████████████████████████████████████████████████████████████████████████████████████████▉                  | 2.27G/2.71G [03:09&lt;00:28, 15.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████                  | 2.27G/2.71G [03:09&lt;00:28, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████▏                 | 2.27G/2.71G [03:09&lt;00:28, 15.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████▍                 | 2.28G/2.71G [03:09&lt;00:21, 19.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████▋                 | 2.28G/2.71G [03:09&lt;00:17, 25.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████▊                 | 2.29G/2.71G [03:10&lt;00:21, 20.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 84%|████████████████████████████████████████████████████████████████████████████████████████████▉                 | 2.29G/2.71G [03:10&lt;00:19, 21.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████                 | 2.29G/2.71G [03:10&lt;00:17, 23.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▏                | 2.30G/2.71G [03:10&lt;00:22, 18.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▎                | 2.30G/2.71G [03:10&lt;00:23, 17.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▍                | 2.30G/2.71G [03:10&lt;00:20, 19.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▌                | 2.31G/2.71G [03:11&lt;00:28, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▌                | 2.31G/2.71G [03:11&lt;00:27, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▋                | 2.31G/2.71G [03:11&lt;00:26, 15.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▊                | 2.31G/2.71G [03:11&lt;00:29, 13.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 85%|█████████████████████████████████████████████████████████████████████████████████████████████▉                | 2.32G/2.71G [03:11&lt;00:21, 18.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████                | 2.32G/2.71G [03:12&lt;00:23, 16.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▏               | 2.32G/2.71G [03:12&lt;00:21, 17.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▎               | 2.33G/2.71G [03:12&lt;00:21, 17.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▍               | 2.33G/2.71G [03:12&lt;00:17, 21.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▌               | 2.33G/2.71G [03:12&lt;00:18, 21.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▋               | 2.33G/2.71G [03:12&lt;00:23, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▊               | 2.34G/2.71G [03:12&lt;00:17, 21.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|██████████████████████████████████████████████████████████████████████████████████████████████▉               | 2.34G/2.71G [03:13&lt;00:25, 14.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|███████████████████████████████████████████████████████████████████████████████████████████████               | 2.34G/2.71G [03:13&lt;00:25, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 86%|███████████████████████████████████████████████████████████████████████████████████████████████               | 2.35G/2.71G [03:13&lt;00:25, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▏              | 2.35G/2.71G [03:13&lt;00:28, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▎              | 2.35G/2.71G [03:13&lt;00:34, 10.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▎              | 2.35G/2.71G [03:14&lt;00:42, 8.60MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▍              | 2.35G/2.71G [03:14&lt;00:32, 10.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▌              | 2.36G/2.71G [03:14&lt;00:28, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▌              | 2.36G/2.71G [03:14&lt;00:24, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▋              | 2.36G/2.71G [03:14&lt;00:29, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▊              | 2.36G/2.71G [03:15&lt;00:26, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▉              | 2.37G/2.71G [03:15&lt;00:23, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|███████████████████████████████████████████████████████████████████████████████████████████████▉              | 2.37G/2.71G [03:15&lt;00:26, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|████████████████████████████████████████████████████████████████████████████████████████████████              | 2.37G/2.71G [03:15&lt;00:26, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 87%|████████████████████████████████████████████████████████████████████████████████████████████████▏             | 2.37G/2.71G [03:15&lt;00:30, 11.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▎             | 2.38G/2.71G [03:16&lt;00:26, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▍             | 2.38G/2.71G [03:16&lt;00:25, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▍             | 2.38G/2.71G [03:16&lt;00:26, 12.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▌             | 2.38G/2.71G [03:16&lt;00:26, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▌             | 2.38G/2.71G [03:16&lt;00:26, 12.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▋             | 2.38G/2.71G [03:17&lt;00:40, 8.12MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|████████████████████████████████████████████████████████████████████████████████████████████████▊             | 2.39G/2.71G [03:17&lt;00:41, 7.72MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|█████████████████████████████████████████████████████████████████████████████████████████████████             | 2.39G/2.71G [03:17&lt;00:27, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|█████████████████████████████████████████████████████████████████████████████████████████████████▏            | 2.39G/2.71G [03:17&lt;00:27, 11.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 88%|█████████████████████████████████████████████████████████████████████████████████████████████████▎            | 2.40G/2.71G [03:18&lt;00:20, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▍            | 2.40G/2.71G [03:18&lt;00:18, 16.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▌            | 2.40G/2.71G [03:18&lt;00:21, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▋            | 2.41G/2.71G [03:18&lt;00:16, 18.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▊            | 2.41G/2.71G [03:18&lt;00:15, 19.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|█████████████████████████████████████████████████████████████████████████████████████████████████▉            | 2.42G/2.71G [03:18&lt;00:17, 17.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|██████████████████████████████████████████████████████████████████████████████████████████████████            | 2.42G/2.71G [03:19&lt;00:16, 18.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|██████████████████████████████████████████████████████████████████████████████████████████████████▏           | 2.42G/2.71G [03:19&lt;00:14, 20.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|██████████████████████████████████████████████████████████████████████████████████████████████████▎           | 2.42G/2.71G [03:19&lt;00:18, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 89%|██████████████████████████████████████████████████████████████████████████████████████████████████▍           | 2.43G/2.71G [03:19&lt;00:16, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████▌           | 2.43G/2.71G [03:19&lt;00:16, 17.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████▋           | 2.43G/2.71G [03:19&lt;00:14, 19.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████▋           | 2.43G/2.71G [03:20&lt;00:18, 14.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████▊           | 2.44G/2.71G [03:20&lt;00:16, 16.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████▉           | 2.44G/2.71G [03:20&lt;00:17, 15.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|███████████████████████████████████████████████████████████████████████████████████████████████████           | 2.44G/2.71G [03:20&lt;00:19, 14.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|███████████████████████████████████████████████████████████████████████████████████████████████████▏          | 2.45G/2.71G [03:20&lt;00:16, 16.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|███████████████████████████████████████████████████████████████████████████████████████████████████▎          | 2.45G/2.71G [03:20&lt;00:12, 21.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 90%|███████████████████████████████████████████████████████████████████████████████████████████████████▍          | 2.45G/2.71G [03:21&lt;00:16, 16.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|███████████████████████████████████████████████████████████████████████████████████████████████████▋          | 2.46G/2.71G [03:21&lt;00:11, 21.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|███████████████████████████████████████████████████████████████████████████████████████████████████▊          | 2.46G/2.71G [03:21&lt;00:11, 22.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|███████████████████████████████████████████████████████████████████████████████████████████████████▉          | 2.46G/2.71G [03:21&lt;00:20, 12.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|███████████████████████████████████████████████████████████████████████████████████████████████████▉          | 2.47G/2.71G [03:22&lt;00:21, 11.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|████████████████████████████████████████████████████████████████████████████████████████████████████          | 2.47G/2.71G [03:22&lt;00:18, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|████████████████████████████████████████████████████████████████████████████████████████████████████▏         | 2.47G/2.71G [03:22&lt;00:16, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|████████████████████████████████████████████████████████████████████████████████████████████████████▎         | 2.47G/2.71G [03:22&lt;00:17, 13.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|████████████████████████████████████████████████████████████████████████████████████████████████████▍         | 2.48G/2.71G [03:22&lt;00:16, 14.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 91%|████████████████████████████████████████████████████████████████████████████████████████████████████▌         | 2.48G/2.71G [03:22&lt;00:12, 18.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|████████████████████████████████████████████████████████████████████████████████████████████████████▋         | 2.48G/2.71G [03:23&lt;00:13, 17.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|████████████████████████████████████████████████████████████████████████████████████████████████████▊         | 2.49G/2.71G [03:23&lt;00:12, 17.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|████████████████████████████████████████████████████████████████████████████████████████████████████▉         | 2.49G/2.71G [03:23&lt;00:10, 20.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████         | 2.49G/2.71G [03:23&lt;00:16, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▏        | 2.50G/2.71G [03:23&lt;00:13, 15.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▎        | 2.50G/2.71G [03:24&lt;00:13, 16.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▍        | 2.50G/2.71G [03:24&lt;00:16, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▍        | 2.50G/2.71G [03:24&lt;00:15, 13.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▌        | 2.50G/2.71G [03:24&lt;00:16, 12.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 92%|█████████████████████████████████████████████████████████████████████████████████████████████████████▋        | 2.51G/2.71G [03:24&lt;00:15, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|█████████████████████████████████████████████████████████████████████████████████████████████████████▊        | 2.51G/2.71G [03:24&lt;00:13, 15.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|█████████████████████████████████████████████████████████████████████████████████████████████████████▉        | 2.51G/2.71G [03:25&lt;00:10, 19.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████        | 2.52G/2.71G [03:25&lt;00:12, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████        | 2.52G/2.71G [03:25&lt;00:11, 17.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 2.52G/2.71G [03:25&lt;00:10, 18.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▎       | 2.52G/2.71G [03:26&lt;00:18, 10.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▍       | 2.53G/2.71G [03:26&lt;00:24, 7.65MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 2.53G/2.71G [03:26&lt;00:22, 8.23MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 2.53G/2.71G [03:26&lt;00:21, 8.37MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▋       | 2.53G/2.71G [03:27&lt;00:22, 8.23MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▋       | 2.53G/2.71G [03:27&lt;00:22, 7.86MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▋       | 2.53G/2.71G [03:27&lt;00:23, 7.53MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 93%|██████████████████████████████████████████████████████████████████████████████████████████████████████▊       | 2.53G/2.71G [03:27&lt;00:25, 7.02MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████▉       | 2.54G/2.71G [03:27&lt;00:18, 9.23MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████▉       | 2.54G/2.71G [03:27&lt;00:15, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████       | 2.54G/2.71G [03:28&lt;00:14, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 2.54G/2.71G [03:28&lt;00:12, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▏      | 2.54G/2.71G [03:28&lt;00:16, 10.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▎      | 2.55G/2.71G [03:28&lt;00:12, 13.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍      | 2.55G/2.71G [03:28&lt;00:17, 9.50MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▍      | 2.55G/2.71G [03:29&lt;00:14, 10.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▌      | 2.55G/2.71G [03:29&lt;00:12, 12.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▋      | 2.56G/2.71G [03:29&lt;00:14, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▊      | 2.56G/2.71G [03:29&lt;00:11, 13.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████▊      | 2.56G/2.71G [03:29&lt;00:10, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|███████████████████████████████████████████████████████████████████████████████████████████████████████▉      | 2.56G/2.71G [03:29&lt;00:13, 11.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████      | 2.57G/2.71G [03:30&lt;00:10, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▏     | 2.57G/2.71G [03:30&lt;00:09, 14.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▎     | 2.57G/2.71G [03:30&lt;00:12, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍     | 2.58G/2.71G [03:31&lt;00:13, 10.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▌     | 2.58G/2.71G [03:31&lt;00:10, 12.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▋     | 2.58G/2.71G [03:31&lt;00:09, 14.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▊     | 2.58G/2.71G [03:31&lt;00:11, 11.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉     | 2.59G/2.71G [03:31&lt;00:08, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 95%|████████████████████████████████████████████████████████████████████████████████████████████████████████▉     | 2.59G/2.71G [03:31&lt;00:08, 14.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████     | 2.59G/2.71G [03:32&lt;00:10, 11.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▏    | 2.59G/2.71G [03:32&lt;00:08, 14.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▎    | 2.60G/2.71G [03:32&lt;00:07, 15.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▍    | 2.60G/2.71G [03:32&lt;00:07, 15.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌    | 2.60G/2.71G [03:32&lt;00:06, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▋    | 2.60G/2.71G [03:32&lt;00:06, 16.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▋    | 2.61G/2.71G [03:33&lt;00:06, 15.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▊    | 2.61G/2.71G [03:33&lt;00:06, 16.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉    | 2.61G/2.71G [03:33&lt;00:05, 18.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████    | 2.61G/2.71G [03:33&lt;00:05, 16.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 96%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▏   | 2.62G/2.71G [03:33&lt;00:04, 20.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▏   | 2.62G/2.71G [03:33&lt;00:07, 12.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▎   | 2.62G/2.71G [03:34&lt;00:08, 10.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▍   | 2.62G/2.71G [03:34&lt;00:08, 11.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▌   | 2.63G/2.71G [03:34&lt;00:05, 15.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▋   | 2.63G/2.71G [03:34&lt;00:06, 13.0MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▋   | 2.63G/2.71G [03:34&lt;00:05, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▊   | 2.63G/2.71G [03:34&lt;00:05, 14.8MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▉   | 2.64G/2.71G [03:35&lt;00:04, 15.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████   | 2.64G/2.71G [03:35&lt;00:05, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████   | 2.64G/2.71G [03:35&lt;00:05, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 97%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▏  | 2.64G/2.71G [03:35&lt;00:04, 15.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▎  | 2.65G/2.71G [03:35&lt;00:05, 12.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▎  | 2.65G/2.71G [03:35&lt;00:04, 13.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍  | 2.65G/2.71G [03:36&lt;00:04, 12.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▌  | 2.65G/2.71G [03:36&lt;00:05, 11.6MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▌  | 2.65G/2.71G [03:36&lt;00:04, 13.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▋  | 2.66G/2.71G [03:36&lt;00:03, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▊  | 2.66G/2.71G [03:36&lt;00:03, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▉  | 2.66G/2.71G [03:36&lt;00:03, 15.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████  | 2.66G/2.71G [03:37&lt;00:02, 18.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 2.67G/2.71G [03:37&lt;00:02, 17.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 2.67G/2.71G [03:37&lt;00:02, 14.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 98%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 2.67G/2.71G [03:37&lt;00:02, 14.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 2.67G/2.71G [03:37&lt;00:02, 14.2MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 2.67G/2.71G [03:37&lt;00:03, 11.1MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 2.68G/2.71G [03:37&lt;00:03, 11.9MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 2.68G/2.71G [03:38&lt;00:02, 12.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 2.68G/2.71G [03:38&lt;00:03, 9.08MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 2.68G/2.71G [03:38&lt;00:03, 10.5MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 2.68G/2.71G [03:38&lt;00:02, 12.3MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 2.68G/2.71G [03:38&lt;00:02, 12.7MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 2.69G/2.71G [03:38&lt;00:02, 10.4MB/s]</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2.69G/2.71G [03:39&lt;00:01, 13.1MB/s]</code></pre>
+</table>
+
+<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2.69G/2.71G [03:39&lt;00:01, 14.4MB/s]</code></pre>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▏| 2.69G/2.71G [03:39&lt;00:01, 16.5MB/s]</code></pre>
+<p>To count the number of rows associated with each group, we use the <code>COUNT</code> keyword. Calling <code>COUNT(*)</code> will compute the total number of rows in each group, including rows with null values. Its <code>pandas</code> equivalent is <code>.groupby().size()</code>.</p>
+<p>Recall the <code>Dragon</code> table from the previous lecture:</p>
+<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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="op">%%</span>sql</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<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">
+<span style="None">Running query in 'basic'</span>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎| 2.70G/2.71G [03:39&lt;00:01, 15.4MB/s]</code></pre>
+<div class="cell-output cell-output-display" data-execution_count="7">
+<table data-quarto-postprocess="true" class="table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th data-quarto-table-cell-role="th">name</th>
+<th data-quarto-table-cell-role="th">year</th>
+<th data-quarto-table-cell-role="th">cute</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>hiccup</td>
+<td>2010</td>
+<td>10</td>
+</tr>
+<tr class="even">
+<td>drogon</td>
+<td>2011</td>
+<td>-100</td>
+</tr>
+<tr class="odd">
+<td>dragon 2</td>
+<td>2019</td>
+<td>0</td>
+</tr>
+<tr class="even">
+<td>puff</td>
+<td>2010</td>
+<td>100</td>
+</tr>
+<tr class="odd">
+<td>smaug</td>
+<td>2011</td>
+<td>None</td>
+</tr>
+</tbody>
+</table>
+
+<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code> 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▍| 2.70G/2.71G [03:39&lt;00:01, 14.4MB/s]</code></pre>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▍| 2.70G/2.71G [03:39&lt;00:00, 15.0MB/s]</code></pre>
+<p>Notice that <code>COUNT(*)</code> and <code>COUNT(cute)</code> result in different outputs.</p>
+<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(<span class="op">*</span>)</span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>GROUP BY year<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">
+<span style="None">Running query in 'basic'</span>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▌| 2.70G/2.71G [03:39&lt;00:00, 12.8MB/s]</code></pre>
+<div class="cell-output cell-output-display" data-execution_count="8">
+<table data-quarto-postprocess="true" class="table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th data-quarto-table-cell-role="th">year</th>
+<th data-quarto-table-cell-role="th">count_star()</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>2010</td>
+<td>2</td>
+</tr>
+<tr class="even">
+<td>2011</td>
+<td>2</td>
+</tr>
+<tr class="odd">
+<td>2019</td>
+<td>1</td>
+</tr>
+</tbody>
+</table>
+
+<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▋| 2.70G/2.71G [03:40&lt;00:00, 13.5MB/s]</code></pre>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 2.71G/2.71G [03:40&lt;00:00, 16.0MB/s]</code></pre>
+<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="9">
+<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><span class="op">%%</span>sql</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(cute)</span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>GROUP BY year<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">
+<span style="None">Running query in 'basic'</span>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 2.71G/2.71G [03:40&lt;00:00, 12.3MB/s]</code></pre>
+<div class="cell-output cell-output-display" data-execution_count="9">
+<table data-quarto-postprocess="true" class="table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th data-quarto-table-cell-role="th">year</th>
+<th data-quarto-table-cell-role="th">count(cute)</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>2010</td>
+<td>2</td>
+</tr>
+<tr class="even">
+<td>2011</td>
+<td>1</td>
+</tr>
+<tr class="odd">
+<td>2019</td>
+<td>1</td>
+</tr>
+</tbody>
+</table>
+
+<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 2.71G/2.71G [03:40&lt;00:00, 11.4MB/s]</code></pre>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 2.71G/2.71G [03:40&lt;00:00, 12.6MB/s]</code></pre>
+<p>With this definition of <code>GROUP BY</code> in hand, let’s update our SQL order of operations. Remember: <em>every</em> SQL query must list clauses in this order.</p>
+<pre><code>SELECT &lt;column expression list&gt;
+FROM &lt;table&gt;
+[WHERE &lt;predicate&gt;]
+[GROUP BY &lt;column list&gt;]
+[ORDER BY &lt;column list&gt;]
+[LIMIT &lt;number of rows&gt;]
+[OFFSET &lt;number of rows&gt;];</code></pre>
+<p>Note that we can use the <code>AS</code> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<code>MAX</code>, <code>MIN</code>, etc.).</p>
+</section>
+<section id="filtering-groups" class="level2" data-number="21.2">
+<h2 data-number="21.2" class="anchored" data-anchor-id="filtering-groups"><span class="header-section-number">21.2</span> Filtering Groups</h2>
+<p>Now, what if we only want groups that meet a certain condition? <code>HAVING</code> filters groups by applying some condition across all rows in each group. We interpret it as a way to keep only the groups <code>HAVING</code> some condition. Note the difference between <code>WHERE</code> and <code>HAVING</code>: we use <code>WHERE</code> to filter rows, whereas we use <code>HAVING</code> to filter <em>groups</em>. <code>WHERE</code> precedes <code>HAVING</code> in terms of how SQL executes a query.</p>
+<p>Let’s take a look at the <code>Dish</code> table to see how we can use <code>HAVING</code>. Say we want to group dishes with a cost greater than 4 by <code>type</code> and only keep groups where the max cost is less than 10.</p>
+<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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><span class="op">%%</span>sql</span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, COUNT(<span class="op">*</span>)</span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>WHERE cost <span class="op">&gt;</span> <span class="dv">4</span></span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span></span>
+<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>HAVING MAX(cost) <span class="op">&lt;</span>  <span class="dv">10</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">
+<span style="None">Running query in 'basic'</span>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.71G/2.71G [03:40&lt;00:00, 12.3MB/s]</code></pre>
+<div class="cell-output cell-output-display" data-execution_count="10">
+<table data-quarto-postprocess="true" class="table table-sm table-striped small">
+<thead>
+<tr class="header">
+<th data-quarto-table-cell-role="th">type</th>
+<th data-quarto-table-cell-role="th">count_star()</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>dessert</td>
+<td>1</td>
+</tr>
+</tbody>
+</table>
+
+<span style="font-style:italic;font-size:11px"><code>ResultSet</code> : to convert to pandas, call <a href="https://jupysql.ploomber.io/en/latest/integrations/pandas.html"><code>.DataFrame()</code></a> or to polars, call <a href="https://jupysql.ploomber.io/en/latest/integrations/polars.html"><code>.PolarsDataFrame()</code></a></span><br>
 </div>
-<div class="cell-output cell-output-stderr">
-<pre><code></code></pre>
 </div>
+<p>Here, we first use <code>WHERE</code> to filter for rows with a cost greater than 4. We then group our values by <code>type</code> before applying the <code>HAVING</code> operator. With <code>HAVING</code>, we can filter our groups based on if the max cost is less than 10.</p>
+</section>
+<section id="summary-sql" class="level2" data-number="21.3">
+<h2 data-number="21.3" class="anchored" data-anchor-id="summary-sql"><span class="header-section-number">21.3</span> Summary: SQL</h2>
+<p>With this definition of <code>GROUP BY</code> and <code>HAVING</code> in hand, let’s update our SQL order of operations. Remember: <em>every</em> SQL query must list clauses in this order.</p>
+<pre><code>SELECT &lt;column expression list&gt;
+FROM &lt;table&gt;
+[WHERE &lt;predicate&gt;]
+[GROUP BY &lt;column list&gt;]
+[ORDER BY &lt;column list&gt;]
+[LIMIT &lt;number of rows&gt;]
+[OFFSET &lt;number of rows&gt;];</code></pre>
+<p>Note that we can use the <code>AS</code> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<code>MAX</code>, <code>MIN</code>, etc.).</p>
+</section>
+<section id="eda-in-sql" class="level2" data-number="21.4">
+<h2 data-number="21.4" class="anchored" data-anchor-id="eda-in-sql"><span class="header-section-number">21.4</span> EDA in SQL</h2>
+<p>In the last lecture, we mostly worked under the assumption that our data had already been cleaned. However, as we saw in our first pass through the data science lifecycle, we’re very unlikely to be given data that is free of formatting issues. With this in mind, we’ll want to learn how to clean and transform data in SQL.</p>
+<p>Our typical workflow when working with “big data” is:</p>
+<ol type="1">
+<li>Use SQL to query data from a database</li>
+<li>Use Python (with <code>pandas</code>) to analyze this data in detail</li>
+</ol>
+<p>We can, however, still perform simple data cleaning and re-structuring using SQL directly. To do so, we’ll use the <code>Title</code> table from the <code>imdb_duck</code> database, which contains information about movies and actors.</p>
+<p>Let’s load in the <code>imdb_duck</code> database.</p>
+<div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" 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="im">import</span> os</span>
+<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>os.environ[<span class="st">"TQDM_DISABLE"</span>] <span class="op">=</span> <span class="st">"1"</span></span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> os.path.exists(<span class="st">"/home/jovyan/shared/sql/imdb_duck.db"</span>):</span>
+<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:////home/jovyan/shared/sql/imdb_duck.db"</span></span>
+<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="cf">elif</span> os.path.exists(<span class="st">"data/imdb_duck.db"</span>):</span>
+<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span>  <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
+<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
+<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>    <span class="im">import</span> gdown</span>
+<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>    url <span class="op">=</span> <span class="st">'https://drive.google.com/uc?id=10tKOHGLt9QoOgq5Ii-FhxpB9lDSQgl1O'</span></span>
+<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>    output_path <span class="op">=</span> <span class="st">'data/imdb_duck.db'</span></span>
+<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>    gdown.download(url, output_path, quiet<span class="op">=</span><span class="va">False</span>)</span>
+<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:///data/imdb_duck.db"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="12">
-<div class="sourceCode cell-code" id="cb1199"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1199-1"><a href="#cb1199-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sqlalchemy <span class="im">import</span> create_engine</span>
-<span id="cb1199-2"><a href="#cb1199-2" aria-hidden="true" tabindex="-1"></a>imdb_engine <span class="op">=</span> create_engine(imdbpath, connect_args<span class="op">=</span>{<span class="st">'read_only'</span>: <span class="va">True</span>})</span>
-<span id="cb1199-3"><a href="#cb1199-3" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql imdb_engine <span class="op">--</span>alias imdb</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<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">from</span> sqlalchemy <span class="im">import</span> create_engine</span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>imdb_engine <span class="op">=</span> create_engine(imdbpath, connect_args<span class="op">=</span>{<span class="st">'read_only'</span>: <span class="va">True</span>})</span>
+<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql imdb_engine <span class="op">--</span>alias imdb</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Since we’ll be working with the <code>Title</code> table, let’s take a quick look at what it contains.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="13">
-<div class="sourceCode cell-code" id="cb1200"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1200-1"><a href="#cb1200-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql imdb </span>
-<span id="cb1200-2"><a href="#cb1200-2" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb1200-3"><a href="#cb1200-3" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb1200-4"><a href="#cb1200-4" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1200-5"><a href="#cb1200-5" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle IN (<span class="st">'Ginny &amp; Georgia'</span>, <span class="st">'What If...?'</span>, <span class="st">'Succession'</span>, <span class="st">'Veep'</span>, <span class="st">'Tenet'</span>)</span>
-<span id="cb1200-6"><a href="#cb1200-6" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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="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="op">%%</span>sql imdb </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>SELECT <span class="op">*</span></span>
+<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle IN (<span class="st">'Ginny &amp; Georgia'</span>, <span class="st">'What If...?'</span>, <span class="st">'Succession'</span>, <span class="st">'Veep'</span>, <span class="st">'Tenet'</span>)</span>
+<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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" data-execution_count="13">
 <table data-quarto-postprocess="true" class="table table-sm table-striped small">
 <thead>
@@ -4316,17 +758,6 @@ <h2 data-number="21.4" class="anchored" data-anchor-id="eda-in-sql"><span class=
 </thead>
 <tbody>
 <tr class="odd">
-<td>tt4097596</td>
-<td>movie</td>
-<td>What If...?</td>
-<td>What If...?</td>
-<td>False</td>
-<td>None</td>
-<td>None</td>
-<td>25</td>
-<td>Action,Drama</td>
-</tr>
-<tr class="even">
 <td>tt0254490</td>
 <td>movie</td>
 <td>Succession</td>
@@ -4337,17 +768,6 @@ <h2 data-number="21.4" class="anchored" data-anchor-id="eda-in-sql"><span class=
 <td>119</td>
 <td>Drama</td>
 </tr>
-<tr class="odd">
-<td>tt1003027</td>
-<td>movie</td>
-<td>What If...?</td>
-<td>Notre univers impitoyable</td>
-<td>False</td>
-<td>2008</td>
-<td>None</td>
-<td>87</td>
-<td>Comedy</td>
-</tr>
 <tr class="even">
 <td>tt6723592</td>
 <td>movie</td>
@@ -4360,6 +780,28 @@ <h2 data-number="21.4" class="anchored" data-anchor-id="eda-in-sql"><span class=
 <td>Action,Sci-Fi,Thriller</td>
 </tr>
 <tr class="odd">
+<td>tt4097596</td>
+<td>movie</td>
+<td>What If...?</td>
+<td>What If...?</td>
+<td>False</td>
+<td>None</td>
+<td>None</td>
+<td>25</td>
+<td>Action,Drama</td>
+</tr>
+<tr class="even">
+<td>tt1003027</td>
+<td>movie</td>
+<td>What If...?</td>
+<td>Notre univers impitoyable</td>
+<td>False</td>
+<td>2008</td>
+<td>None</td>
+<td>87</td>
+<td>Comedy</td>
+</tr>
+<tr class="odd">
 <td>tt8008862</td>
 <td>movie</td>
 <td>Succession</td>
@@ -4403,10 +845,10 @@ <h3 data-number="21.4.1" class="anchored" data-anchor-id="matching-text-using-li
 <p>One common task we encountered in our first look at EDA was needing to match string data. For example, we might want to remove entries beginning with the same prefix as part of the data cleaning process.</p>
 <p>In SQL, we use the <code>LIKE</code> operator to (you guessed it) look for strings that are <em>like</em> a given string pattern.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="14">
-<div class="sourceCode cell-code" id="cb1201"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1201-1"><a href="#cb1201-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1201-2"><a href="#cb1201-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1201-3"><a href="#cb1201-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1201-4"><a href="#cb1201-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'Star Wars: Episode I - The Phantom Menace'</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="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>sql</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'Star Wars: Episode I - The Phantom Menace'</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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4431,11 +873,11 @@ <h3 data-number="21.4.1" class="anchored" data-anchor-id="matching-text-using-li
 </div>
 <p>What if we wanted to find <em>all</em> Star Wars movies? <code>%</code> is the wildcard operator, it means “look for any character, any number of times”. This makes it helpful for identifying strings that are similar to our desired pattern, even when we don’t know the full text of what we aim to extract.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="15">
-<div class="sourceCode cell-code" id="cb1202"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1202-1"><a href="#cb1202-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1202-2"><a href="#cb1202-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1202-3"><a href="#cb1202-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1202-4"><a href="#cb1202-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'%Star Wars%'</span></span>
-<span id="cb1202-5"><a href="#cb1202-5" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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="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="op">%%</span>sql</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'%Star Wars%'</span></span>
+<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4497,11 +939,11 @@ <h3 data-number="21.4.1" class="anchored" data-anchor-id="matching-text-using-li
 </div>
 <p>Alternatively, we can use RegEx! DuckDB and most real DBMSs allow for this. Note that here, we have to use the <code>SIMILAR TO</code> operater rather than <code>LIKE</code>.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="16">
-<div class="sourceCode cell-code" id="cb1203"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1203-1"><a href="#cb1203-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1203-2"><a href="#cb1203-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1203-3"><a href="#cb1203-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1203-4"><a href="#cb1203-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle SIMILAR TO <span class="st">'.*Star Wars*.'</span></span>
-<span id="cb1203-5"><a href="#cb1203-5" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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="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="op">%%</span>sql</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle SIMILAR TO <span class="st">'.*Star Wars*.'</span></span>
+<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4566,9 +1008,9 @@ <h3 data-number="21.4.1" class="anchored" data-anchor-id="matching-text-using-li
 <h3 data-number="21.4.2" class="anchored" data-anchor-id="casting-data-types"><span class="header-section-number">21.4.2</span> <code>CAST</code>ing Data Types</h3>
 <p>A common data cleaning task is converting data to the correct variable type. The <code>CAST</code> keyword is used to generate a new output column. Each entry in this output column is the result of converting the data in an existing column to a new data type. For example, we may wish to convert numeric data stored as a string to an integer.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="17">
-<div class="sourceCode cell-code" id="cb1204"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1204-1"><a href="#cb1204-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1204-2"><a href="#cb1204-2" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, CAST(runtimeMinutes AS INT)</span>
-<span id="cb1204-3"><a href="#cb1204-3" aria-hidden="true" tabindex="-1"></a>FROM Title<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="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="op">%%</span>sql</span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, CAST(runtimeMinutes AS INT)</span>
+<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>FROM Title<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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4631,10 +1073,10 @@ <h3 data-number="21.4.2" class="anchored" data-anchor-id="casting-data-types"><s
 <p>We use <code>CAST</code> when <code>SELECT</code>ing colunns for our output table. In the example above, we want to <code>SELECT</code> the columns of integer year and runtime data that is created by the <code>CAST</code>.</p>
 <p>SQL will automatically name a new column according to the command used to <code>SELECT</code> it, which can lead to unwieldy column names. We can rename the <code>CAST</code>ed column using the <code>AS</code> keyword.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="18">
-<div class="sourceCode cell-code" id="cb1205"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1205-1"><a href="#cb1205-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1205-2"><a href="#cb1205-2" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle AS title, CAST(runtimeMinutes AS INT) AS minutes, CAST(startYear AS INT) AS year</span>
-<span id="cb1205-3"><a href="#cb1205-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1205-4"><a href="#cb1205-4" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">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="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>sql</span>
+<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle AS title, CAST(runtimeMinutes AS INT) AS minutes, CAST(startYear AS INT) AS year</span>
+<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">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="cell-output cell-output-display">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4692,17 +1134,17 @@ <h3 data-number="21.4.3" class="anchored" data-anchor-id="using-conditional-stat
 <p>Scanning through the skeleton code above, you can see that the logic is similar to that of an <code>if</code> statement in Python. The conditional statement is first opened by calling <code>CASE</code>. Each new condition is specified by <code>WHEN</code>, with <code>THEN</code> indicating what value should be filled if the condition is met. <code>ELSE</code> specifies the value that should be filled if no other conditions are met. Lastly, <code>END</code> indicates the end of the conditional statement; once <code>END</code> has been called, SQL will continue evaluating the query as usual.</p>
 <p>Let’s see this in action. In the example below, we give the new column created by the <code>CASE</code> statement the name <code>movie_age</code>.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="19">
-<div class="sourceCode cell-code" id="cb1207"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1207-1"><a href="#cb1207-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1207-2"><a href="#cb1207-2" aria-hidden="true" tabindex="-1"></a><span class="op">/*</span> If a movie was filmed before <span class="dv">1950</span>, it <span class="kw">is</span> <span class="st">"old"</span></span>
-<span id="cb1207-3"><a href="#cb1207-3" aria-hidden="true" tabindex="-1"></a>Otherwise, <span class="cf">if</span> a movie was filmed before <span class="dv">2000</span>, it <span class="kw">is</span> <span class="st">"mid-aged"</span></span>
-<span id="cb1207-4"><a href="#cb1207-4" aria-hidden="true" tabindex="-1"></a>Else, a movie <span class="kw">is</span> <span class="st">"new"</span> <span class="op">*/</span></span>
-<span id="cb1207-5"><a href="#cb1207-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1207-6"><a href="#cb1207-6" aria-hidden="true" tabindex="-1"></a>SELECT titleType, startYear,</span>
-<span id="cb1207-7"><a href="#cb1207-7" aria-hidden="true" tabindex="-1"></a>CASE WHEN startYear <span class="op">&lt;</span> <span class="dv">1950</span> THEN <span class="st">'old'</span></span>
-<span id="cb1207-8"><a href="#cb1207-8" aria-hidden="true" tabindex="-1"></a>     WHEN startYear <span class="op">&lt;</span> <span class="dv">2000</span> THEN <span class="st">'mid-aged'</span></span>
-<span id="cb1207-9"><a href="#cb1207-9" aria-hidden="true" tabindex="-1"></a>     ELSE <span class="st">'new'</span></span>
-<span id="cb1207-10"><a href="#cb1207-10" aria-hidden="true" tabindex="-1"></a>     END AS movie_age</span>
-<span id="cb1207-11"><a href="#cb1207-11" aria-hidden="true" tabindex="-1"></a>FROM Title<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="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="op">%%</span>sql</span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="op">/*</span> If a movie was filmed before <span class="dv">1950</span>, it <span class="kw">is</span> <span class="st">"old"</span></span>
+<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>Otherwise, <span class="cf">if</span> a movie was filmed before <span class="dv">2000</span>, it <span class="kw">is</span> <span class="st">"mid-aged"</span></span>
+<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>Else, a movie <span class="kw">is</span> <span class="st">"new"</span> <span class="op">*/</span></span>
+<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>SELECT titleType, startYear,</span>
+<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>CASE WHEN startYear <span class="op">&lt;</span> <span class="dv">1950</span> THEN <span class="st">'old'</span></span>
