etc/notebooks/cookbooks_demo/use_cookbooks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebooks as Reusable Modules and Cookbooks\n",
    "\n",
    "In this notebook, we will demonstrate how the `jupyter_cms` extension enables reuse of Python Jupyter Notebooks like typical Python modules and like cookbooks of injectable recipes. In the first half of this tutorial, we'll import and reuse functions and cells from two other notebooks that reside in the same folder as this one ([sklearn_cookbook.ipynb](sklearn_cookbook.ipynb) and [api_cookbook.ipynb](api_cookbook.ipynb)). In the second half of this tutorial, we'll introduce the annotation syntax that makes notebooks reusable by turning this notebook into a reusable module itself! &#x1F648;&#x1F649;&#x1F64A;\n",
    "\n",
    "On your first visit to this notebook, we recommend that you execute one cell at a time as you read along. To run all of the examples in this notebook, you must have the following Python packages installed.\n",
    "\n",
    "```\n",
    "notebook==4.0.6\n",
    "ipywidgets==4.0.3\n",
    "numexpr==2.4.4\n",
    "pandas==0.16.2\n",
    "matplotlib==1.4.3\n",
    "scikit-learn==0.16.1\n",
    "jupyter_cms==0.3.0\n",
    "```\n",
    "\n",
    "The versions are not set in stone: they're simply what I had installed and working when I wrote this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part #1: How to reuse notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enable the extension\n",
    "\n",
    "To start, we first need to load the content management extension. This magic makes it possible to import other notebooks as Python modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext jupyter_cms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import other notebooks\n",
    "\n",
    "With the extension enabled, we can now import other notebooks using either of two methods. The first method lets us use the standard Python import syntax like so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import mywb.cookbooks_demo.sklearn_cookbook as skcook\n",
    "import mywb.cookbooks_demo.api_cookbook as apicook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using the import syntax:\n",
    "\n",
    "* The root of our Jupyter Notebook server notebook directory becomes a fake package called `mywb` (short for \"my workbench\"). \n",
    "* Subfolders become subpackages. \n",
    "* Notebooks become terminal module names. \n",
    "* Imports may be expressed as shown above or using the `from ... import ...` syntax.\n",
    "* Imports must be absolute with respect to `mywb`. \n",
    "* All folders and notebook names must conform to the naming rules of Python packages and modules.\n",
    "* Notebook modules are cached in `sys.modules` like regular Python modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second method lets us call a function instead of an import statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from jupyter_cms.loader import load_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "apicook = load_notebook('./api_cookbook.ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using the `load_notebook` function:\n",
    "\n",
    "* The path to the notebook may be relative to calling notebook or absolute.\n",
    "* The path and notebook name must conform only to the naming rules of the filesystem.\n",
    "* The notebook is loaded from disk on every invocation, not cached in `sys.modules`. (This makes development and debugging easier across notebooks.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call imported functions\n",
    "\n",
    "With the two notebooks imported, we can now start to use them like Python modules. For example, if we type `skcook.` in a cell and hit <kbd>Tab</kbd>, Jupyter will show us a list of objects exposed by the imported notebook, just like it would for any other module. \n",
    "\n",
    "One of the items in the list is a `help` function. Let's run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<style>\n",
       "    .output .input_prompt,\n",
       "    .output .output .output_prompt,\n",
       "    .output .output .prompt { display: none; }\n",
       "    .output .input_area pre { padding: 0.4em; }\n",
       "</style>\n",
       "\n",
       "<div class=\"cell border-box-sizing text_cell rendered\">\n",
       "<div class=\"prompt input_prompt\">\n",
       "</div>\n",
       "<div class=\"inner_cell\">\n",
       "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
       "<h1 id=\"API-Cookbook\">API Cookbook<a class=\"anchor-link\" href=\"#API-Cookbook\">&#182;</a></h1><p>This notebook contains examples of functions that can be reused from other notebooks. See the <a href=\"use_cookbooks.ipynb\">use_cookbooks</a> notebook that accompanies this one for a tutorial on how to take advantage of this cookbook.</p>\n",
       "<p>The example functions in this notebook require <code>ipywidgets</code>, <code>pandas</code>, <code>matplotlib</code>, <code>seaborn