+<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>     WHEN startYear <span class="op">&lt;</span> <span class="dv">2000</span> THEN <span class="st">'mid-aged'</span></span>
+<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>     ELSE <span class="st">'new'</span></span>
+<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a>     END AS movie_age</span>
+<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>FROM Title<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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4825,11 +1267,11 @@ <h3 data-number="21.5.1" class="anchored" data-anchor-id="aliasing-in-joins"><sp
 <p>When joining tables, we often create aliases for table names (similarly to what we did with column names in the last lecture). We do this as it is typically easier to refer to aliases, especially when we are working with long table names. We can even reference columns using aliased table names!</p>
 <p>Let’s say we want to determine the average rating of various movies. We’ll need to <code>JOIN</code> the <code>Title</code> and <code>Rating</code> tables and can create aliases for both tables.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="20">
-<div class="sourceCode cell-code" id="cb1209"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1209-1"><a href="#cb1209-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1209-2"><a href="#cb1209-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1209-3"><a href="#cb1209-3" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
-<span id="cb1209-4"><a href="#cb1209-4" aria-hidden="true" tabindex="-1"></a>FROM Title AS T INNER JOIN Rating AS R</span>
-<span id="cb1209-5"><a href="#cb1209-5" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<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="op">%%</span>sql</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>SELECT primaryTitle, averageRating</span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>FROM Title AS T INNER JOIN Rating AS R</span>
+<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4843,44 +1285,44 @@ <h3 data-number="21.5.1" class="anchored" data-anchor-id="aliasing-in-joins"><sp
 </thead>
 <tbody>
 <tr class="odd">
-<td>Schwägereltern</td>
-<td>4.599999904632568</td>
+<td>The Corbett-Fitzsimmons Fight</td>
+<td>5.199999809265137</td>
 </tr>
 <tr class="even">
-<td>IndeXovci: Izbori, jer ste vi to trazili</td>
-<td>7.699999809265137</td>
+<td>Bohemios</td>
+<td>4.199999809265137</td>
 </tr>
 <tr class="odd">
-<td>A Nightmare in Las Cruces: Part 2</td>
-<td>3.0999999046325684</td>
+<td>The Story of the Kelly Gang</td>
+<td>6.0</td>
 </tr>
 <tr class="even">
-<td>New York</td>
-<td>8.199999809265137</td>
+<td>The Prodigal Son</td>
+<td>5.400000095367432</td>
 </tr>
 <tr class="odd">
-<td>Cha-lui: Lost in Seoul</td>
-<td>4.699999809265137</td>
+<td>Robbery Under Arms</td>
+<td>4.300000190734863</td>
 </tr>
 <tr class="even">
-<td>Crazy Beautiful You</td>
-<td>6.900000095367432</td>
+<td>Hamlet</td>
+<td>2.9000000953674316</td>
 </tr>
 <tr class="odd">
-<td>Saken</td>
-<td>7.199999809265137</td>
+<td>Don Quijote</td>
+<td>4.199999809265137</td>
 </tr>
 <tr class="even">
-<td>Rattle the Cage</td>
-<td>6.599999904632568</td>
+<td>The Fairylogue and Radio-Plays</td>
+<td>5.099999904632568</td>
 </tr>
 <tr class="odd">
-<td>To Dream</td>
-<td>5.0</td>
+<td>Faldgruben</td>
+<td>4.400000095367432</td>
 </tr>
 <tr class="even">
-<td>Cerca de tu casa</td>
-<td>6.099999904632568</td>
+<td>Hamlet, Prince of Denmark</td>
+<td>4.699999809265137</td>
 </tr>
 </tbody>
 </table>
@@ -4891,11 +1333,11 @@ <h3 data-number="21.5.1" class="anchored" data-anchor-id="aliasing-in-joins"><sp
 </div>
 <p>Note that the <code>AS</code> is actually optional! We can create aliases for our tables even without it, but we usually include it for clarity.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="21">
-<div class="sourceCode cell-code" id="cb1210"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1210-1"><a href="#cb1210-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1210-2"><a href="#cb1210-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1210-3"><a href="#cb1210-3" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
-<span id="cb1210-4"><a href="#cb1210-4" aria-hidden="true" tabindex="-1"></a>FROM Title T INNER JOIN Rating R</span>
-<span id="cb1210-5"><a href="#cb1210-5" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<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="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="op">%%</span>sql</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>SELECT primaryTitle, averageRating</span>
+<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>FROM Title T INNER JOIN Rating R</span>
+<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -4972,24 +1414,24 @@ <h3 data-number="21.5.2" class="anchored" data-anchor-id="common-table-expressio
 table_name2, ...</code></pre>
 <p>Let’s say we want to identify the top 10 action movies that are highly rated (with an average rating greater than 7) and popular (having more than 5000 votes), along with the primary actors who are the most popular. We can use CTEs to break this query down into separate problems. Initially, we can filter to find good action movies and prolific actors separately. This way, in our final join, we only need to change the order.</p>
 <div class="cell" data-vscode="{&quot;languageId&quot;:&quot;python&quot;}" data-execution_count="22">
-<div class="sourceCode cell-code" id="cb1212"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1212-1"><a href="#cb1212-1" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1212-2"><a href="#cb1212-2" aria-hidden="true" tabindex="-1"></a>WITH </span>
-<span id="cb1212-3"><a href="#cb1212-3" aria-hidden="true" tabindex="-1"></a>good_action_movies AS (</span>
-<span id="cb1212-4"><a href="#cb1212-4" aria-hidden="true" tabindex="-1"></a>    SELECT <span class="op">*</span></span>
-<span id="cb1212-5"><a href="#cb1212-5" aria-hidden="true" tabindex="-1"></a>    FROM Title T JOIN Rating R ON T.tconst <span class="op">=</span> R.tconst  </span>
-<span id="cb1212-6"><a href="#cb1212-6" aria-hidden="true" tabindex="-1"></a>    WHERE genres LIKE <span class="st">'%Action%'</span> AND averageRating <span class="op">&gt;</span> <span class="dv">7</span> AND numVotes <span class="op">&gt;</span> <span class="dv">5000</span></span>
-<span id="cb1212-7"><a href="#cb1212-7" aria-hidden="true" tabindex="-1"></a>),</span>
-<span id="cb1212-8"><a href="#cb1212-8" aria-hidden="true" tabindex="-1"></a>prolific_actors AS (</span>
-<span id="cb1212-9"><a href="#cb1212-9" aria-hidden="true" tabindex="-1"></a>    SELECT N.nconst, primaryName, COUNT(<span class="op">*</span>) <span class="im">as</span> numRoles</span>
-<span id="cb1212-10"><a href="#cb1212-10" aria-hidden="true" tabindex="-1"></a>    FROM Name N JOIN Principal P ON N.nconst <span class="op">=</span> P.nconst</span>
-<span id="cb1212-11"><a href="#cb1212-11" aria-hidden="true" tabindex="-1"></a>    WHERE category <span class="op">=</span> <span class="st">'actor'</span></span>
-<span id="cb1212-12"><a href="#cb1212-12" aria-hidden="true" tabindex="-1"></a>    GROUP BY N.nconst, primaryName</span>
-<span id="cb1212-13"><a href="#cb1212-13" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb1212-14"><a href="#cb1212-14" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, primaryName, numRoles, ROUND(averageRating) AS rating</span>
-<span id="cb1212-15"><a href="#cb1212-15" aria-hidden="true" tabindex="-1"></a>FROM good_action_movies m, prolific_actors a, principal p</span>
-<span id="cb1212-16"><a href="#cb1212-16" aria-hidden="true" tabindex="-1"></a>WHERE p.tconst <span class="op">=</span> m.tconst AND p.nconst <span class="op">=</span> a.nconst</span>
-<span id="cb1212-17"><a href="#cb1212-17" aria-hidden="true" tabindex="-1"></a>ORDER BY rating DESC, numRoles DESC</span>
-<span id="cb1212-18"><a href="#cb1212-18" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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="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="op">%%</span>sql</span>
+<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>WITH </span>
+<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>good_action_movies AS (</span>
+<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>    SELECT <span class="op">*</span></span>
+<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>    FROM Title T JOIN Rating R ON T.tconst <span class="op">=</span> R.tconst  </span>
+<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>    WHERE genres LIKE <span class="st">'%Action%'</span> AND averageRating <span class="op">&gt;</span> <span class="dv">7</span> AND numVotes <span class="op">&gt;</span> <span class="dv">5000</span></span>
+<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a>),</span>
+<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>prolific_actors AS (</span>
+<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a>    SELECT N.nconst, primaryName, COUNT(<span class="op">*</span>) <span class="im">as</span> numRoles</span>
+<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a>    FROM Name N JOIN Principal P ON N.nconst <span class="op">=</span> P.nconst</span>
+<span id="cb27-11"><a href="#cb27-11" aria-hidden="true" tabindex="-1"></a>    WHERE category <span class="op">=</span> <span class="st">'actor'</span></span>
+<span id="cb27-12"><a href="#cb27-12" aria-hidden="true" tabindex="-1"></a>    GROUP BY N.nconst, primaryName</span>
+<span id="cb27-13"><a href="#cb27-13" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb27-14"><a href="#cb27-14" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, primaryName, numRoles, ROUND(averageRating) AS rating</span>
+<span id="cb27-15"><a href="#cb27-15" aria-hidden="true" tabindex="-1"></a>FROM good_action_movies m, prolific_actors a, principal p</span>
+<span id="cb27-16"><a href="#cb27-16" aria-hidden="true" tabindex="-1"></a>WHERE p.tconst <span class="op">=</span> m.tconst AND p.nconst <span class="op">=</span> a.nconst</span>
+<span id="cb27-17"><a href="#cb27-17" aria-hidden="true" tabindex="-1"></a>ORDER BY rating DESC, numRoles DESC</span>
+<span id="cb27-18"><a href="#cb27-18" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</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">
 <span style="None">Running query in 'imdb'</span>
 </div>
@@ -5375,457 +1817,457 @@ <h3 data-number="21.5.2" class="anchored" data-anchor-id="common-table-expressio
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1213" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1213-1"><a href="#cb1213-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb1213-2"><a href="#cb1213-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> SQL II</span></span>
-<span id="cb1213-3"><a href="#cb1213-3" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span>
-<span id="cb1213-4"><a href="#cb1213-4" aria-hidden="true" tabindex="-1"></a><span class="co">  echo: true</span></span>
-<span id="cb1213-5"><a href="#cb1213-5" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
-<span id="cb1213-6"><a href="#cb1213-6" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
-<span id="cb1213-7"><a href="#cb1213-7" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: false</span></span>
-<span id="cb1213-8"><a href="#cb1213-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
-<span id="cb1213-9"><a href="#cb1213-9" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
-<span id="cb1213-10"><a href="#cb1213-10" aria-hidden="true" tabindex="-1"></a><span class="co">    toc-title: SQL II</span></span>
-<span id="cb1213-11"><a href="#cb1213-11" aria-hidden="true" tabindex="-1"></a><span class="co">    page-layout: full</span></span>
-<span id="cb1213-12"><a href="#cb1213-12" aria-hidden="true" tabindex="-1"></a><span class="co">    theme:</span></span>
-<span id="cb1213-13"><a href="#cb1213-13" aria-hidden="true" tabindex="-1"></a><span class="co">      - cosmo</span></span>
-<span id="cb1213-14"><a href="#cb1213-14" aria-hidden="true" tabindex="-1"></a><span class="co">      - cerulean</span></span>
-<span id="cb1213-15"><a href="#cb1213-15" aria-hidden="true" tabindex="-1"></a><span class="co">    callout-icon: false</span></span>
-<span id="cb1213-16"><a href="#cb1213-16" aria-hidden="true" tabindex="-1"></a><span class="an">jupyter:</span></span>
-<span id="cb1213-17"><a href="#cb1213-17" aria-hidden="true" tabindex="-1"></a><span class="co">  jupytext:</span></span>
-<span id="cb1213-18"><a href="#cb1213-18" aria-hidden="true" tabindex="-1"></a><span class="co">    text_representation:</span></span>
-<span id="cb1213-19"><a href="#cb1213-19" aria-hidden="true" tabindex="-1"></a><span class="co">      extension: .qmd</span></span>
-<span id="cb1213-20"><a href="#cb1213-20" aria-hidden="true" tabindex="-1"></a><span class="co">      format_name: quarto</span></span>
-<span id="cb1213-21"><a href="#cb1213-21" aria-hidden="true" tabindex="-1"></a><span class="co">      format_version: '1.0'</span></span>
-<span id="cb1213-22"><a href="#cb1213-22" aria-hidden="true" tabindex="-1"></a><span class="co">      jupytext_version: 1.16.1</span></span>
-<span id="cb1213-23"><a href="#cb1213-23" aria-hidden="true" tabindex="-1"></a><span class="co">  kernelspec:</span></span>
-<span id="cb1213-24"><a href="#cb1213-24" aria-hidden="true" tabindex="-1"></a><span class="co">    display_name: Python 3 (ipykernel)</span></span>
-<span id="cb1213-25"><a href="#cb1213-25" aria-hidden="true" tabindex="-1"></a><span class="co">    language: python</span></span>
-<span id="cb1213-26"><a href="#cb1213-26" aria-hidden="true" tabindex="-1"></a><span class="co">    name: python3</span></span>
-<span id="cb1213-27"><a href="#cb1213-27" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
-<span id="cb1213-28"><a href="#cb1213-28" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-29"><a href="#cb1213-29" aria-hidden="true" tabindex="-1"></a>::: {.callout-note collapse="false"}</span>
-<span id="cb1213-30"><a href="#cb1213-30" aria-hidden="true" tabindex="-1"></a><span class="fu">## Learning Outcomes</span></span>
-<span id="cb1213-31"><a href="#cb1213-31" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform aggregations using <span class="in">`GROUP BY`</span></span>
-<span id="cb1213-32"><a href="#cb1213-32" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Introduce the ability to filter groups</span>
-<span id="cb1213-33"><a href="#cb1213-33" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform data cleaning and text manipulation in SQL</span>
-<span id="cb1213-34"><a href="#cb1213-34" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Join data across tables</span>
-<span id="cb1213-35"><a href="#cb1213-35" aria-hidden="true" tabindex="-1"></a>:::</span>
-<span id="cb1213-36"><a href="#cb1213-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-37"><a href="#cb1213-37" aria-hidden="true" tabindex="-1"></a>In this lecture, we'll continue our work from last time to introduce some advanced SQL syntax. </span>
-<span id="cb1213-38"><a href="#cb1213-38" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-39"><a href="#cb1213-39" aria-hidden="true" tabindex="-1"></a>First, let's load in the <span class="in">`basic_examples.db`</span> database.</span>
-<span id="cb1213-40"><a href="#cb1213-40" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-43"><a href="#cb1213-43" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-44"><a href="#cb1213-44" aria-hidden="true" tabindex="-1"></a><span class="co">#| code-fold: true</span></span>
-<span id="cb1213-45"><a href="#cb1213-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the SQL Alchemy Python library and DuckDB</span></span>
-<span id="cb1213-46"><a href="#cb1213-46" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sqlalchemy</span>
-<span id="cb1213-47"><a href="#cb1213-47" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> duckdb</span>
-<span id="cb1213-48"><a href="#cb1213-48" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-49"><a href="#cb1213-49" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-52"><a href="#cb1213-52" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-53"><a href="#cb1213-53" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-54"><a href="#cb1213-54" aria-hidden="true" tabindex="-1"></a><span class="co"># Load %%sql cell magic</span></span>
-<span id="cb1213-55"><a href="#cb1213-55" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext sql</span>
-<span id="cb1213-56"><a href="#cb1213-56" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-57"><a href="#cb1213-57" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-60"><a href="#cb1213-60" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-61"><a href="#cb1213-61" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-62"><a href="#cb1213-62" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the database</span></span>
-<span id="cb1213-63"><a href="#cb1213-63" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>basic_examples.db <span class="op">--</span>alias basic</span>
-<span id="cb1213-64"><a href="#cb1213-64" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-65"><a href="#cb1213-65" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-66"><a href="#cb1213-66" aria-hidden="true" tabindex="-1"></a><span class="fu">## Aggregating with `GROUP BY`</span></span>
-<span id="cb1213-67"><a href="#cb1213-67" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-68"><a href="#cb1213-68" aria-hidden="true" tabindex="-1"></a>At this point, we've seen that SQL offers much of the same functionality that was given to us by <span class="in">`pandas`</span>. We can extract data from a table, filter it, and reorder it to suit our needs.</span>
-<span id="cb1213-69"><a href="#cb1213-69" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-70"><a href="#cb1213-70" aria-hidden="true" tabindex="-1"></a>In <span class="in">`pandas`</span>, much of our analysis work relied heavily on being able to use <span class="in">`.groupby()`</span> to aggregate across the rows of our dataset. SQL's answer to this task is the (very conveniently named) <span class="in">`GROUP BY`</span> clause. While the outputs of <span class="in">`GROUP BY`</span> are similar to those of <span class="in">`.groupby()`</span> —— in both cases, we obtain an output table where some column has been used for grouping —— the syntax and logic used to group data in SQL are fairly different to the <span class="in">`pandas`</span> implementation.</span>
-<span id="cb1213-71"><a href="#cb1213-71" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-72"><a href="#cb1213-72" aria-hidden="true" tabindex="-1"></a>To illustrate <span class="in">`GROUP BY`</span>, we will consider the <span class="in">`Dish`</span> table from our database.</span>
-<span id="cb1213-73"><a href="#cb1213-73" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-76"><a href="#cb1213-76" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-77"><a href="#cb1213-77" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-78"><a href="#cb1213-78" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-79"><a href="#cb1213-79" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
-<span id="cb1213-80"><a href="#cb1213-80" aria-hidden="true" tabindex="-1"></a>FROM Dish<span class="op">;</span></span>
-<span id="cb1213-81"><a href="#cb1213-81" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-82"><a href="#cb1213-82" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-83"><a href="#cb1213-83" aria-hidden="true" tabindex="-1"></a>Notice that there are multiple dishes of the same <span class="in">`type`</span>. What if we wanted to find the total costs of dishes of a certain <span class="in">`type`</span>? To accomplish this, we would write the following code.</span>
-<span id="cb1213-84"><a href="#cb1213-84" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-87"><a href="#cb1213-87" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-88"><a href="#cb1213-88" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-89"><a href="#cb1213-89" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-90"><a href="#cb1213-90" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, SUM(cost)</span>
-<span id="cb1213-91"><a href="#cb1213-91" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
-<span id="cb1213-92"><a href="#cb1213-92" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span><span class="op">;</span></span>
-<span id="cb1213-93"><a href="#cb1213-93" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-94"><a href="#cb1213-94" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-95"><a href="#cb1213-95" aria-hidden="true" tabindex="-1"></a>What is going on here? The statement <span class="in">`GROUP BY type`</span> tells SQL to group the data based on the value contained in the <span class="in">`type`</span> column (whether a record is an appetizer, entree, or dessert). <span class="in">`SUM(cost)`</span> sums up the costs of dishes in each <span class="in">`type`</span> and displays the result in the output table.</span>
-<span id="cb1213-96"><a href="#cb1213-96" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-97"><a href="#cb1213-97" aria-hidden="true" tabindex="-1"></a>You may be wondering: why does <span class="in">`SUM(cost)`</span> come before the command to <span class="in">`GROUP BY type`</span>? Don't we need to form groups before we can count the number of entries in each? Remember that SQL is a *declarative* programming language —— a SQL programmer simply states what end result they would like to see, and leaves the task of figuring out *how* to obtain this result to SQL itself. This means that SQL queries sometimes don't follow what a reader sees as a "logical" sequence of thought. Instead, SQL requires that we follow its set order of operations when constructing queries. So long as we follow this order, SQL will handle the underlying logic.</span>
-<span id="cb1213-98"><a href="#cb1213-98" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-99"><a href="#cb1213-99" aria-hidden="true" tabindex="-1"></a>In practical terms: our goal with this query was to output the total <span class="in">`cost`</span>s of each <span class="in">`type`</span>. To communicate this to SQL, we say that we want to <span class="in">`SELECT`</span> the <span class="in">`SUM`</span>med <span class="in">`cost`</span> values for each <span class="in">`type`</span> group. </span>
-<span id="cb1213-100"><a href="#cb1213-100" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-101"><a href="#cb1213-101" aria-hidden="true" tabindex="-1"></a>There are many aggregation functions that can be used to aggregate the data contained in each group. Some common examples are:</span>
-<span id="cb1213-102"><a href="#cb1213-102" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-103"><a href="#cb1213-103" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`COUNT`</span>: count the number of rows associated with each group</span>
-<span id="cb1213-104"><a href="#cb1213-104" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`MIN`</span>: find the minimum value of each group</span>
-<span id="cb1213-105"><a href="#cb1213-105" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`MAX`</span>: find the maximum value of each group</span>
-<span id="cb1213-106"><a href="#cb1213-106" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`SUM`</span>: sum across all records in each group</span>
-<span id="cb1213-107"><a href="#cb1213-107" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`AVG`</span>: find the average value of each group</span>
-<span id="cb1213-108"><a href="#cb1213-108" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-109"><a href="#cb1213-109" aria-hidden="true" tabindex="-1"></a>We can easily compute multiple aggregations all at once (a task that was very tricky in <span class="in">`pandas`</span>).</span>
-<span id="cb1213-110"><a href="#cb1213-110" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-113"><a href="#cb1213-113" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-114"><a href="#cb1213-114" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-115"><a href="#cb1213-115" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-116"><a href="#cb1213-116" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, SUM(cost), MIN(cost), MAX(name)</span>
-<span id="cb1213-117"><a href="#cb1213-117" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
-<span id="cb1213-118"><a href="#cb1213-118" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span><span class="op">;</span></span>
-<span id="cb1213-119"><a href="#cb1213-119" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-120"><a href="#cb1213-120" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-121"><a href="#cb1213-121" aria-hidden="true" tabindex="-1"></a>To count the number of rows associated with each group, we use the <span class="in">`COUNT`</span> keyword. Calling <span class="in">`COUNT(*)`</span> will compute the total number of rows in each group, including rows with null values. Its <span class="in">`pandas`</span> equivalent is <span class="in">`.groupby().size()`</span>.</span>
-<span id="cb1213-122"><a href="#cb1213-122" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-123"><a href="#cb1213-123" aria-hidden="true" tabindex="-1"></a>Recall the <span class="in">`Dragon`</span> table from the previous lecture:</span>
-<span id="cb1213-124"><a href="#cb1213-124" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-127"><a href="#cb1213-127" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-128"><a href="#cb1213-128" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-129"><a href="#cb1213-129" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-130"><a href="#cb1213-130" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<span class="op">;</span></span>
-<span id="cb1213-131"><a href="#cb1213-131" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-132"><a href="#cb1213-132" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-133"><a href="#cb1213-133" aria-hidden="true" tabindex="-1"></a>Notice that <span class="in">`COUNT(*)`</span> and <span class="in">`COUNT(cute)`</span> result in different outputs.</span>
-<span id="cb1213-134"><a href="#cb1213-134" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-137"><a href="#cb1213-137" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-138"><a href="#cb1213-138" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-139"><a href="#cb1213-139" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-140"><a href="#cb1213-140" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(<span class="op">*</span>)</span>
-<span id="cb1213-141"><a href="#cb1213-141" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb1213-142"><a href="#cb1213-142" aria-hidden="true" tabindex="-1"></a>GROUP BY year<span class="op">;</span></span>
-<span id="cb1213-143"><a href="#cb1213-143" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-144"><a href="#cb1213-144" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-147"><a href="#cb1213-147" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-148"><a href="#cb1213-148" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-149"><a href="#cb1213-149" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-150"><a href="#cb1213-150" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(cute)</span>
-<span id="cb1213-151"><a href="#cb1213-151" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
-<span id="cb1213-152"><a href="#cb1213-152" aria-hidden="true" tabindex="-1"></a>GROUP BY year<span class="op">;</span></span>
-<span id="cb1213-153"><a href="#cb1213-153" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-154"><a href="#cb1213-154" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-155"><a href="#cb1213-155" aria-hidden="true" tabindex="-1"></a>With this definition of <span class="in">`GROUP BY`</span> in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
-<span id="cb1213-156"><a href="#cb1213-156" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-157"><a href="#cb1213-157" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
-<span id="cb1213-158"><a href="#cb1213-158" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
-<span id="cb1213-159"><a href="#cb1213-159" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
-<span id="cb1213-160"><a href="#cb1213-160" aria-hidden="true" tabindex="-1"></a><span class="in">    [GROUP BY &lt;column list&gt;]</span></span>
-<span id="cb1213-161"><a href="#cb1213-161" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
-<span id="cb1213-162"><a href="#cb1213-162" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
-<span id="cb1213-163"><a href="#cb1213-163" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
-<span id="cb1213-164"><a href="#cb1213-164" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-165"><a href="#cb1213-165" aria-hidden="true" tabindex="-1"></a>Note that we can use the <span class="in">`AS`</span> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<span class="in">`MAX`</span>, <span class="in">`MIN`</span>, etc.).</span>
-<span id="cb1213-166"><a href="#cb1213-166" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-167"><a href="#cb1213-167" aria-hidden="true" tabindex="-1"></a><span class="fu">## Filtering Groups</span></span>
-<span id="cb1213-168"><a href="#cb1213-168" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-169"><a href="#cb1213-169" aria-hidden="true" tabindex="-1"></a>Now, what if we only want groups that meet a certain condition? <span class="in">`HAVING`</span> filters groups by applying some condition across all rows in each group. We interpret it as a way to keep only the groups <span class="in">`HAVING`</span> some condition. Note the difference between <span class="in">`WHERE`</span> and <span class="in">`HAVING`</span>: we use <span class="in">`WHERE`</span> to filter rows, whereas we use <span class="in">`HAVING`</span> to filter *groups*. <span class="in">`WHERE`</span> precedes <span class="in">`HAVING`</span> in terms of how SQL executes a query.</span>
-<span id="cb1213-170"><a href="#cb1213-170" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-171"><a href="#cb1213-171" aria-hidden="true" tabindex="-1"></a>Let's take a look at the <span class="in">`Dish`</span> table to see how we can use <span class="in">`HAVING`</span>. Say we want to group dishes with a cost greater than 4 by <span class="in">`type`</span> and only keep groups where the max cost is less than 10.</span>
-<span id="cb1213-172"><a href="#cb1213-172" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-175"><a href="#cb1213-175" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-176"><a href="#cb1213-176" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-177"><a href="#cb1213-177" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-178"><a href="#cb1213-178" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, COUNT(<span class="op">*</span>)</span>
-<span id="cb1213-179"><a href="#cb1213-179" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
-<span id="cb1213-180"><a href="#cb1213-180" aria-hidden="true" tabindex="-1"></a>WHERE cost <span class="op">&gt;</span> <span class="dv">4</span></span>
-<span id="cb1213-181"><a href="#cb1213-181" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span></span>
-<span id="cb1213-182"><a href="#cb1213-182" aria-hidden="true" tabindex="-1"></a>HAVING MAX(cost) <span class="op">&lt;</span>  <span class="dv">10</span><span class="op">;</span></span>
-<span id="cb1213-183"><a href="#cb1213-183" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-184"><a href="#cb1213-184" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-185"><a href="#cb1213-185" aria-hidden="true" tabindex="-1"></a>Here, we first use <span class="in">`WHERE`</span> to filter for rows with a cost greater than 4. We then group our values by <span class="in">`type`</span> before applying the <span class="in">`HAVING`</span> operator. With <span class="in">`HAVING`</span>, we can filter our groups based on if the max cost is less than 10.</span>
-<span id="cb1213-186"><a href="#cb1213-186" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-187"><a href="#cb1213-187" aria-hidden="true" tabindex="-1"></a><span class="fu">## Summary: SQL</span></span>
-<span id="cb1213-188"><a href="#cb1213-188" aria-hidden="true" tabindex="-1"></a>With this definition of <span class="in">`GROUP BY`</span> and <span class="in">`HAVING`</span> in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
-<span id="cb1213-189"><a href="#cb1213-189" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-190"><a href="#cb1213-190" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
-<span id="cb1213-191"><a href="#cb1213-191" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
-<span id="cb1213-192"><a href="#cb1213-192" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
-<span id="cb1213-193"><a href="#cb1213-193" aria-hidden="true" tabindex="-1"></a><span class="in">    [GROUP BY &lt;column list&gt;]</span></span>
-<span id="cb1213-194"><a href="#cb1213-194" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
-<span id="cb1213-195"><a href="#cb1213-195" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
-<span id="cb1213-196"><a href="#cb1213-196" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
-<span id="cb1213-197"><a href="#cb1213-197" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-198"><a href="#cb1213-198" aria-hidden="true" tabindex="-1"></a>Note that we can use the <span class="in">`AS`</span> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<span class="in">`MAX`</span>, <span class="in">`MIN`</span>, etc.).</span>
-<span id="cb1213-199"><a href="#cb1213-199" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-200"><a href="#cb1213-200" aria-hidden="true" tabindex="-1"></a><span class="fu">## EDA in SQL</span></span>
-<span id="cb1213-201"><a href="#cb1213-201" aria-hidden="true" tabindex="-1"></a>In the last lecture, we mostly worked under the assumption that our data had already been cleaned. However, as we saw in our first pass through the data science lifecycle, we're very unlikely to be given data that is free of formatting issues. With this in mind, we'll want to learn how to clean and transform data in SQL. </span>
-<span id="cb1213-202"><a href="#cb1213-202" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-203"><a href="#cb1213-203" aria-hidden="true" tabindex="-1"></a>Our typical workflow when working with "big data" is:</span>
-<span id="cb1213-204"><a href="#cb1213-204" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-205"><a href="#cb1213-205" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>Use SQL to query data from a database</span>
-<span id="cb1213-206"><a href="#cb1213-206" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>Use Python (with <span class="in">`pandas`</span>) to analyze this data in detail</span>
-<span id="cb1213-207"><a href="#cb1213-207" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-208"><a href="#cb1213-208" aria-hidden="true" tabindex="-1"></a>We can, however, still perform simple data cleaning and re-structuring using SQL directly. To do so, we'll use the <span class="in">`Title`</span> table from the <span class="in">`imdb_duck`</span> database, which contains information about movies and actors.</span>
-<span id="cb1213-209"><a href="#cb1213-209" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-210"><a href="#cb1213-210" aria-hidden="true" tabindex="-1"></a>Let's load in the <span class="in">`imdb_duck`</span> database.</span>
-<span id="cb1213-211"><a href="#cb1213-211" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-214"><a href="#cb1213-214" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-215"><a href="#cb1213-215" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-216"><a href="#cb1213-216" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
-<span id="cb1213-217"><a href="#cb1213-217" aria-hidden="true" tabindex="-1"></a>os.environ[<span class="st">"TQDM_DISABLE"</span>] <span class="op">=</span> <span class="st">"1"</span></span>
-<span id="cb1213-218"><a href="#cb1213-218" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> os.path.exists(<span class="st">"/home/jovyan/shared/sql/imdb_duck.db"</span>):</span>
-<span id="cb1213-219"><a href="#cb1213-219" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:////home/jovyan/shared/sql/imdb_duck.db"</span></span>
-<span id="cb1213-220"><a href="#cb1213-220" aria-hidden="true" tabindex="-1"></a><span class="cf">elif</span> os.path.exists(<span class="st">"data/imdb_duck.db"</span>):</span>
-<span id="cb1213-221"><a href="#cb1213-221" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span>  <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
-<span id="cb1213-222"><a href="#cb1213-222" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
-<span id="cb1213-223"><a href="#cb1213-223" aria-hidden="true" tabindex="-1"></a>    <span class="im">import</span> gdown</span>
-<span id="cb1213-224"><a href="#cb1213-224" aria-hidden="true" tabindex="-1"></a>    url <span class="op">=</span> <span class="st">'https://drive.google.com/uc?id=10tKOHGLt9QoOgq5Ii-FhxpB9lDSQgl1O'</span></span>
-<span id="cb1213-225"><a href="#cb1213-225" aria-hidden="true" tabindex="-1"></a>    output_path <span class="op">=</span> <span class="st">'data/imdb_duck.db'</span></span>
-<span id="cb1213-226"><a href="#cb1213-226" aria-hidden="true" tabindex="-1"></a>    gdown.download(url, output_path, quiet<span class="op">=</span><span class="va">False</span>)</span>
-<span id="cb1213-227"><a href="#cb1213-227" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
-<span id="cb1213-228"><a href="#cb1213-228" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-229"><a href="#cb1213-229" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-232"><a href="#cb1213-232" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-233"><a href="#cb1213-233" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-234"><a href="#cb1213-234" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sqlalchemy <span class="im">import</span> create_engine</span>
-<span id="cb1213-235"><a href="#cb1213-235" aria-hidden="true" tabindex="-1"></a>imdb_engine <span class="op">=</span> create_engine(imdbpath, connect_args<span class="op">=</span>{<span class="st">'read_only'</span>: <span class="va">True</span>})</span>
-<span id="cb1213-236"><a href="#cb1213-236" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql imdb_engine <span class="op">--</span>alias imdb</span>
-<span id="cb1213-237"><a href="#cb1213-237" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-238"><a href="#cb1213-238" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-239"><a href="#cb1213-239" aria-hidden="true" tabindex="-1"></a>Since we'll be working with the <span class="in">`Title`</span> table, let's take a quick look at what it contains. </span>
-<span id="cb1213-240"><a href="#cb1213-240" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-243"><a href="#cb1213-243" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-244"><a href="#cb1213-244" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-245"><a href="#cb1213-245" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql imdb </span>
-<span id="cb1213-246"><a href="#cb1213-246" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb1213-247"><a href="#cb1213-247" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
-<span id="cb1213-248"><a href="#cb1213-248" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1213-249"><a href="#cb1213-249" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle IN (<span class="st">'Ginny &amp; Georgia'</span>, <span class="st">'What If...?'</span>, <span class="st">'Succession'</span>, <span class="st">'Veep'</span>, <span class="st">'Tenet'</span>)</span>
-<span id="cb1213-250"><a href="#cb1213-250" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
-<span id="cb1213-251"><a href="#cb1213-251" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-252"><a href="#cb1213-252" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-253"><a href="#cb1213-253" aria-hidden="true" tabindex="-1"></a><span class="fu">### Matching Text using `LIKE`</span></span>
-<span id="cb1213-254"><a href="#cb1213-254" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-255"><a href="#cb1213-255" aria-hidden="true" tabindex="-1"></a>One common task we encountered in our first look at EDA was needing to match string data. For example, we might want to remove entries beginning with the same prefix as part of the data cleaning process.</span>
-<span id="cb1213-256"><a href="#cb1213-256" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-257"><a href="#cb1213-257" aria-hidden="true" tabindex="-1"></a>In SQL, we use the <span class="in">`LIKE`</span> operator to (you guessed it) look for strings that are *like* a given string pattern. </span>
-<span id="cb1213-258"><a href="#cb1213-258" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-261"><a href="#cb1213-261" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-262"><a href="#cb1213-262" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-263"><a href="#cb1213-263" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-264"><a href="#cb1213-264" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1213-265"><a href="#cb1213-265" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1213-266"><a href="#cb1213-266" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'Star Wars: Episode I - The Phantom Menace'</span></span>
-<span id="cb1213-267"><a href="#cb1213-267" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-268"><a href="#cb1213-268" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-269"><a href="#cb1213-269" aria-hidden="true" tabindex="-1"></a>What if we wanted to find *all* Star Wars movies? <span class="in">`%`</span> is the wildcard operator, it means "look for any character, any number of times". This makes it helpful for identifying strings that are similar to our desired pattern, even when we don't know the full text of what we aim to extract.</span>
-<span id="cb1213-270"><a href="#cb1213-270" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-273"><a href="#cb1213-273" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-274"><a href="#cb1213-274" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-275"><a href="#cb1213-275" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-276"><a href="#cb1213-276" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1213-277"><a href="#cb1213-277" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1213-278"><a href="#cb1213-278" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'%Star Wars%'</span></span>
-<span id="cb1213-279"><a href="#cb1213-279" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
-<span id="cb1213-280"><a href="#cb1213-280" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-281"><a href="#cb1213-281" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-282"><a href="#cb1213-282" aria-hidden="true" tabindex="-1"></a>Alternatively, we can use RegEx! DuckDB and most real DBMSs allow for this. Note that here, we have to use the <span class="in">`SIMILAR TO`</span> operater rather than <span class="in">`LIKE`</span>.</span>
-<span id="cb1213-283"><a href="#cb1213-283" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-286"><a href="#cb1213-286" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-287"><a href="#cb1213-287" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-288"><a href="#cb1213-288" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-289"><a href="#cb1213-289" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
-<span id="cb1213-290"><a href="#cb1213-290" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1213-291"><a href="#cb1213-291" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle SIMILAR TO <span class="st">'.*Star Wars*.'</span></span>
-<span id="cb1213-292"><a href="#cb1213-292" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
-<span id="cb1213-293"><a href="#cb1213-293" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-294"><a href="#cb1213-294" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-295"><a href="#cb1213-295" aria-hidden="true" tabindex="-1"></a><span class="fu">### `CAST`ing Data Types</span></span>
-<span id="cb1213-296"><a href="#cb1213-296" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-297"><a href="#cb1213-297" aria-hidden="true" tabindex="-1"></a>A common data cleaning task is converting data to the correct variable type. The <span class="in">`CAST`</span> keyword is used to generate a new output column. Each entry in this output column is the result of converting the data in an existing column to a new data type. For example, we may wish to convert numeric data stored as a string to an integer.</span>
-<span id="cb1213-298"><a href="#cb1213-298" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-301"><a href="#cb1213-301" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-302"><a href="#cb1213-302" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-303"><a href="#cb1213-303" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-304"><a href="#cb1213-304" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, CAST(runtimeMinutes AS INT)</span>
-<span id="cb1213-305"><a href="#cb1213-305" aria-hidden="true" tabindex="-1"></a>FROM Title<span class="op">;</span></span>
-<span id="cb1213-306"><a href="#cb1213-306" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-307"><a href="#cb1213-307" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-308"><a href="#cb1213-308" aria-hidden="true" tabindex="-1"></a>We use <span class="in">`CAST`</span> when <span class="in">`SELECT`</span>ing colunns for our output table. In the example above, we want to <span class="in">`SELECT`</span> the columns of integer year and runtime data that is created by the <span class="in">`CAST`</span>. </span>
-<span id="cb1213-309"><a href="#cb1213-309" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-310"><a href="#cb1213-310" aria-hidden="true" tabindex="-1"></a>SQL will automatically name a new column according to the command used to <span class="in">`SELECT`</span> it, which can lead to unwieldy column names. We can rename the <span class="in">`CAST`</span>ed column using the <span class="in">`AS`</span> keyword.</span>
-<span id="cb1213-311"><a href="#cb1213-311" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-314"><a href="#cb1213-314" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-315"><a href="#cb1213-315" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-316"><a href="#cb1213-316" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-317"><a href="#cb1213-317" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle AS title, CAST(runtimeMinutes AS INT) AS minutes, CAST(startYear AS INT) AS year</span>
-<span id="cb1213-318"><a href="#cb1213-318" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
-<span id="cb1213-319"><a href="#cb1213-319" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">5</span><span class="op">;</span></span>
-<span id="cb1213-320"><a href="#cb1213-320" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-321"><a href="#cb1213-321" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-322"><a href="#cb1213-322" aria-hidden="true" tabindex="-1"></a><span class="fu">### Using Conditional Statements with `CASE`</span></span>
-<span id="cb1213-323"><a href="#cb1213-323" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-324"><a href="#cb1213-324" aria-hidden="true" tabindex="-1"></a>When working with <span class="in">`pandas`</span>, we often ran into situations where we wanted to generate new columns using some form of conditional statement. For example, say we wanted to describe a film title as "old," "mid-aged," or "new," depending on the year of its release.</span>
-<span id="cb1213-325"><a href="#cb1213-325" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-326"><a href="#cb1213-326" aria-hidden="true" tabindex="-1"></a>In SQL, conditional operations are performed using a <span class="in">`CASE`</span> clause. Conceptually, <span class="in">`CASE`</span> behaves much like the <span class="in">`CAST`</span> operation: it creates a new column that we can then <span class="in">`SELECT`</span> to appear in the output. The syntax for a <span class="in">`CASE`</span> clause is as follows:</span>
-<span id="cb1213-327"><a href="#cb1213-327" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-328"><a href="#cb1213-328" aria-hidden="true" tabindex="-1"></a><span class="in">    CASE WHEN &lt;condition&gt; THEN &lt;value&gt;</span></span>
-<span id="cb1213-329"><a href="#cb1213-329" aria-hidden="true" tabindex="-1"></a><span class="in">         WHEN &lt;other condition&gt; THEN &lt;other value&gt;</span></span>
-<span id="cb1213-330"><a href="#cb1213-330" aria-hidden="true" tabindex="-1"></a><span class="in">         ...</span></span>
-<span id="cb1213-331"><a href="#cb1213-331" aria-hidden="true" tabindex="-1"></a><span class="in">         ELSE &lt;yet another value&gt;</span></span>
-<span id="cb1213-332"><a href="#cb1213-332" aria-hidden="true" tabindex="-1"></a><span class="in">         END</span></span>
-<span id="cb1213-333"><a href="#cb1213-333" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-334"><a href="#cb1213-334" aria-hidden="true" tabindex="-1"></a>Scanning through the skeleton code above, you can see that the logic is similar to that of an <span class="in">`if`</span> statement in Python. The conditional statement is first opened by calling <span class="in">`CASE`</span>. Each new condition is specified by <span class="in">`WHEN`</span>, with <span class="in">`THEN`</span> indicating what value should be filled if the condition is met. <span class="in">`ELSE`</span> specifies the value that should be filled if no other conditions are met. Lastly, <span class="in">`END`</span> indicates the end of the conditional statement; once <span class="in">`END`</span> has been called, SQL will continue evaluating the query as usual. </span>
-<span id="cb1213-335"><a href="#cb1213-335" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-336"><a href="#cb1213-336" aria-hidden="true" tabindex="-1"></a>Let's see this in action. In the example below, we give the new column created by the <span class="in">`CASE`</span> statement the name <span class="in">`movie_age`</span>.</span>
-<span id="cb1213-337"><a href="#cb1213-337" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-340"><a href="#cb1213-340" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-341"><a href="#cb1213-341" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-342"><a href="#cb1213-342" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-343"><a href="#cb1213-343" aria-hidden="true" tabindex="-1"></a><span class="op">/*</span> If a movie was filmed before <span class="dv">1950</span>, it <span class="kw">is</span> <span class="st">"old"</span></span>
-<span id="cb1213-344"><a href="#cb1213-344" aria-hidden="true" tabindex="-1"></a>Otherwise, <span class="cf">if</span> a movie was filmed before <span class="dv">2000</span>, it <span class="kw">is</span> <span class="st">"mid-aged"</span></span>
-<span id="cb1213-345"><a href="#cb1213-345" aria-hidden="true" tabindex="-1"></a>Else, a movie <span class="kw">is</span> <span class="st">"new"</span> <span class="op">*/</span></span>
-<span id="cb1213-346"><a href="#cb1213-346" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-347"><a href="#cb1213-347" aria-hidden="true" tabindex="-1"></a>SELECT titleType, startYear,</span>
-<span id="cb1213-348"><a href="#cb1213-348" aria-hidden="true" tabindex="-1"></a>CASE WHEN startYear <span class="op">&lt;</span> <span class="dv">1950</span> THEN <span class="st">'old'</span></span>
-<span id="cb1213-349"><a href="#cb1213-349" aria-hidden="true" tabindex="-1"></a>     WHEN startYear <span class="op">&lt;</span> <span class="dv">2000</span> THEN <span class="st">'mid-aged'</span></span>
-<span id="cb1213-350"><a href="#cb1213-350" aria-hidden="true" tabindex="-1"></a>     ELSE <span class="st">'new'</span></span>
-<span id="cb1213-351"><a href="#cb1213-351" aria-hidden="true" tabindex="-1"></a>     END AS movie_age</span>
-<span id="cb1213-352"><a href="#cb1213-352" aria-hidden="true" tabindex="-1"></a>FROM Title<span class="op">;</span></span>
-<span id="cb1213-353"><a href="#cb1213-353" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-354"><a href="#cb1213-354" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-355"><a href="#cb1213-355" aria-hidden="true" tabindex="-1"></a><span class="fu">## `JOIN`ing Tables</span></span>
-<span id="cb1213-356"><a href="#cb1213-356" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-357"><a href="#cb1213-357" aria-hidden="true" tabindex="-1"></a>At this point, we're well-versed in using SQL as a tool to clean, manipulate, and transform data in a table. Notice that this sentence referred to one *table*, specifically. What happens if the data we need is distributed across multiple tables? This is an important consideration when using SQL —— recall that we first introduced SQL as a language to query from databases. Databases often store data in a multidimensional structure. In other words, information is stored across several tables, with each table containing a small subset of all the data housed by the database. </span>
-<span id="cb1213-358"><a href="#cb1213-358" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-359"><a href="#cb1213-359" aria-hidden="true" tabindex="-1"></a>A common way of organizing a database is by using a **star schema**. A star schema is composed of two types of tables. A **fact table** is the central table of the database —— it contains the information needed to link entries across several **dimension tables**, which contain more detailed information about the data. </span>
-<span id="cb1213-360"><a href="#cb1213-360" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-361"><a href="#cb1213-361" aria-hidden="true" tabindex="-1"></a>Say we were working with a database about boba offerings in Berkeley. The dimension tables of the database might contain information about tea varieties and boba toppings. The fact table would be used to link this information across the various dimension tables.</span>
-<span id="cb1213-362"><a href="#cb1213-362" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-363"><a href="#cb1213-363" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-364"><a href="#cb1213-364" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/multidimensional.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'multidimensional'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'850'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-365"><a href="#cb1213-365" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-366"><a href="#cb1213-366" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-367"><a href="#cb1213-367" aria-hidden="true" tabindex="-1"></a>If we explicitly mark the relationships between tables, we start to see the star-like structure of the star schema.</span>
-<span id="cb1213-368"><a href="#cb1213-368" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-369"><a href="#cb1213-369" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-370"><a href="#cb1213-370" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/star.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'star'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'650'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-371"><a href="#cb1213-371" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-372"><a href="#cb1213-372" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-373"><a href="#cb1213-373" aria-hidden="true" tabindex="-1"></a>To join data across multiple tables, we'll use the (creatively named) <span class="in">`JOIN`</span> keyword. We'll make things easier for now by first considering the simpler <span class="in">`cats`</span> dataset, which consists of the tables <span class="in">`s`</span> and <span class="in">`t`</span>.</span>
-<span id="cb1213-374"><a href="#cb1213-374" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-375"><a href="#cb1213-375" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-376"><a href="#cb1213-376" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/cats.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'cats'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'500'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-377"><a href="#cb1213-377" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-378"><a href="#cb1213-378" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-379"><a href="#cb1213-379" aria-hidden="true" tabindex="-1"></a>To perform a join, we amend the <span class="in">`FROM`</span> clause. You can think of this as saying, "<span class="in">`SELECT`</span> my data <span class="in">`FROM`</span> tables that have  been <span class="in">`JOIN`</span>ed together." </span>
-<span id="cb1213-380"><a href="#cb1213-380" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-381"><a href="#cb1213-381" aria-hidden="true" tabindex="-1"></a>Remember: SQL does not consider newlines or whitespace when interpreting queries. The indentation given in the example below is to help improve readability. If you wish, you can write code that does not follow this formatting.</span>
-<span id="cb1213-382"><a href="#cb1213-382" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-383"><a href="#cb1213-383" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
-<span id="cb1213-384"><a href="#cb1213-384" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM table_1 </span></span>
-<span id="cb1213-385"><a href="#cb1213-385" aria-hidden="true" tabindex="-1"></a><span class="in">        JOIN table_2 </span></span>
-<span id="cb1213-386"><a href="#cb1213-386" aria-hidden="true" tabindex="-1"></a><span class="in">        ON key_1 = key_2;</span></span>
-<span id="cb1213-387"><a href="#cb1213-387" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-388"><a href="#cb1213-388" aria-hidden="true" tabindex="-1"></a>We also need to specify what column from each table should be used to determine matching entries. By defining these keys, we provide SQL with the information it needs to pair rows of data together.</span>
-<span id="cb1213-389"><a href="#cb1213-389" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-390"><a href="#cb1213-390" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-391"><a href="#cb1213-391" aria-hidden="true" tabindex="-1"></a>The most commonly used type of SQL <span class="in">`JOIN`</span> is the **inner join**. It turns out you're already familiar with what an inner join does, and how it works – this is the type of join we've been using in <span class="in">`pandas`</span> all along! In an inner join, we combine every row in our first table with its matching entry in the second table. If a row from either table does not have a match in the other table, it is omitted from the output. </span>
-<span id="cb1213-392"><a href="#cb1213-392" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-393"><a href="#cb1213-393" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-394"><a href="#cb1213-394" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/inner.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'inner'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-395"><a href="#cb1213-395" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-396"><a href="#cb1213-396" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-397"><a href="#cb1213-397" aria-hidden="true" tabindex="-1"></a>In a **cross join**, *all* possible combinations of rows appear in the output table, regardless of whether or not rows share a matching key. Because all rows are joined, even if there is no matching key, it is not necessary to specify what keys to consider in an <span class="in">`ON`</span> statement. A cross join is also known as a cartesian product.</span>
-<span id="cb1213-398"><a href="#cb1213-398" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-399"><a href="#cb1213-399" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-400"><a href="#cb1213-400" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/cross.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'cross'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-401"><a href="#cb1213-401" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-402"><a href="#cb1213-402" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-403"><a href="#cb1213-403" aria-hidden="true" tabindex="-1"></a>Conceptually, we can interpret an inner join as a cross join, followed by removing all rows that do not share a matching key. Notice that the output of the inner join above contains all rows of the cross join example that contain a single color across the entire row.</span>
-<span id="cb1213-404"><a href="#cb1213-404" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-405"><a href="#cb1213-405" aria-hidden="true" tabindex="-1"></a>In a **left outer join**, *all* rows in the left table are kept in the output table. If a row in the right table shares a match with the left table, this row will be kept; otherwise, the rows in the right table are omitted from the output. We can fill in any missing values with <span class="in">`NULL`</span>.</span>
-<span id="cb1213-406"><a href="#cb1213-406" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-407"><a href="#cb1213-407" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-408"><a href="#cb1213-408" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/left.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'left'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-409"><a href="#cb1213-409" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-410"><a href="#cb1213-410" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-411"><a href="#cb1213-411" aria-hidden="true" tabindex="-1"></a>A **right outer join** keeps all rows in the right table. Rows in the left table are only kept if they share a match in the right table. Again, we can fill in any missing values with <span class="in">`NULL`</span>. </span>
-<span id="cb1213-412"><a href="#cb1213-412" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-413"><a href="#cb1213-413" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-414"><a href="#cb1213-414" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/right.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'right'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-415"><a href="#cb1213-415" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-416"><a href="#cb1213-416" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-417"><a href="#cb1213-417" aria-hidden="true" tabindex="-1"></a>In a **full outer join**, all rows that have a match between the two tables are joined together. If a row has no match in the second table, then the values of the columns for that second table are filled with <span class="in">`NULL`</span>. In other words, a full outer join performs an inner join *while still keeping* rows that have no match in the other table. This is best understood visually:</span>
-<span id="cb1213-418"><a href="#cb1213-418" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-419"><a href="#cb1213-419" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
-<span id="cb1213-420"><a href="#cb1213-420" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/full.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'full'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
-<span id="cb1213-421"><a href="#cb1213-421" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
-<span id="cb1213-422"><a href="#cb1213-422" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-423"><a href="#cb1213-423" aria-hidden="true" tabindex="-1"></a>We have kept the same output achieved using an inner join, with the addition of partially null rows for entries in <span class="in">`s`</span> and <span class="in">`t`</span> that had no match in the second table. </span>
-<span id="cb1213-424"><a href="#cb1213-424" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-425"><a href="#cb1213-425" aria-hidden="true" tabindex="-1"></a><span class="fu">### Aliasing in `JOIN`s</span></span>
-<span id="cb1213-426"><a href="#cb1213-426" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-427"><a href="#cb1213-427" aria-hidden="true" tabindex="-1"></a>When joining tables, we often create aliases for table names (similarly to what we did with column names in the last lecture). We do this as it is typically easier to refer to aliases, especially when we are working with long table names. We can even reference columns using aliased table names!</span>
-<span id="cb1213-428"><a href="#cb1213-428" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-429"><a href="#cb1213-429" aria-hidden="true" tabindex="-1"></a>Let's say we want to determine the average rating of various movies. We'll need to <span class="in">`JOIN`</span> the <span class="in">`Title`</span> and <span class="in">`Rating`</span> tables and can create aliases for both tables.</span>
-<span id="cb1213-430"><a href="#cb1213-430" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-433"><a href="#cb1213-433" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-434"><a href="#cb1213-434" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-435"><a href="#cb1213-435" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-436"><a href="#cb1213-436" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-437"><a href="#cb1213-437" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
-<span id="cb1213-438"><a href="#cb1213-438" aria-hidden="true" tabindex="-1"></a>FROM Title AS T INNER JOIN Rating AS R</span>
-<span id="cb1213-439"><a href="#cb1213-439" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<span class="op">;</span></span>
-<span id="cb1213-440"><a href="#cb1213-440" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-441"><a href="#cb1213-441" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-442"><a href="#cb1213-442" aria-hidden="true" tabindex="-1"></a>Note that the <span class="in">`AS`</span> is actually optional! We can create aliases for our tables even without it, but we usually include it for clarity.</span>
-<span id="cb1213-443"><a href="#cb1213-443" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-446"><a href="#cb1213-446" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-447"><a href="#cb1213-447" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-448"><a href="#cb1213-448" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-449"><a href="#cb1213-449" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-450"><a href="#cb1213-450" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
-<span id="cb1213-451"><a href="#cb1213-451" aria-hidden="true" tabindex="-1"></a>FROM Title T INNER JOIN Rating R</span>
-<span id="cb1213-452"><a href="#cb1213-452" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<span class="op">;</span></span>
-<span id="cb1213-453"><a href="#cb1213-453" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
-<span id="cb1213-454"><a href="#cb1213-454" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-455"><a href="#cb1213-455" aria-hidden="true" tabindex="-1"></a><span class="fu">### Common Table Expressions</span></span>
-<span id="cb1213-456"><a href="#cb1213-456" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-457"><a href="#cb1213-457" aria-hidden="true" tabindex="-1"></a>For more sophisticated data problems, the queries can become very complex. Common table expressions (CTEs) allow us to break down these complex queries into more manageable parts. To do so, we create temporary tables corresponding to different aspects of the problem and then reference them in the final query: </span>
-<span id="cb1213-458"><a href="#cb1213-458" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-459"><a href="#cb1213-459" aria-hidden="true" tabindex="-1"></a><span class="in">    WITH </span></span>
-<span id="cb1213-460"><a href="#cb1213-460" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name1 AS ( </span></span>
-<span id="cb1213-461"><a href="#cb1213-461" aria-hidden="true" tabindex="-1"></a><span class="in">        SELECT ...</span></span>
-<span id="cb1213-462"><a href="#cb1213-462" aria-hidden="true" tabindex="-1"></a><span class="in">    ),</span></span>
-<span id="cb1213-463"><a href="#cb1213-463" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name2 AS ( </span></span>
-<span id="cb1213-464"><a href="#cb1213-464" aria-hidden="true" tabindex="-1"></a><span class="in">        SELECT ...</span></span>
-<span id="cb1213-465"><a href="#cb1213-465" aria-hidden="true" tabindex="-1"></a><span class="in">    )</span></span>
-<span id="cb1213-466"><a href="#cb1213-466" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT ... </span></span>
-<span id="cb1213-467"><a href="#cb1213-467" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM </span></span>
-<span id="cb1213-468"><a href="#cb1213-468" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name1, </span></span>
-<span id="cb1213-469"><a href="#cb1213-469" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name2, ...</span></span>
-<span id="cb1213-470"><a href="#cb1213-470" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-471"><a href="#cb1213-471" aria-hidden="true" tabindex="-1"></a>Let's say we want to identify the top 10 action movies that are highly rated (with an average rating greater than 7) and popular (having more than 5000 votes), along with the primary actors who are the most popular. We can use CTEs to break this query down into separate problems. Initially, we can filter to find good action movies and prolific actors separately. This way, in our final join, we only need to change the order.</span>
-<span id="cb1213-472"><a href="#cb1213-472" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1213-475"><a href="#cb1213-475" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
-<span id="cb1213-476"><a href="#cb1213-476" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
-<span id="cb1213-477"><a href="#cb1213-477" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
-<span id="cb1213-478"><a href="#cb1213-478" aria-hidden="true" tabindex="-1"></a>WITH </span>
-<span id="cb1213-479"><a href="#cb1213-479" aria-hidden="true" tabindex="-1"></a>good_action_movies AS (</span>
-<span id="cb1213-480"><a href="#cb1213-480" aria-hidden="true" tabindex="-1"></a>    SELECT <span class="op">*</span></span>
-<span id="cb1213-481"><a href="#cb1213-481" aria-hidden="true" tabindex="-1"></a>    FROM Title T JOIN Rating R ON T.tconst <span class="op">=</span> R.tconst  </span>
-<span id="cb1213-482"><a href="#cb1213-482" aria-hidden="true" tabindex="-1"></a>    WHERE genres LIKE <span class="st">'%Action%'</span> AND averageRating <span class="op">&gt;</span> <span class="dv">7</span> AND numVotes <span class="op">&gt;</span> <span class="dv">5000</span></span>
-<span id="cb1213-483"><a href="#cb1213-483" aria-hidden="true" tabindex="-1"></a>),</span>
-<span id="cb1213-484"><a href="#cb1213-484" aria-hidden="true" tabindex="-1"></a>prolific_actors AS (</span>
-<span id="cb1213-485"><a href="#cb1213-485" aria-hidden="true" tabindex="-1"></a>    SELECT N.nconst, primaryName, COUNT(<span class="op">*</span>) <span class="im">as</span> numRoles</span>
-<span id="cb1213-486"><a href="#cb1213-486" aria-hidden="true" tabindex="-1"></a>    FROM Name N JOIN Principal P ON N.nconst <span class="op">=</span> P.nconst</span>
-<span id="cb1213-487"><a href="#cb1213-487" aria-hidden="true" tabindex="-1"></a>    WHERE category <span class="op">=</span> <span class="st">'actor'</span></span>
-<span id="cb1213-488"><a href="#cb1213-488" aria-hidden="true" tabindex="-1"></a>    GROUP BY N.nconst, primaryName</span>
-<span id="cb1213-489"><a href="#cb1213-489" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb1213-490"><a href="#cb1213-490" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, primaryName, numRoles, ROUND(averageRating) AS rating</span>
-<span id="cb1213-491"><a href="#cb1213-491" aria-hidden="true" tabindex="-1"></a>FROM good_action_movies m, prolific_actors a, principal p</span>
-<span id="cb1213-492"><a href="#cb1213-492" aria-hidden="true" tabindex="-1"></a>WHERE p.tconst <span class="op">=</span> m.tconst AND p.nconst <span class="op">=</span> a.nconst</span>
-<span id="cb1213-493"><a href="#cb1213-493" aria-hidden="true" tabindex="-1"></a>ORDER BY rating DESC, numRoles DESC</span>
-<span id="cb1213-494"><a href="#cb1213-494" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
-<span id="cb1213-495"><a href="#cb1213-495" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<div class="sourceCode" id="cb28" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> SQL II</span></span>
+<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span>
+<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a><span class="co">  echo: true</span></span>
+<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
+<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a><span class="co">  html:</span></span>
+<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a><span class="co">    code-fold: false</span></span>
+<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a><span class="co">    code-tools: true</span></span>
+<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a><span class="co">    toc: true</span></span>
+<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a><span class="co">    toc-title: SQL II</span></span>
+<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a><span class="co">    page-layout: full</span></span>
+<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a><span class="co">    theme:</span></span>
+<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a><span class="co">      - cosmo</span></span>
+<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a><span class="co">      - cerulean</span></span>
+<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a><span class="co">    callout-icon: false</span></span>
+<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a><span class="an">jupyter:</span></span>
+<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a><span class="co">  jupytext:</span></span>
+<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a><span class="co">    text_representation:</span></span>
+<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a><span class="co">      extension: .qmd</span></span>
+<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a><span class="co">      format_name: quarto</span></span>
+<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a><span class="co">      format_version: '1.0'</span></span>
+<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a><span class="co">      jupytext_version: 1.16.1</span></span>
+<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a><span class="co">  kernelspec:</span></span>
+<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a><span class="co">    display_name: Python 3 (ipykernel)</span></span>
+<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a><span class="co">    language: python</span></span>
+<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a><span class="co">    name: python3</span></span>
+<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a>::: {.callout-note collapse="false"}</span>
+<span id="cb28-30"><a href="#cb28-30" aria-hidden="true" tabindex="-1"></a><span class="fu">## Learning Outcomes</span></span>
+<span id="cb28-31"><a href="#cb28-31" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform aggregations using <span class="in">`GROUP BY`</span></span>
+<span id="cb28-32"><a href="#cb28-32" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Introduce the ability to filter groups</span>
+<span id="cb28-33"><a href="#cb28-33" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Perform data cleaning and text manipulation in SQL</span>
+<span id="cb28-34"><a href="#cb28-34" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span>Join data across tables</span>
+<span id="cb28-35"><a href="#cb28-35" aria-hidden="true" tabindex="-1"></a>:::</span>
+<span id="cb28-36"><a href="#cb28-36" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-37"><a href="#cb28-37" aria-hidden="true" tabindex="-1"></a>In this lecture, we'll continue our work from last time to introduce some advanced SQL syntax. </span>
+<span id="cb28-38"><a href="#cb28-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-39"><a href="#cb28-39" aria-hidden="true" tabindex="-1"></a>First, let's load in the <span class="in">`basic_examples.db`</span> database.</span>
+<span id="cb28-40"><a href="#cb28-40" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-43"><a href="#cb28-43" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-44"><a href="#cb28-44" aria-hidden="true" tabindex="-1"></a><span class="co">#| code-fold: true</span></span>
+<span id="cb28-45"><a href="#cb28-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Load the SQL Alchemy Python library and DuckDB</span></span>
+<span id="cb28-46"><a href="#cb28-46" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sqlalchemy</span>
+<span id="cb28-47"><a href="#cb28-47" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> duckdb</span>
+<span id="cb28-48"><a href="#cb28-48" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-49"><a href="#cb28-49" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-52"><a href="#cb28-52" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-53"><a href="#cb28-53" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-54"><a href="#cb28-54" aria-hidden="true" tabindex="-1"></a><span class="co"># Load %%sql cell magic</span></span>
+<span id="cb28-55"><a href="#cb28-55" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>load_ext sql</span>
+<span id="cb28-56"><a href="#cb28-56" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-57"><a href="#cb28-57" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-60"><a href="#cb28-60" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-61"><a href="#cb28-61" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-62"><a href="#cb28-62" aria-hidden="true" tabindex="-1"></a><span class="co"># Connect to the database</span></span>
+<span id="cb28-63"><a href="#cb28-63" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql duckdb:<span class="op">///</span>data<span class="op">/</span>basic_examples.db <span class="op">--</span>alias basic</span>
+<span id="cb28-64"><a href="#cb28-64" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-65"><a href="#cb28-65" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-66"><a href="#cb28-66" aria-hidden="true" tabindex="-1"></a><span class="fu">## Aggregating with `GROUP BY`</span></span>
+<span id="cb28-67"><a href="#cb28-67" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-68"><a href="#cb28-68" aria-hidden="true" tabindex="-1"></a>At this point, we've seen that SQL offers much of the same functionality that was given to us by <span class="in">`pandas`</span>. We can extract data from a table, filter it, and reorder it to suit our needs.</span>
+<span id="cb28-69"><a href="#cb28-69" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-70"><a href="#cb28-70" aria-hidden="true" tabindex="-1"></a>In <span class="in">`pandas`</span>, much of our analysis work relied heavily on being able to use <span class="in">`.groupby()`</span> to aggregate across the rows of our dataset. SQL's answer to this task is the (very conveniently named) <span class="in">`GROUP BY`</span> clause. While the outputs of <span class="in">`GROUP BY`</span> are similar to those of <span class="in">`.groupby()`</span> —— in both cases, we obtain an output table where some column has been used for grouping —— the syntax and logic used to group data in SQL are fairly different to the <span class="in">`pandas`</span> implementation.</span>
+<span id="cb28-71"><a href="#cb28-71" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-72"><a href="#cb28-72" aria-hidden="true" tabindex="-1"></a>To illustrate <span class="in">`GROUP BY`</span>, we will consider the <span class="in">`Dish`</span> table from our database.</span>
+<span id="cb28-73"><a href="#cb28-73" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-76"><a href="#cb28-76" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-77"><a href="#cb28-77" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-78"><a href="#cb28-78" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-79"><a href="#cb28-79" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> </span>
+<span id="cb28-80"><a href="#cb28-80" aria-hidden="true" tabindex="-1"></a>FROM Dish<span class="op">;</span></span>
+<span id="cb28-81"><a href="#cb28-81" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-82"><a href="#cb28-82" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-83"><a href="#cb28-83" aria-hidden="true" tabindex="-1"></a>Notice that there are multiple dishes of the same <span class="in">`type`</span>. What if we wanted to find the total costs of dishes of a certain <span class="in">`type`</span>? To accomplish this, we would write the following code.</span>
+<span id="cb28-84"><a href="#cb28-84" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-87"><a href="#cb28-87" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-88"><a href="#cb28-88" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-89"><a href="#cb28-89" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-90"><a href="#cb28-90" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, SUM(cost)</span>
+<span id="cb28-91"><a href="#cb28-91" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
+<span id="cb28-92"><a href="#cb28-92" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span><span class="op">;</span></span>
+<span id="cb28-93"><a href="#cb28-93" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-94"><a href="#cb28-94" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-95"><a href="#cb28-95" aria-hidden="true" tabindex="-1"></a>What is going on here? The statement <span class="in">`GROUP BY type`</span> tells SQL to group the data based on the value contained in the <span class="in">`type`</span> column (whether a record is an appetizer, entree, or dessert). <span class="in">`SUM(cost)`</span> sums up the costs of dishes in each <span class="in">`type`</span> and displays the result in the output table.</span>
+<span id="cb28-96"><a href="#cb28-96" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-97"><a href="#cb28-97" aria-hidden="true" tabindex="-1"></a>You may be wondering: why does <span class="in">`SUM(cost)`</span> come before the command to <span class="in">`GROUP BY type`</span>? Don't we need to form groups before we can count the number of entries in each? Remember that SQL is a *declarative* programming language —— a SQL programmer simply states what end result they would like to see, and leaves the task of figuring out *how* to obtain this result to SQL itself. This means that SQL queries sometimes don't follow what a reader sees as a "logical" sequence of thought. Instead, SQL requires that we follow its set order of operations when constructing queries. So long as we follow this order, SQL will handle the underlying logic.</span>
+<span id="cb28-98"><a href="#cb28-98" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-99"><a href="#cb28-99" aria-hidden="true" tabindex="-1"></a>In practical terms: our goal with this query was to output the total <span class="in">`cost`</span>s of each <span class="in">`type`</span>. To communicate this to SQL, we say that we want to <span class="in">`SELECT`</span> the <span class="in">`SUM`</span>med <span class="in">`cost`</span> values for each <span class="in">`type`</span> group. </span>
+<span id="cb28-100"><a href="#cb28-100" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-101"><a href="#cb28-101" aria-hidden="true" tabindex="-1"></a>There are many aggregation functions that can be used to aggregate the data contained in each group. Some common examples are:</span>
+<span id="cb28-102"><a href="#cb28-102" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-103"><a href="#cb28-103" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`COUNT`</span>: count the number of rows associated with each group</span>
+<span id="cb28-104"><a href="#cb28-104" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`MIN`</span>: find the minimum value of each group</span>
+<span id="cb28-105"><a href="#cb28-105" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`MAX`</span>: find the maximum value of each group</span>
+<span id="cb28-106"><a href="#cb28-106" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`SUM`</span>: sum across all records in each group</span>
+<span id="cb28-107"><a href="#cb28-107" aria-hidden="true" tabindex="-1"></a><span class="ss">* </span><span class="in">`AVG`</span>: find the average value of each group</span>
+<span id="cb28-108"><a href="#cb28-108" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-109"><a href="#cb28-109" aria-hidden="true" tabindex="-1"></a>We can easily compute multiple aggregations all at once (a task that was very tricky in <span class="in">`pandas`</span>).</span>
+<span id="cb28-110"><a href="#cb28-110" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-113"><a href="#cb28-113" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-114"><a href="#cb28-114" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-115"><a href="#cb28-115" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-116"><a href="#cb28-116" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, SUM(cost), MIN(cost), MAX(name)</span>
+<span id="cb28-117"><a href="#cb28-117" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
+<span id="cb28-118"><a href="#cb28-118" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span><span class="op">;</span></span>
+<span id="cb28-119"><a href="#cb28-119" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-120"><a href="#cb28-120" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-121"><a href="#cb28-121" aria-hidden="true" tabindex="-1"></a>To count the number of rows associated with each group, we use the <span class="in">`COUNT`</span> keyword. Calling <span class="in">`COUNT(*)`</span> will compute the total number of rows in each group, including rows with null values. Its <span class="in">`pandas`</span> equivalent is <span class="in">`.groupby().size()`</span>.</span>
+<span id="cb28-122"><a href="#cb28-122" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-123"><a href="#cb28-123" aria-hidden="true" tabindex="-1"></a>Recall the <span class="in">`Dragon`</span> table from the previous lecture:</span>
+<span id="cb28-124"><a href="#cb28-124" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-127"><a href="#cb28-127" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-128"><a href="#cb28-128" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-129"><a href="#cb28-129" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-130"><a href="#cb28-130" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span> FROM Dragon<span class="op">;</span></span>
+<span id="cb28-131"><a href="#cb28-131" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-132"><a href="#cb28-132" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-133"><a href="#cb28-133" aria-hidden="true" tabindex="-1"></a>Notice that <span class="in">`COUNT(*)`</span> and <span class="in">`COUNT(cute)`</span> result in different outputs.</span>
+<span id="cb28-134"><a href="#cb28-134" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-137"><a href="#cb28-137" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-138"><a href="#cb28-138" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-139"><a href="#cb28-139" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-140"><a href="#cb28-140" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(<span class="op">*</span>)</span>
+<span id="cb28-141"><a href="#cb28-141" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb28-142"><a href="#cb28-142" aria-hidden="true" tabindex="-1"></a>GROUP BY year<span class="op">;</span></span>
+<span id="cb28-143"><a href="#cb28-143" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-144"><a href="#cb28-144" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-147"><a href="#cb28-147" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-148"><a href="#cb28-148" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-149"><a href="#cb28-149" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-150"><a href="#cb28-150" aria-hidden="true" tabindex="-1"></a>SELECT year, COUNT(cute)</span>
+<span id="cb28-151"><a href="#cb28-151" aria-hidden="true" tabindex="-1"></a>FROM Dragon</span>
+<span id="cb28-152"><a href="#cb28-152" aria-hidden="true" tabindex="-1"></a>GROUP BY year<span class="op">;</span></span>
+<span id="cb28-153"><a href="#cb28-153" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-154"><a href="#cb28-154" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-155"><a href="#cb28-155" aria-hidden="true" tabindex="-1"></a>With this definition of <span class="in">`GROUP BY`</span> in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
+<span id="cb28-156"><a href="#cb28-156" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-157"><a href="#cb28-157" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
+<span id="cb28-158"><a href="#cb28-158" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
+<span id="cb28-159"><a href="#cb28-159" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
+<span id="cb28-160"><a href="#cb28-160" aria-hidden="true" tabindex="-1"></a><span class="in">    [GROUP BY &lt;column list&gt;]</span></span>
+<span id="cb28-161"><a href="#cb28-161" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
+<span id="cb28-162"><a href="#cb28-162" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
+<span id="cb28-163"><a href="#cb28-163" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
+<span id="cb28-164"><a href="#cb28-164" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-165"><a href="#cb28-165" aria-hidden="true" tabindex="-1"></a>Note that we can use the <span class="in">`AS`</span> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<span class="in">`MAX`</span>, <span class="in">`MIN`</span>, etc.).</span>
+<span id="cb28-166"><a href="#cb28-166" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-167"><a href="#cb28-167" aria-hidden="true" tabindex="-1"></a><span class="fu">## Filtering Groups</span></span>
+<span id="cb28-168"><a href="#cb28-168" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-169"><a href="#cb28-169" aria-hidden="true" tabindex="-1"></a>Now, what if we only want groups that meet a certain condition? <span class="in">`HAVING`</span> filters groups by applying some condition across all rows in each group. We interpret it as a way to keep only the groups <span class="in">`HAVING`</span> some condition. Note the difference between <span class="in">`WHERE`</span> and <span class="in">`HAVING`</span>: we use <span class="in">`WHERE`</span> to filter rows, whereas we use <span class="in">`HAVING`</span> to filter *groups*. <span class="in">`WHERE`</span> precedes <span class="in">`HAVING`</span> in terms of how SQL executes a query.</span>
+<span id="cb28-170"><a href="#cb28-170" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-171"><a href="#cb28-171" aria-hidden="true" tabindex="-1"></a>Let's take a look at the <span class="in">`Dish`</span> table to see how we can use <span class="in">`HAVING`</span>. Say we want to group dishes with a cost greater than 4 by <span class="in">`type`</span> and only keep groups where the max cost is less than 10.</span>
+<span id="cb28-172"><a href="#cb28-172" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-175"><a href="#cb28-175" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-176"><a href="#cb28-176" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-177"><a href="#cb28-177" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-178"><a href="#cb28-178" aria-hidden="true" tabindex="-1"></a>SELECT <span class="bu">type</span>, COUNT(<span class="op">*</span>)</span>
+<span id="cb28-179"><a href="#cb28-179" aria-hidden="true" tabindex="-1"></a>FROM Dish</span>
+<span id="cb28-180"><a href="#cb28-180" aria-hidden="true" tabindex="-1"></a>WHERE cost <span class="op">&gt;</span> <span class="dv">4</span></span>
+<span id="cb28-181"><a href="#cb28-181" aria-hidden="true" tabindex="-1"></a>GROUP BY <span class="bu">type</span></span>
+<span id="cb28-182"><a href="#cb28-182" aria-hidden="true" tabindex="-1"></a>HAVING MAX(cost) <span class="op">&lt;</span>  <span class="dv">10</span><span class="op">;</span></span>
+<span id="cb28-183"><a href="#cb28-183" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-184"><a href="#cb28-184" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-185"><a href="#cb28-185" aria-hidden="true" tabindex="-1"></a>Here, we first use <span class="in">`WHERE`</span> to filter for rows with a cost greater than 4. We then group our values by <span class="in">`type`</span> before applying the <span class="in">`HAVING`</span> operator. With <span class="in">`HAVING`</span>, we can filter our groups based on if the max cost is less than 10.</span>
+<span id="cb28-186"><a href="#cb28-186" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-187"><a href="#cb28-187" aria-hidden="true" tabindex="-1"></a><span class="fu">## Summary: SQL</span></span>
+<span id="cb28-188"><a href="#cb28-188" aria-hidden="true" tabindex="-1"></a>With this definition of <span class="in">`GROUP BY`</span> and <span class="in">`HAVING`</span> in hand, let's update our SQL order of operations. Remember: *every* SQL query must list clauses in this order. </span>
+<span id="cb28-189"><a href="#cb28-189" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-190"><a href="#cb28-190" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column expression list&gt;</span></span>
+<span id="cb28-191"><a href="#cb28-191" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM &lt;table&gt;</span></span>
+<span id="cb28-192"><a href="#cb28-192" aria-hidden="true" tabindex="-1"></a><span class="in">    [WHERE &lt;predicate&gt;]</span></span>
+<span id="cb28-193"><a href="#cb28-193" aria-hidden="true" tabindex="-1"></a><span class="in">    [GROUP BY &lt;column list&gt;]</span></span>
+<span id="cb28-194"><a href="#cb28-194" aria-hidden="true" tabindex="-1"></a><span class="in">    [ORDER BY &lt;column list&gt;]</span></span>
+<span id="cb28-195"><a href="#cb28-195" aria-hidden="true" tabindex="-1"></a><span class="in">    [LIMIT &lt;number of rows&gt;]</span></span>
+<span id="cb28-196"><a href="#cb28-196" aria-hidden="true" tabindex="-1"></a><span class="in">    [OFFSET &lt;number of rows&gt;];</span></span>
+<span id="cb28-197"><a href="#cb28-197" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-198"><a href="#cb28-198" aria-hidden="true" tabindex="-1"></a>Note that we can use the <span class="in">`AS`</span> keyword to rename columns during the selection process and that column expressions may include aggregation functions (<span class="in">`MAX`</span>, <span class="in">`MIN`</span>, etc.).</span>
+<span id="cb28-199"><a href="#cb28-199" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-200"><a href="#cb28-200" aria-hidden="true" tabindex="-1"></a><span class="fu">## EDA in SQL</span></span>
+<span id="cb28-201"><a href="#cb28-201" aria-hidden="true" tabindex="-1"></a>In the last lecture, we mostly worked under the assumption that our data had already been cleaned. However, as we saw in our first pass through the data science lifecycle, we're very unlikely to be given data that is free of formatting issues. With this in mind, we'll want to learn how to clean and transform data in SQL. </span>
+<span id="cb28-202"><a href="#cb28-202" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-203"><a href="#cb28-203" aria-hidden="true" tabindex="-1"></a>Our typical workflow when working with "big data" is:</span>
+<span id="cb28-204"><a href="#cb28-204" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-205"><a href="#cb28-205" aria-hidden="true" tabindex="-1"></a><span class="ss">1. </span>Use SQL to query data from a database</span>
+<span id="cb28-206"><a href="#cb28-206" aria-hidden="true" tabindex="-1"></a><span class="ss">2. </span>Use Python (with <span class="in">`pandas`</span>) to analyze this data in detail</span>
+<span id="cb28-207"><a href="#cb28-207" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-208"><a href="#cb28-208" aria-hidden="true" tabindex="-1"></a>We can, however, still perform simple data cleaning and re-structuring using SQL directly. To do so, we'll use the <span class="in">`Title`</span> table from the <span class="in">`imdb_duck`</span> database, which contains information about movies and actors.</span>
+<span id="cb28-209"><a href="#cb28-209" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-210"><a href="#cb28-210" aria-hidden="true" tabindex="-1"></a>Let's load in the <span class="in">`imdb_duck`</span> database.</span>
+<span id="cb28-211"><a href="#cb28-211" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-214"><a href="#cb28-214" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-215"><a href="#cb28-215" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-216"><a href="#cb28-216" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
+<span id="cb28-217"><a href="#cb28-217" aria-hidden="true" tabindex="-1"></a>os.environ[<span class="st">"TQDM_DISABLE"</span>] <span class="op">=</span> <span class="st">"1"</span></span>
+<span id="cb28-218"><a href="#cb28-218" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> os.path.exists(<span class="st">"/home/jovyan/shared/sql/imdb_duck.db"</span>):</span>
+<span id="cb28-219"><a href="#cb28-219" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:////home/jovyan/shared/sql/imdb_duck.db"</span></span>
+<span id="cb28-220"><a href="#cb28-220" aria-hidden="true" tabindex="-1"></a><span class="cf">elif</span> os.path.exists(<span class="st">"data/imdb_duck.db"</span>):</span>
+<span id="cb28-221"><a href="#cb28-221" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span>  <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
+<span id="cb28-222"><a href="#cb28-222" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
+<span id="cb28-223"><a href="#cb28-223" aria-hidden="true" tabindex="-1"></a>    <span class="im">import</span> gdown</span>
+<span id="cb28-224"><a href="#cb28-224" aria-hidden="true" tabindex="-1"></a>    url <span class="op">=</span> <span class="st">'https://drive.google.com/uc?id=10tKOHGLt9QoOgq5Ii-FhxpB9lDSQgl1O'</span></span>
+<span id="cb28-225"><a href="#cb28-225" aria-hidden="true" tabindex="-1"></a>    output_path <span class="op">=</span> <span class="st">'data/imdb_duck.db'</span></span>
+<span id="cb28-226"><a href="#cb28-226" aria-hidden="true" tabindex="-1"></a>    gdown.download(url, output_path, quiet<span class="op">=</span><span class="va">False</span>)</span>
+<span id="cb28-227"><a href="#cb28-227" aria-hidden="true" tabindex="-1"></a>    imdbpath <span class="op">=</span> <span class="st">"duckdb:///data/imdb_duck.db"</span></span>
+<span id="cb28-228"><a href="#cb28-228" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-229"><a href="#cb28-229" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-232"><a href="#cb28-232" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-233"><a href="#cb28-233" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-234"><a href="#cb28-234" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sqlalchemy <span class="im">import</span> create_engine</span>
+<span id="cb28-235"><a href="#cb28-235" aria-hidden="true" tabindex="-1"></a>imdb_engine <span class="op">=</span> create_engine(imdbpath, connect_args<span class="op">=</span>{<span class="st">'read_only'</span>: <span class="va">True</span>})</span>
+<span id="cb28-236"><a href="#cb28-236" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>sql imdb_engine <span class="op">--</span>alias imdb</span>
+<span id="cb28-237"><a href="#cb28-237" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-238"><a href="#cb28-238" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-239"><a href="#cb28-239" aria-hidden="true" tabindex="-1"></a>Since we'll be working with the <span class="in">`Title`</span> table, let's take a quick look at what it contains. </span>
+<span id="cb28-240"><a href="#cb28-240" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-243"><a href="#cb28-243" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-244"><a href="#cb28-244" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-245"><a href="#cb28-245" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql imdb </span>
+<span id="cb28-246"><a href="#cb28-246" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb28-247"><a href="#cb28-247" aria-hidden="true" tabindex="-1"></a>SELECT <span class="op">*</span></span>
+<span id="cb28-248"><a href="#cb28-248" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb28-249"><a href="#cb28-249" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle IN (<span class="st">'Ginny &amp; Georgia'</span>, <span class="st">'What If...?'</span>, <span class="st">'Succession'</span>, <span class="st">'Veep'</span>, <span class="st">'Tenet'</span>)</span>
+<span id="cb28-250"><a href="#cb28-250" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
+<span id="cb28-251"><a href="#cb28-251" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-252"><a href="#cb28-252" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-253"><a href="#cb28-253" aria-hidden="true" tabindex="-1"></a><span class="fu">### Matching Text using `LIKE`</span></span>
+<span id="cb28-254"><a href="#cb28-254" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-255"><a href="#cb28-255" aria-hidden="true" tabindex="-1"></a>One common task we encountered in our first look at EDA was needing to match string data. For example, we might want to remove entries beginning with the same prefix as part of the data cleaning process.</span>
+<span id="cb28-256"><a href="#cb28-256" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-257"><a href="#cb28-257" aria-hidden="true" tabindex="-1"></a>In SQL, we use the <span class="in">`LIKE`</span> operator to (you guessed it) look for strings that are *like* a given string pattern. </span>
+<span id="cb28-258"><a href="#cb28-258" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-261"><a href="#cb28-261" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-262"><a href="#cb28-262" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-263"><a href="#cb28-263" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-264"><a href="#cb28-264" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb28-265"><a href="#cb28-265" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb28-266"><a href="#cb28-266" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'Star Wars: Episode I - The Phantom Menace'</span></span>
+<span id="cb28-267"><a href="#cb28-267" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-268"><a href="#cb28-268" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-269"><a href="#cb28-269" aria-hidden="true" tabindex="-1"></a>What if we wanted to find *all* Star Wars movies? <span class="in">`%`</span> is the wildcard operator, it means "look for any character, any number of times". This makes it helpful for identifying strings that are similar to our desired pattern, even when we don't know the full text of what we aim to extract.</span>
+<span id="cb28-270"><a href="#cb28-270" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-273"><a href="#cb28-273" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-274"><a href="#cb28-274" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-275"><a href="#cb28-275" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-276"><a href="#cb28-276" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb28-277"><a href="#cb28-277" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb28-278"><a href="#cb28-278" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle LIKE <span class="st">'%Star Wars%'</span></span>
+<span id="cb28-279"><a href="#cb28-279" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
+<span id="cb28-280"><a href="#cb28-280" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-281"><a href="#cb28-281" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-282"><a href="#cb28-282" aria-hidden="true" tabindex="-1"></a>Alternatively, we can use RegEx! DuckDB and most real DBMSs allow for this. Note that here, we have to use the <span class="in">`SIMILAR TO`</span> operater rather than <span class="in">`LIKE`</span>.</span>
+<span id="cb28-283"><a href="#cb28-283" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-286"><a href="#cb28-286" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-287"><a href="#cb28-287" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-288"><a href="#cb28-288" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-289"><a href="#cb28-289" aria-hidden="true" tabindex="-1"></a>SELECT titleType, primaryTitle</span>
+<span id="cb28-290"><a href="#cb28-290" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb28-291"><a href="#cb28-291" aria-hidden="true" tabindex="-1"></a>WHERE primaryTitle SIMILAR TO <span class="st">'.*Star Wars*.'</span></span>
+<span id="cb28-292"><a href="#cb28-292" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
+<span id="cb28-293"><a href="#cb28-293" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-294"><a href="#cb28-294" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-295"><a href="#cb28-295" aria-hidden="true" tabindex="-1"></a><span class="fu">### `CAST`ing Data Types</span></span>
+<span id="cb28-296"><a href="#cb28-296" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-297"><a href="#cb28-297" aria-hidden="true" tabindex="-1"></a>A common data cleaning task is converting data to the correct variable type. The <span class="in">`CAST`</span> keyword is used to generate a new output column. Each entry in this output column is the result of converting the data in an existing column to a new data type. For example, we may wish to convert numeric data stored as a string to an integer.</span>
+<span id="cb28-298"><a href="#cb28-298" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-301"><a href="#cb28-301" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-302"><a href="#cb28-302" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-303"><a href="#cb28-303" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-304"><a href="#cb28-304" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, CAST(runtimeMinutes AS INT)</span>
+<span id="cb28-305"><a href="#cb28-305" aria-hidden="true" tabindex="-1"></a>FROM Title<span class="op">;</span></span>
+<span id="cb28-306"><a href="#cb28-306" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-307"><a href="#cb28-307" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-308"><a href="#cb28-308" aria-hidden="true" tabindex="-1"></a>We use <span class="in">`CAST`</span> when <span class="in">`SELECT`</span>ing colunns for our output table. In the example above, we want to <span class="in">`SELECT`</span> the columns of integer year and runtime data that is created by the <span class="in">`CAST`</span>. </span>
+<span id="cb28-309"><a href="#cb28-309" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-310"><a href="#cb28-310" aria-hidden="true" tabindex="-1"></a>SQL will automatically name a new column according to the command used to <span class="in">`SELECT`</span> it, which can lead to unwieldy column names. We can rename the <span class="in">`CAST`</span>ed column using the <span class="in">`AS`</span> keyword.</span>
+<span id="cb28-311"><a href="#cb28-311" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-314"><a href="#cb28-314" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-315"><a href="#cb28-315" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-316"><a href="#cb28-316" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-317"><a href="#cb28-317" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle AS title, CAST(runtimeMinutes AS INT) AS minutes, CAST(startYear AS INT) AS year</span>
+<span id="cb28-318"><a href="#cb28-318" aria-hidden="true" tabindex="-1"></a>FROM Title</span>
+<span id="cb28-319"><a href="#cb28-319" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">5</span><span class="op">;</span></span>
+<span id="cb28-320"><a href="#cb28-320" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-321"><a href="#cb28-321" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-322"><a href="#cb28-322" aria-hidden="true" tabindex="-1"></a><span class="fu">### Using Conditional Statements with `CASE`</span></span>
+<span id="cb28-323"><a href="#cb28-323" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-324"><a href="#cb28-324" aria-hidden="true" tabindex="-1"></a>When working with <span class="in">`pandas`</span>, we often ran into situations where we wanted to generate new columns using some form of conditional statement. For example, say we wanted to describe a film title as "old," "mid-aged," or "new," depending on the year of its release.</span>
+<span id="cb28-325"><a href="#cb28-325" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-326"><a href="#cb28-326" aria-hidden="true" tabindex="-1"></a>In SQL, conditional operations are performed using a <span class="in">`CASE`</span> clause. Conceptually, <span class="in">`CASE`</span> behaves much like the <span class="in">`CAST`</span> operation: it creates a new column that we can then <span class="in">`SELECT`</span> to appear in the output. The syntax for a <span class="in">`CASE`</span> clause is as follows:</span>
+<span id="cb28-327"><a href="#cb28-327" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-328"><a href="#cb28-328" aria-hidden="true" tabindex="-1"></a><span class="in">    CASE WHEN &lt;condition&gt; THEN &lt;value&gt;</span></span>
+<span id="cb28-329"><a href="#cb28-329" aria-hidden="true" tabindex="-1"></a><span class="in">         WHEN &lt;other condition&gt; THEN &lt;other value&gt;</span></span>
+<span id="cb28-330"><a href="#cb28-330" aria-hidden="true" tabindex="-1"></a><span class="in">         ...</span></span>
+<span id="cb28-331"><a href="#cb28-331" aria-hidden="true" tabindex="-1"></a><span class="in">         ELSE &lt;yet another value&gt;</span></span>
+<span id="cb28-332"><a href="#cb28-332" aria-hidden="true" tabindex="-1"></a><span class="in">         END</span></span>
+<span id="cb28-333"><a href="#cb28-333" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-334"><a href="#cb28-334" aria-hidden="true" tabindex="-1"></a>Scanning through the skeleton code above, you can see that the logic is similar to that of an <span class="in">`if`</span> statement in Python. The conditional statement is first opened by calling <span class="in">`CASE`</span>. Each new condition is specified by <span class="in">`WHEN`</span>, with <span class="in">`THEN`</span> indicating what value should be filled if the condition is met. <span class="in">`ELSE`</span> specifies the value that should be filled if no other conditions are met. Lastly, <span class="in">`END`</span> indicates the end of the conditional statement; once <span class="in">`END`</span> has been called, SQL will continue evaluating the query as usual. </span>
+<span id="cb28-335"><a href="#cb28-335" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-336"><a href="#cb28-336" aria-hidden="true" tabindex="-1"></a>Let's see this in action. In the example below, we give the new column created by the <span class="in">`CASE`</span> statement the name <span class="in">`movie_age`</span>.</span>
+<span id="cb28-337"><a href="#cb28-337" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-340"><a href="#cb28-340" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-341"><a href="#cb28-341" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-342"><a href="#cb28-342" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-343"><a href="#cb28-343" aria-hidden="true" tabindex="-1"></a><span class="op">/*</span> If a movie was filmed before <span class="dv">1950</span>, it <span class="kw">is</span> <span class="st">"old"</span></span>
+<span id="cb28-344"><a href="#cb28-344" aria-hidden="true" tabindex="-1"></a>Otherwise, <span class="cf">if</span> a movie was filmed before <span class="dv">2000</span>, it <span class="kw">is</span> <span class="st">"mid-aged"</span></span>
+<span id="cb28-345"><a href="#cb28-345" aria-hidden="true" tabindex="-1"></a>Else, a movie <span class="kw">is</span> <span class="st">"new"</span> <span class="op">*/</span></span>
+<span id="cb28-346"><a href="#cb28-346" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-347"><a href="#cb28-347" aria-hidden="true" tabindex="-1"></a>SELECT titleType, startYear,</span>
+<span id="cb28-348"><a href="#cb28-348" aria-hidden="true" tabindex="-1"></a>CASE WHEN startYear <span class="op">&lt;</span> <span class="dv">1950</span> THEN <span class="st">'old'</span></span>
+<span id="cb28-349"><a href="#cb28-349" aria-hidden="true" tabindex="-1"></a>     WHEN startYear <span class="op">&lt;</span> <span class="dv">2000</span> THEN <span class="st">'mid-aged'</span></span>
+<span id="cb28-350"><a href="#cb28-350" aria-hidden="true" tabindex="-1"></a>     ELSE <span class="st">'new'</span></span>
+<span id="cb28-351"><a href="#cb28-351" aria-hidden="true" tabindex="-1"></a>     END AS movie_age</span>
+<span id="cb28-352"><a href="#cb28-352" aria-hidden="true" tabindex="-1"></a>FROM Title<span class="op">;</span></span>
+<span id="cb28-353"><a href="#cb28-353" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-354"><a href="#cb28-354" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-355"><a href="#cb28-355" aria-hidden="true" tabindex="-1"></a><span class="fu">## `JOIN`ing Tables</span></span>
+<span id="cb28-356"><a href="#cb28-356" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-357"><a href="#cb28-357" aria-hidden="true" tabindex="-1"></a>At this point, we're well-versed in using SQL as a tool to clean, manipulate, and transform data in a table. Notice that this sentence referred to one *table*, specifically. What happens if the data we need is distributed across multiple tables? This is an important consideration when using SQL —— recall that we first introduced SQL as a language to query from databases. Databases often store data in a multidimensional structure. In other words, information is stored across several tables, with each table containing a small subset of all the data housed by the database. </span>
+<span id="cb28-358"><a href="#cb28-358" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-359"><a href="#cb28-359" aria-hidden="true" tabindex="-1"></a>A common way of organizing a database is by using a **star schema**. A star schema is composed of two types of tables. A **fact table** is the central table of the database —— it contains the information needed to link entries across several **dimension tables**, which contain more detailed information about the data. </span>
+<span id="cb28-360"><a href="#cb28-360" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-361"><a href="#cb28-361" aria-hidden="true" tabindex="-1"></a>Say we were working with a database about boba offerings in Berkeley. The dimension tables of the database might contain information about tea varieties and boba toppings. The fact table would be used to link this information across the various dimension tables.</span>
+<span id="cb28-362"><a href="#cb28-362" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-363"><a href="#cb28-363" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-364"><a href="#cb28-364" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/multidimensional.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'multidimensional'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'850'</span><span class="kw">&gt;</span></span>
+<span id="cb28-365"><a href="#cb28-365" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-366"><a href="#cb28-366" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-367"><a href="#cb28-367" aria-hidden="true" tabindex="-1"></a>If we explicitly mark the relationships between tables, we start to see the star-like structure of the star schema.</span>
+<span id="cb28-368"><a href="#cb28-368" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-369"><a href="#cb28-369" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-370"><a href="#cb28-370" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/star.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'star'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'650'</span><span class="kw">&gt;</span></span>
+<span id="cb28-371"><a href="#cb28-371" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-372"><a href="#cb28-372" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-373"><a href="#cb28-373" aria-hidden="true" tabindex="-1"></a>To join data across multiple tables, we'll use the (creatively named) <span class="in">`JOIN`</span> keyword. We'll make things easier for now by first considering the simpler <span class="in">`cats`</span> dataset, which consists of the tables <span class="in">`s`</span> and <span class="in">`t`</span>.</span>
+<span id="cb28-374"><a href="#cb28-374" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-375"><a href="#cb28-375" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-376"><a href="#cb28-376" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/cats.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'cats'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'500'</span><span class="kw">&gt;</span></span>
+<span id="cb28-377"><a href="#cb28-377" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-378"><a href="#cb28-378" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-379"><a href="#cb28-379" aria-hidden="true" tabindex="-1"></a>To perform a join, we amend the <span class="in">`FROM`</span> clause. You can think of this as saying, "<span class="in">`SELECT`</span> my data <span class="in">`FROM`</span> tables that have  been <span class="in">`JOIN`</span>ed together." </span>
+<span id="cb28-380"><a href="#cb28-380" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-381"><a href="#cb28-381" aria-hidden="true" tabindex="-1"></a>Remember: SQL does not consider newlines or whitespace when interpreting queries. The indentation given in the example below is to help improve readability. If you wish, you can write code that does not follow this formatting.</span>
+<span id="cb28-382"><a href="#cb28-382" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-383"><a href="#cb28-383" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT &lt;column list&gt;</span></span>
+<span id="cb28-384"><a href="#cb28-384" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM table_1 </span></span>
+<span id="cb28-385"><a href="#cb28-385" aria-hidden="true" tabindex="-1"></a><span class="in">        JOIN table_2 </span></span>
+<span id="cb28-386"><a href="#cb28-386" aria-hidden="true" tabindex="-1"></a><span class="in">        ON key_1 = key_2;</span></span>
+<span id="cb28-387"><a href="#cb28-387" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-388"><a href="#cb28-388" aria-hidden="true" tabindex="-1"></a>We also need to specify what column from each table should be used to determine matching entries. By defining these keys, we provide SQL with the information it needs to pair rows of data together.</span>
+<span id="cb28-389"><a href="#cb28-389" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-390"><a href="#cb28-390" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-391"><a href="#cb28-391" aria-hidden="true" tabindex="-1"></a>The most commonly used type of SQL <span class="in">`JOIN`</span> is the **inner join**. It turns out you're already familiar with what an inner join does, and how it works – this is the type of join we've been using in <span class="in">`pandas`</span> all along! In an inner join, we combine every row in our first table with its matching entry in the second table. If a row from either table does not have a match in the other table, it is omitted from the output. </span>
+<span id="cb28-392"><a href="#cb28-392" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-393"><a href="#cb28-393" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-394"><a href="#cb28-394" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/inner.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'inner'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
+<span id="cb28-395"><a href="#cb28-395" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-396"><a href="#cb28-396" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-397"><a href="#cb28-397" aria-hidden="true" tabindex="-1"></a>In a **cross join**, *all* possible combinations of rows appear in the output table, regardless of whether or not rows share a matching key. Because all rows are joined, even if there is no matching key, it is not necessary to specify what keys to consider in an <span class="in">`ON`</span> statement. A cross join is also known as a cartesian product.</span>
+<span id="cb28-398"><a href="#cb28-398" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-399"><a href="#cb28-399" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-400"><a href="#cb28-400" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/cross.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'cross'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
+<span id="cb28-401"><a href="#cb28-401" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-402"><a href="#cb28-402" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-403"><a href="#cb28-403" aria-hidden="true" tabindex="-1"></a>Conceptually, we can interpret an inner join as a cross join, followed by removing all rows that do not share a matching key. Notice that the output of the inner join above contains all rows of the cross join example that contain a single color across the entire row.</span>
+<span id="cb28-404"><a href="#cb28-404" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-405"><a href="#cb28-405" aria-hidden="true" tabindex="-1"></a>In a **left outer join**, *all* rows in the left table are kept in the output table. If a row in the right table shares a match with the left table, this row will be kept; otherwise, the rows in the right table are omitted from the output. We can fill in any missing values with <span class="in">`NULL`</span>.</span>
+<span id="cb28-406"><a href="#cb28-406" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-407"><a href="#cb28-407" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-408"><a href="#cb28-408" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/left.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'left'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
+<span id="cb28-409"><a href="#cb28-409" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-410"><a href="#cb28-410" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-411"><a href="#cb28-411" aria-hidden="true" tabindex="-1"></a>A **right outer join** keeps all rows in the right table. Rows in the left table are only kept if they share a match in the right table. Again, we can fill in any missing values with <span class="in">`NULL`</span>. </span>
+<span id="cb28-412"><a href="#cb28-412" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-413"><a href="#cb28-413" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-414"><a href="#cb28-414" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/right.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'right'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
+<span id="cb28-415"><a href="#cb28-415" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-416"><a href="#cb28-416" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-417"><a href="#cb28-417" aria-hidden="true" tabindex="-1"></a>In a **full outer join**, all rows that have a match between the two tables are joined together. If a row has no match in the second table, then the values of the columns for that second table are filled with <span class="in">`NULL`</span>. In other words, a full outer join performs an inner join *while still keeping* rows that have no match in the other table. This is best understood visually:</span>
+<span id="cb28-418"><a href="#cb28-418" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-419"><a href="#cb28-419" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;div</span> <span class="er">style</span><span class="ot">=</span><span class="st">"text-align: center;"</span><span class="kw">&gt;</span></span>
+<span id="cb28-420"><a href="#cb28-420" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;img</span> <span class="er">src</span><span class="ot">=</span><span class="st">"images/full.png"</span> <span class="er">alt</span><span class="ot">=</span><span class="st">'full'</span> <span class="er">width</span><span class="ot">=</span><span class="st">'800'</span><span class="kw">&gt;</span></span>
+<span id="cb28-421"><a href="#cb28-421" aria-hidden="true" tabindex="-1"></a><span class="kw">&lt;/div&gt;</span> </span>
+<span id="cb28-422"><a href="#cb28-422" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-423"><a href="#cb28-423" aria-hidden="true" tabindex="-1"></a>We have kept the same output achieved using an inner join, with the addition of partially null rows for entries in <span class="in">`s`</span> and <span class="in">`t`</span> that had no match in the second table. </span>
+<span id="cb28-424"><a href="#cb28-424" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-425"><a href="#cb28-425" aria-hidden="true" tabindex="-1"></a><span class="fu">### Aliasing in `JOIN`s</span></span>
+<span id="cb28-426"><a href="#cb28-426" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-427"><a href="#cb28-427" aria-hidden="true" tabindex="-1"></a>When joining tables, we often create aliases for table names (similarly to what we did with column names in the last lecture). We do this as it is typically easier to refer to aliases, especially when we are working with long table names. We can even reference columns using aliased table names!</span>
+<span id="cb28-428"><a href="#cb28-428" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-429"><a href="#cb28-429" aria-hidden="true" tabindex="-1"></a>Let's say we want to determine the average rating of various movies. We'll need to <span class="in">`JOIN`</span> the <span class="in">`Title`</span> and <span class="in">`Rating`</span> tables and can create aliases for both tables.</span>
+<span id="cb28-430"><a href="#cb28-430" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-433"><a href="#cb28-433" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-434"><a href="#cb28-434" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-435"><a href="#cb28-435" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-436"><a href="#cb28-436" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-437"><a href="#cb28-437" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
+<span id="cb28-438"><a href="#cb28-438" aria-hidden="true" tabindex="-1"></a>FROM Title AS T INNER JOIN Rating AS R</span>
+<span id="cb28-439"><a href="#cb28-439" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<span class="op">;</span></span>
+<span id="cb28-440"><a href="#cb28-440" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-441"><a href="#cb28-441" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-442"><a href="#cb28-442" aria-hidden="true" tabindex="-1"></a>Note that the <span class="in">`AS`</span> is actually optional! We can create aliases for our tables even without it, but we usually include it for clarity.</span>
+<span id="cb28-443"><a href="#cb28-443" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-446"><a href="#cb28-446" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-447"><a href="#cb28-447" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-448"><a href="#cb28-448" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-449"><a href="#cb28-449" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-450"><a href="#cb28-450" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, averageRating</span>
+<span id="cb28-451"><a href="#cb28-451" aria-hidden="true" tabindex="-1"></a>FROM Title T INNER JOIN Rating R</span>
+<span id="cb28-452"><a href="#cb28-452" aria-hidden="true" tabindex="-1"></a>ON T.tconst <span class="op">=</span> R.tconst<span class="op">;</span></span>
+<span id="cb28-453"><a href="#cb28-453" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
+<span id="cb28-454"><a href="#cb28-454" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-455"><a href="#cb28-455" aria-hidden="true" tabindex="-1"></a><span class="fu">### Common Table Expressions</span></span>
+<span id="cb28-456"><a href="#cb28-456" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-457"><a href="#cb28-457" aria-hidden="true" tabindex="-1"></a>For more sophisticated data problems, the queries can become very complex. Common table expressions (CTEs) allow us to break down these complex queries into more manageable parts. To do so, we create temporary tables corresponding to different aspects of the problem and then reference them in the final query: </span>
+<span id="cb28-458"><a href="#cb28-458" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-459"><a href="#cb28-459" aria-hidden="true" tabindex="-1"></a><span class="in">    WITH </span></span>
+<span id="cb28-460"><a href="#cb28-460" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name1 AS ( </span></span>
+<span id="cb28-461"><a href="#cb28-461" aria-hidden="true" tabindex="-1"></a><span class="in">        SELECT ...</span></span>
+<span id="cb28-462"><a href="#cb28-462" aria-hidden="true" tabindex="-1"></a><span class="in">    ),</span></span>
+<span id="cb28-463"><a href="#cb28-463" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name2 AS ( </span></span>
+<span id="cb28-464"><a href="#cb28-464" aria-hidden="true" tabindex="-1"></a><span class="in">        SELECT ...</span></span>
+<span id="cb28-465"><a href="#cb28-465" aria-hidden="true" tabindex="-1"></a><span class="in">    )</span></span>
+<span id="cb28-466"><a href="#cb28-466" aria-hidden="true" tabindex="-1"></a><span class="in">    SELECT ... </span></span>
+<span id="cb28-467"><a href="#cb28-467" aria-hidden="true" tabindex="-1"></a><span class="in">    FROM </span></span>
+<span id="cb28-468"><a href="#cb28-468" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name1, </span></span>
+<span id="cb28-469"><a href="#cb28-469" aria-hidden="true" tabindex="-1"></a><span class="in">    table_name2, ...</span></span>
+<span id="cb28-470"><a href="#cb28-470" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-471"><a href="#cb28-471" aria-hidden="true" tabindex="-1"></a>Let's say we want to identify the top 10 action movies that are highly rated (with an average rating greater than 7) and popular (having more than 5000 votes), along with the primary actors who are the most popular. We can use CTEs to break this query down into separate problems. Initially, we can filter to find good action movies and prolific actors separately. This way, in our final join, we only need to change the order.</span>
+<span id="cb28-472"><a href="#cb28-472" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-475"><a href="#cb28-475" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
+<span id="cb28-476"><a href="#cb28-476" aria-hidden="true" tabindex="-1"></a><span class="co">#| vscode: {languageId: python}</span></span>
+<span id="cb28-477"><a href="#cb28-477" aria-hidden="true" tabindex="-1"></a><span class="op">%%</span>sql</span>
+<span id="cb28-478"><a href="#cb28-478" aria-hidden="true" tabindex="-1"></a>WITH </span>
+<span id="cb28-479"><a href="#cb28-479" aria-hidden="true" tabindex="-1"></a>good_action_movies AS (</span>
+<span id="cb28-480"><a href="#cb28-480" aria-hidden="true" tabindex="-1"></a>    SELECT <span class="op">*</span></span>
+<span id="cb28-481"><a href="#cb28-481" aria-hidden="true" tabindex="-1"></a>    FROM Title T JOIN Rating R ON T.tconst <span class="op">=</span> R.tconst  </span>
+<span id="cb28-482"><a href="#cb28-482" aria-hidden="true" tabindex="-1"></a>    WHERE genres LIKE <span class="st">'%Action%'</span> AND averageRating <span class="op">&gt;</span> <span class="dv">7</span> AND numVotes <span class="op">&gt;</span> <span class="dv">5000</span></span>
+<span id="cb28-483"><a href="#cb28-483" aria-hidden="true" tabindex="-1"></a>),</span>
+<span id="cb28-484"><a href="#cb28-484" aria-hidden="true" tabindex="-1"></a>prolific_actors AS (</span>
+<span id="cb28-485"><a href="#cb28-485" aria-hidden="true" tabindex="-1"></a>    SELECT N.nconst, primaryName, COUNT(<span class="op">*</span>) <span class="im">as</span> numRoles</span>
+<span id="cb28-486"><a href="#cb28-486" aria-hidden="true" tabindex="-1"></a>    FROM Name N JOIN Principal P ON N.nconst <span class="op">=</span> P.nconst</span>
+<span id="cb28-487"><a href="#cb28-487" aria-hidden="true" tabindex="-1"></a>    WHERE category <span class="op">=</span> <span class="st">'actor'</span></span>
+<span id="cb28-488"><a href="#cb28-488" aria-hidden="true" tabindex="-1"></a>    GROUP BY N.nconst, primaryName</span>
+<span id="cb28-489"><a href="#cb28-489" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb28-490"><a href="#cb28-490" aria-hidden="true" tabindex="-1"></a>SELECT primaryTitle, primaryName, numRoles, ROUND(averageRating) AS rating</span>
+<span id="cb28-491"><a href="#cb28-491" aria-hidden="true" tabindex="-1"></a>FROM good_action_movies m, prolific_actors a, principal p</span>
+<span id="cb28-492"><a href="#cb28-492" aria-hidden="true" tabindex="-1"></a>WHERE p.tconst <span class="op">=</span> m.tconst AND p.nconst <span class="op">=</span> a.nconst</span>
+<span id="cb28-493"><a href="#cb28-493" aria-hidden="true" tabindex="-1"></a>ORDER BY rating DESC, numRoles DESC</span>
+<span id="cb28-494"><a href="#cb28-494" aria-hidden="true" tabindex="-1"></a>LIMIT <span class="dv">10</span><span class="op">;</span></span>
+<span id="cb28-495"><a href="#cb28-495" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
 </code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
 </div> <!-- /content -->
diff --git a/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png b/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png
index 8d38b596..5b69bcab 100644
Binary files a/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png and b/docs/visualization_2/visualization_2_files/figure-html/cell-17-output-2.png differ
diff --git a/index.log b/index.log
index 70b9c059..ef365654 100644
--- a/index.log
+++ b/index.log
@@ -1,4 +1,4 @@
-This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2024.3.3)  11 APR 2024 11:57
+This is XeTeX, Version 3.141592653-2.6-0.999995 (TeX Live 2023) (preloaded format=xelatex 2024.3.3)  12 APR 2024 10:10
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
diff --git a/index.pdf b/index.pdf
index d959ee0f..7948a851 100644
Binary files a/index.pdf and b/index.pdf differ
diff --git a/index.tex b/index.tex
index 7e12f56b..d602eb36 100644
--- a/index.tex
+++ b/index.tex
@@ -197,7 +197,7 @@
 \urlstyle{same} % disable monospaced font for URLs
 \hypersetup{
   pdftitle={Principles and Techniques of Data Science},
-  pdfauthor={Bella Crouch; Yash Dave; Kanu Grover; Ishani Gupta; Sakshi Kolli; Minh Phan; Nikhil Reddy; Milad Shafaie; Matthew Shen; Lillian Weng},
+  pdfauthor={Bella Crouch; Yash Dave; Ian Dong; Kanu Grover; Ishani Gupta; Sakshi Kolli; Minh Phan; Nikhil Reddy; Milad Shafaie; Matthew Shen; Lillian Weng},
   colorlinks=true,
   linkcolor={blue},
   filecolor={Maroon},
@@ -213,14 +213,14 @@
 }
 \makeatother
 \subtitle{Data 100}
-\author{Bella Crouch \and Yash Dave \and Kanu Grover \and Ishani
-Gupta \and Sakshi Kolli \and Minh Phan \and Nikhil Reddy \and Milad
-Shafaie \and Matthew Shen \and Lillian Weng}
+\author{Bella Crouch \and Yash Dave \and Ian Dong \and Kanu
+Grover \and Ishani Gupta \and Sakshi Kolli \and Minh Phan \and Nikhil
+Reddy \and Milad Shafaie \and Matthew Shen \and Lillian Weng}
 \date{}
 
 \begin{document}
 \maketitle
-\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[breakable, frame hidden, borderline west={3pt}{0pt}{shadecolor}, boxrule=0pt, interior hidden, enhanced, sharp corners]}{\end{tcolorbox}}\fi
+\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[breakable, enhanced, borderline west={3pt}{0pt}{shadecolor}, boxrule=0pt, sharp corners, frame hidden, interior hidden]}{\end{tcolorbox}}\fi
 
 \renewcommand*\contentsname{Table of contents}
 {
@@ -262,7 +262,7 @@ \section*{About the Course Notes}\label{about-the-course-notes}}
 \hypertarget{introduction}{%
 \chapter{Introduction}\label{introduction}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -316,7 +316,7 @@ \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, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Course Goals}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -334,7 +334,7 @@ \chapter{Introduction}\label{introduction}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Some Topics We'll Cover}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -364,7 +364,7 @@ \chapter{Introduction}\label{introduction}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Prerequisites}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 To ensure that you can get the most out of the course content, please
 make sure that you are familiar with:
@@ -602,7 +602,7 @@ \section{Conclusion}\label{conclusion}}
 \hypertarget{pandas-i}{%
 \chapter{Pandas I}\label{pandas-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -3276,7 +3276,7 @@ \section{Parting Note}\label{parting-note}}
 \hypertarget{pandas-ii}{%
 \chapter{Pandas II}\label{pandas-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -4137,9 +4137,9 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lllrlr}
 \toprule
-{} & State & Sex &  Year &     Name &  Count \\
+{} & State & Sex &  Year &   Name &  Count \\
 \midrule
-4829 &    CA &   F &  1921 &  Yoshiye &     12 \\
+352867 &    CA &   M &  2004 &  Kaden &    297 \\
 \bottomrule
 \end{tabular}
 
@@ -4161,13 +4161,13 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lrlr}
 \toprule
-{} &  Year &     Name &  Count \\
+{} &  Year &         Name &  Count \\
 \midrule
-341569 &  1999 &   Gevork &      8 \\
-11995  &  1932 &     Erma &     25 \\
-206891 &  2014 &  Mayleen &     23 \\
-46041  &  1962 &   Leanne &    104 \\
-208510 &  2014 &    Kayle &      7 \\
+51166  &  1964 &      Louella &      5 \\
+89343  &  1982 &      Chelsey &     25 \\
+24384  &  1947 &       Shelia &     16 \\
+248633 &  1930 &      Patrick &     62 \\
+47428  &  1962 &  Jackqueline &      5 \\
 \bottomrule
 \end{tabular}
 
@@ -4186,12 +4186,12 @@ \subsection{\texorpdfstring{\texttt{.sample()}}{.sample()}}\label{sample}}
 
 \begin{tabular}{lrlr}
 \toprule
-{} &  Year &      Name &  Count \\
+{} &  Year &     Name &  Count \\
 \midrule
-150805 &  2000 &    Thania &     13 \\
-343614 &  2000 &     Parth &     13 \\
-344476 &  2000 &     Jorje &      6 \\
-150472 &  2000 &  Sheridan &     18 \\
+344149 &  2000 &     Zeke &      8 \\
+342568 &  2000 &  Jeffrey &    468 \\
+344223 &  2000 &    Gavyn &      7 \\
+150447 &  2000 &    Elina &     18 \\
 \bottomrule
 \end{tabular}
 
@@ -4334,7 +4334,7 @@ \section{Parting Note}\label{parting-note-1}}
 \hypertarget{pandas-iii}{%
 \chapter{Pandas III}\label{pandas-iii}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -4637,7 +4637,7 @@ \section{\texorpdfstring{Aggregating Data with
 \end{Shaded}
 
 \begin{verbatim}
-<pandas.core.groupby.generic.DataFrameGroupBy object at 0x13542aac0>
+<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1200d5ac0>
 \end{verbatim}
 
 What does this strange output mean? Calling \texttt{.groupby}
@@ -20061,7 +20061,7 @@ \chapter{Data Cleaning and EDA}\label{data-cleaning-and-eda}}
 \end{Highlighting}
 \end{Shaded}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -22623,7 +22623,7 @@ \subsection{EDA and Data Wrangling}\label{eda-and-data-wrangling}}
 \hypertarget{regular-expressions}{%
 \chapter{Regular Expressions}\label{regular-expressions}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -22960,11 +22960,11 @@ \subsubsection{Canonicalization with Pandas Series
 \end{Shaded}
 
 \begin{verbatim}
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49429/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_61747/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
-/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_49429/2523629438.py:3: FutureWarning:
+/var/folders/7t/zbwy02ts2m7cn64fvwjqb8xw0000gp/T/ipykernel_61747/2523629438.py:3: FutureWarning:
 
 The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
 
@@ -23911,7 +23911,7 @@ \section{Limitations of Regular
 \hypertarget{visualization-i}{%
 \chapter{Visualization I}\label{visualization-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -24749,7 +24749,7 @@ \subsubsection{Evaluating Histograms}\label{evaluating-histograms}}
 \hypertarget{visualization-ii}{%
 \chapter{Visualization II}\label{visualization-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -26295,7 +26295,7 @@ \subsection{Harnessing Context}\label{harnessing-context}}
 \hypertarget{sampling}{%
 \chapter{Sampling}\label{sampling}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -26904,7 +26904,7 @@ \subsubsection{Simple Random Sample}\label{simple-random-sample}}
 \end{Shaded}
 
 \begin{verbatim}
-0.5309101937695467
+0.5317894467384167
 \end{verbatim}
 
 This is very close to the actual vote of 0.5302792307692308!
@@ -26928,8 +26928,8 @@ \subsubsection{Simple Random Sample}\label{simple-random-sample}}
 \end{Highlighting}
 \end{Shaded}
 
-\textbf{Actual} = 0.5303, \textbf{Sample} = 0.5337, \textbf{Err} =
-0.65\%.
+\textbf{Actual} = 0.5303, \textbf{Sample} = 0.5150, \textbf{Err} =
+2.88\%.
 
 We'll learn how to choose this number when we (re)learn the Central
 Limit Theorem later in the semester.
@@ -26979,7 +26979,7 @@ \subsubsection{Quantifying Chance
 \end{Shaded}
 
 \begin{verbatim}
-0.952
+0.956
 \end{verbatim}
 
 You can see the curve looks roughly Gaussian/normal. Using KDE:
@@ -27012,7 +27012,7 @@ \section{Summary}\label{summary-1}}
 \hypertarget{introduction-to-modeling}{%
 \chapter{Introduction to Modeling}\label{introduction-to-modeling}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -27375,7 +27375,7 @@ \subsection{Derivation}\label{derivation}}
   \(\hat{a} = \text{average of }y - \text{slope}\cdot\text{average of }x\)
 \end{itemize}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 Proof:
 
@@ -27727,7 +27727,7 @@ \section{Fitting the Model}\label{fitting-the-model}}
 \chapter{Constant Model, Loss, and
 Transformations}\label{constant-model-loss-and-transformations}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -29065,7 +29065,7 @@ \section{Bonus: Calculating Constant Model MSE Using an Algebraic
 \hypertarget{ordinary-least-squares}{%
 \chapter{Ordinary Least Squares}\label{ordinary-least-squares}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -29239,7 +29239,7 @@ \subsection{Multiple Linear
 \hypertarget{linear-algebra-approach}{%
 \subsection{Linear Algebra Approach}\label{linear-algebra-approach}}
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra Review: Vector Dot Product}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 The \textbf{dot product (or inner product)} is a vector operation that:
 
@@ -29337,7 +29337,7 @@ \subsection{Linear Algebra Approach}\label{linear-algebra-approach}}
 \(\mathbb{Y}\) is also a vector with \(n\) elements
 (\(\mathbb{Y} \in \mathbb{R}^{n}\)).
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra Review: Linearity}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 An expression is \textbf{linear in \(\theta\)} (a set of parameters) if
 it is a linear combination of the elements of the set. Checking if an
@@ -29382,7 +29382,7 @@ \subsection{Mean Squared Error}\label{mean-squared-error}}
 indication of how ``far away'' the predictions are from the true values,
 on average.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: L2 Norm}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: L2 Norm}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 When working with vectors, this idea of ``distance'' or the vector's
 \textbf{size/length} is represented by the \textbf{norm}. More
@@ -29530,7 +29530,7 @@ \subsection{A Note on Terminology for Multiple Linear
 \hypertarget{geometric-derivation}{%
 \section{Geometric Derivation}\label{geometric-derivation}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Span}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Span}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Recall that the \textbf{span} or \textbf{column space} of a matrix
 \(\mathbb{X}\) (denoted \(span(\mathbb{X})\)) is the set of all possible
@@ -29542,7 +29542,7 @@ \section{Geometric Derivation}\label{geometric-derivation}}
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Matrix-Vector Multiplication}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Matrix-Vector Multiplication}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 There are 2 ways we can think about matrix-vector multiplication
 
@@ -29631,7 +29631,7 @@ \section{Geometric Derivation}\label{geometric-derivation}}
 visualize this as the vector created by dropping a perpendicular line
 from \(\mathbb{Y}\) onto the span of \(\mathbb{X}\).
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Orthogonality}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Linear Algebra: Orthogonality}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Recall that two vectors \(\vec{a}\) and \(\vec{b}\) are orthogonal if
 their dot product is zero: \(\vec{a}^{T}\vec{b} = 0\).
@@ -29754,7 +29754,7 @@ \section{OLS Properties}\label{ols-properties}}
 
 \[\mathbb{X}^Te = 0 \]
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 Proof:
 
@@ -29792,7 +29792,7 @@ \section{OLS Properties}\label{ols-properties}}
 
 \[\sum_i^n e_i = 0\]
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 Proof:
 
@@ -29858,7 +29858,7 @@ \section{Bonus: Uniqueness of the
 The Least Squares estimate \(\hat{\theta}\) is \textbf{unique} if and
 only if \(\mathbb{X}\) is \textbf{full column rank}.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 Proof:
 
@@ -29948,7 +29948,7 @@ \section{Bonus: Uniqueness of the
 \chapter{sklearn and Gradient
 Descent}\label{sklearn-and-gradient-descent}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -31134,7 +31134,7 @@ \subsection{Batch Gradient Descent and Stochastic Gradient
 \hypertarget{feature-engineering}{%
 \chapter{Feature Engineering}\label{feature-engineering}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -31731,7 +31731,7 @@ \section{Complexity and Overfitting}\label{complexity-and-overfitting}}
 we can improve model performance by designing increasingly complex
 models.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Math Fact: Polynomial Degrees}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Math Fact: Polynomial Degrees}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Given \(N\) overlapping data points, we can always find a polynomial of
 degree \(N-1\) that goes through all those points.
@@ -31827,7 +31827,7 @@ \chapter{Case Study in Human Contexts and
 understand the material. The course notes will have the same broader
 structure but are by no means comprehensive.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -32052,7 +32052,7 @@ \section{The Response: Cook County Open Data
 \subsection{1. Question/Problem
 Formulation}\label{questionproblem-formulation}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -32092,7 +32092,7 @@ \subsection{1. Question/Problem
 comfortable. We can determine some metrics of success and frame a social
 problem as a data science problem.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Definitions: Fairness and Transparency}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Definitions: Fairness and Transparency}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 The definitions, as given by the Cook County Assessor's Office, are
 given below:
@@ -32185,7 +32185,7 @@ \subsection{1. Question/Problem
 \subsection{2. Data Acquisition and
 Cleaning}\label{data-acquisition-and-cleaning}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -32262,7 +32262,7 @@ \subsection{2. Data Acquisition and
 \subsection{3. Exploratory Data
 Analysis}\label{exploratory-data-analysis}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -32308,7 +32308,7 @@ \subsection{3. Exploratory Data
 \subsection{4. Prediction and
 Inference}\label{prediction-and-inference}}
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Driving Questions}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -32370,7 +32370,7 @@ \subsection{4. Prediction and
 \hypertarget{results-and-conclusions}{%
 \subsection{5. Results and Conclusions}\label{results-and-conclusions}}
 
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 \begin{itemize}
 \tightlist
@@ -32516,7 +32516,7 @@ \section{Key Takeaways}\label{key-takeaways}}
 \chapter{Cross Validation and
 Regularization}\label{cross-validation-and-regularization}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
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 \begin{itemize}
 \tightlist
@@ -33314,7 +33314,7 @@ \section{Regression Summary}\label{regression-summary}}
 \hypertarget{random-variables}{%
 \chapter{Random Variables}\label{random-variables}}
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -33360,7 +33360,7 @@ \chapter{Random Variables}\label{random-variables}}
 implications for modeling, but if you want to go a step further, take
 Data 140, CS 70, and/or EECS 126.
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Data 8 Recap}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Recall the following concepts from Data 8:
 
@@ -33719,7 +33719,7 @@ \subsection{Variance}\label{variance}}
 calculations, as \(\mathbb{E}[X^2] = \text{Var}(X)\) if \(X\) is
 centered and \(E(X)=0\).
 
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 \[\begin{align}
    \text{Var}(X) &= \mathbb{E}[(X-\mathbb{E}[X])^2] \\
@@ -33747,7 +33747,7 @@ \subsection{Example: Die}\label{example-die}}
       0, \text{otherwise} 
    \end{cases}\]
 
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 \[ \begin{align} 
          \mathbb{E}[X] &= 1\big(\frac{1}{6}\big) + 2\big(\frac{1}{6}\big) + 3\big(\frac{1}{6}\big) + 4\big(\frac{1}{6}\big) + 5\big(\frac{1}{6}\big) + 6\big(\frac{1}{6}\big) \\
@@ -33757,7 +33757,7 @@ \subsection{Example: Die}\label{example-die}}
 
 \end{tcolorbox}
 
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 Using Approach 1 (definition): \[\begin{align} 
       \text{Var}(X) &= \big(\frac{1}{6}\big)((1 - \frac{7}{2})^2 + (2 - \frac{7}{2})^2 + (3 - \frac{7}{2})^2 + (4 - \frac{7}{2})^2 + (5 - \frac{7}{2})^2 + (6 - \frac{7}{2})^2) \\
@@ -33820,7 +33820,7 @@ \subsection{Properties of Expectation}\label{properties-of-expectation}}
 
 \[\mathbb{E}[aX+b] = aE[\mathbb{X}] + b\]
 
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 \[\begin{align}
         \mathbb{E}[aX+b] &= \sum_{x} (ax + b) P(X=x) \\
@@ -33841,7 +33841,7 @@ \subsection{Properties of Expectation}\label{properties-of-expectation}}
 
 \[\mathbb{E}[X+Y] = \mathbb{E}[X] + \mathbb{E}[Y]\]
 
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 \[\begin{align}
     \mathbb{E}[X+Y] &= \sum_{s} (X+Y)(s) P(s) \\
@@ -33892,7 +33892,7 @@ \subsection{Properties of Variance}\label{properties-of-variance}}
   \(X\) by \(b\) units.
 \end{itemize}
 
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 We know that \[\mathbb{E}[aX+b] = aE[\mathbb{X}] + b\]
 
@@ -33931,7 +33931,7 @@ \subsection{Properties of Variance}\label{properties-of-variance}}
   \[\text{Var}(X + Y) = \text{Var}(X) + \text{Var}(Y) \qquad \text{if } X, Y \text{ independent}\]
 \end{enumerate}
 
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 The variance of a sum is affected by the dependence between the two
 random variables that are being added. Let's expand the definition of
@@ -34103,7 +34103,7 @@ \subsection{Summary}\label{summary-2}}
 \chapter{Estimators, Bias, and
 Variance}\label{estimators-bias-and-variance}}
 
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 \begin{itemize}
 \tightlist
@@ -34255,7 +34255,7 @@ \subsection{Example}\label{example}}
 
 C. \(Y_C = 20 * X_1\)
 
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 Let \(X_1, X_2, ... X_{20}\) be 20 i.i.d Bernoulli(0.5) random
 variables. Since the \(X_i\)'s are independent,
@@ -34416,7 +34416,7 @@ \subsection{Using the Sample Mean to Estimate the Population
 \textbf{unbiased estimator} of the population mean and will explore this
 idea more in the next lecture.
 
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 The square root law
 (\href{https://inferentialthinking.com/chapters/14/5/Variability_of_the_Sample_Mean.html\#the-square-root-law}{Data
@@ -34539,7 +34539,7 @@ \subsubsection{Estimating a Linear
 modeled by \[Y = g(x) + \epsilon\]
 \[ f_{\theta}(x) = Y = \theta_0 + \sum_{j=1}^p \theta_j x_j + \epsilon\]
 
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 In our two equations above, the true relationship
 \(g(x) = \theta_0 + \sum_{j=1}^p \theta_j x_j\) is not random, but
@@ -34554,7 +34554,7 @@ \subsubsection{Estimating a Linear
 use it to train a model and obtain an estimate of \(\hat{\theta}\)
 \[\hat{Y}(x) = f_{\hat{\theta}}(x) = \hat{\theta_0} + \sum_{j=1}^p \hat{\theta_j} x_j\]
 
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 In our estimating equation above, our sample \(\Bbb{X}\), \(\Bbb{Y}\)
 are random (often due to human error). Hence, the estimates we calculate
@@ -34745,7 +34745,7 @@ \subsubsection{Model Bias}\label{model-bias}}
 \(g(x)\); if it's negative, our model tends to underestimate \(g(x)\).
 And if it's 0, we can say that our model is \textbf{unbiased}.
 
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 An \textbf{unbiased model} has a \(\text{model bias } = 0\). In other
 words, our model predicts \(g(x)\) on average.
@@ -34842,7 +34842,7 @@ \section{{[}Bonus{]} Proof of Bias-Variance
 Decomposition in the Bias-Variance Tradeoff section above, and this
 content is out of scope.
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 \textbf{Click to show}\vspace{2mm}
 
@@ -34991,7 +34991,7 @@ \subsection{Step 4: Bias-Variance
 \chapter{Causal Inference and
 Confounding}\label{causal-inference-and-confounding}}
 
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 \begin{itemize}
 \tightlist
@@ -35120,7 +35120,7 @@ \section{Review: Bootstrap
   accurately model the variability of our estimates.
 \end{itemize}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-warning-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Why must we resample \emph{with replacement}?}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-warning-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-warning-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-warning-color}{\faExclamationTriangle}\hspace{0.5em}{Why must we resample \emph{with replacement}?}, colframe=quarto-callout-warning-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Given an original sample of size \(n\), we want a resample that has the
 same size \(n\) as the original. Sampling \emph{without} replacement
@@ -35784,7 +35784,7 @@ \subsection{How to perform causal
 \hypertarget{sql-i}{%
 \chapter{SQL I}\label{sql-i}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -36300,8 +36300,8 @@ \subsubsection{\texorpdfstring{Uniqueness with
 \bottomrule\noalign{}
 \endlastfoot
 2010 \\
-2011 \\
 2019 \\
+2011 \\
 \end{longtable}
 
 \hypertarget{applying-where-conditions}{%
@@ -36665,7 +36665,7 @@ \section{Summary}\label{summary-3}}
 \hypertarget{sql-ii}{%
 \chapter{SQL II}\label{sql-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -36780,9 +36780,9 @@ \section{\texorpdfstring{Aggregating with
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
+appetizer & 12 \\
 entree & 30 \\
 dessert & 5 \\
-appetizer & 12 \\
 \end{longtable}
 
 What is going on here? The statement \texttt{GROUP\ BY\ type} tells SQL
@@ -37097,14 +37097,14 @@ \section{EDA in SQL}\label{eda-in-sql}}
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-tt4097596 & movie & What If...? & What If...? & False & None & None & 25
-& Action,Drama \\
 tt0254490 & movie & Succession & Keisho sakazuki & False & 1992 & None &
 119 & Drama \\
 tt6723592 & movie & Tenet & Tenet & False & 2020 & None & 150 &
 Action,Sci-Fi,Thriller \\
 tt1003027 & movie & What If...? & Notre univers impitoyable & False &
 2008 & None & 87 & Comedy \\
+tt4097596 & movie & What If...? & What If...? & False & None & None & 25
+& Action,Drama \\
 tt8008862 & movie & Succession & Succession & False & 2018 & None & None
 & Horror \\
 tt8819150 & movie & Succession & Succession & False & 2018 & None & 71 &
@@ -37507,16 +37507,16 @@ \subsection{\texorpdfstring{Aliasing in
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-The Corbett-Fitzsimmons Fight & 5.199999809265137 \\
-Bohemios & 4.199999809265137 \\
-The Story of the Kelly Gang & 6.0 \\
-The Prodigal Son & 5.400000095367432 \\
-Robbery Under Arms & 4.300000190734863 \\
-Hamlet & 2.9000000953674316 \\
-Don Quijote & 4.199999809265137 \\
-The Fairylogue and Radio-Plays & 5.099999904632568 \\
-Faldgruben & 4.400000095367432 \\
-Hamlet, Prince of Denmark & 4.699999809265137 \\
+Madame Sans-Gêne & 6.900000095367432 \\
+La maison des sept jeunes filles & 6.099999904632568 \\
+Malambo & 6.5 \\
+Mansfield Park & 7.0 \\
+Marinella & 5.599999904632568 \\
+María Montecristo & 5.400000095367432 \\
+Matter of Trust & 4.400000095367432 \\
+Maîtresse pour couple & 6.400000095367432 \\
+Me casé con una estrella & 6.199999809265137 \\
+Memphis Cathouse Blues & 6.300000190734863 \\
 \end{longtable}
 
 Note that the \texttt{AS} is actually optional! We can create aliases
@@ -37543,16 +37543,16 @@ \subsection{\texorpdfstring{Aliasing in
 \endhead
 \bottomrule\noalign{}
 \endlastfoot
-The Corbett-Fitzsimmons Fight & 5.199999809265137 \\
-Bohemios & 4.199999809265137 \\
-The Story of the Kelly Gang & 6.0 \\
-The Prodigal Son & 5.400000095367432 \\
-Robbery Under Arms & 4.300000190734863 \\
-Hamlet & 2.9000000953674316 \\
-Don Quijote & 4.199999809265137 \\
-The Fairylogue and Radio-Plays & 5.099999904632568 \\
-Faldgruben & 4.400000095367432 \\
-Hamlet, Prince of Denmark & 4.699999809265137 \\
+Madame Sans-Gêne & 6.900000095367432 \\
+La maison des sept jeunes filles & 6.099999904632568 \\
+Malambo & 6.5 \\
+Mansfield Park & 7.0 \\
+Marinella & 5.599999904632568 \\
+María Montecristo & 5.400000095367432 \\
+Matter of Trust & 4.400000095367432 \\
+Maîtresse pour couple & 6.400000095367432 \\
+Me casé con una estrella & 6.199999809265137 \\
+Memphis Cathouse Blues & 6.300000190734863 \\
 \end{longtable}
 
 \hypertarget{common-table-expressions}{%
@@ -37622,9 +37622,9 @@ \subsection{Common Table Expressions}\label{common-table-expressions}}
 \endlastfoot
 Threat Level Midnight: The Movie & Steve Carell & 51 & 10.0 \\
 Threat Level Midnight: The Movie & Steve Carell & 51 & 10.0 \\
-Threat Level Midnight: The Movie & Rainn Wilson & 36 & 10.0 \\
 Threat Level Midnight: The Movie & Ed Helms & 36 & 10.0 \\
 Threat Level Midnight: The Movie & Ed Helms & 36 & 10.0 \\
+Threat Level Midnight: The Movie & Rainn Wilson & 36 & 10.0 \\
 Threat Level Midnight: The Movie & John Krasinski & 28 & 10.0 \\
 Threat Level Midnight: The Movie & Craig Robinson & 26 & 10.0 \\
 Threat Level Midnight: The Movie & Ricky Gervais & 17 & 10.0 \\
@@ -37637,7 +37637,7 @@ \subsection{Common Table Expressions}\label{common-table-expressions}}
 \hypertarget{logistic-regression-i}{%
 \chapter{Logistic Regression I}\label{logistic-regression-i}}
 
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+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -38096,7 +38096,7 @@ \subsubsection{3. Putting it Together}\label{putting-it-together}}
 
 \[\sigma(t) = \frac{1}{1+e^{-t}}\]
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Properties of the Sigmoid}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Properties of the Sigmoid}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist
@@ -38170,7 +38170,7 @@ \section{The Logistic Regression
 \hat{P}_{\theta}(Y = 1 | x) = \sigma(x^{\top}\theta)
 \end{align}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-tip-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Properties of the Logistic Model}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-tip-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-tip-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-tip-color}{\faLightbulb}\hspace{0.5em}{Properties of the Logistic Model}, colframe=quarto-callout-tip-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 Consider a logistic regression model with one feature and an intercept
 term:
@@ -38195,7 +38195,7 @@ \section{The Logistic Regression
 
 \end{tcolorbox}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, opacityback=0, leftrule=.75mm, colback=white, arc=.35mm, bottomrule=.15mm, rightrule=.15mm, left=2mm]
+\begin{tcolorbox}[enhanced jigsaw, breakable, toprule=.15mm, leftrule=.75mm, left=2mm, rightrule=.15mm, colback=white, opacityback=0, arc=.35mm, bottomrule=.15mm]
 
 \textbf{Example Calculation}\vspace{2mm}
 
@@ -38642,7 +38642,7 @@ \subsection{Likelihood of Data}\label{likelihood-of-data}}
 \hypertarget{logistic-regression-ii}{%
 \chapter{Logistic Regression II}\label{logistic-regression-ii}}
 
-\begin{tcolorbox}[enhanced jigsaw, breakable, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, arc=.35mm, bottomrule=.15mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, opacitybacktitle=0.6, toprule=.15mm, opacityback=0, bottomtitle=1mm, colback=white, colframe=quarto-callout-note-color-frame, toptitle=1mm, titlerule=0mm, rightrule=.15mm, left=2mm, coltitle=black]
+\begin{tcolorbox}[enhanced jigsaw, breakable, leftrule=.75mm, toptitle=1mm, titlerule=0mm, left=2mm, colback=white, opacityback=0, coltitle=black, colbacktitle=quarto-callout-note-color!10!white, toprule=.15mm, rightrule=.15mm, bottomtitle=1mm, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Learning Outcomes}, colframe=quarto-callout-note-color-frame, opacitybacktitle=0.6, arc=.35mm, bottomrule=.15mm]
 
 \begin{itemize}
 \tightlist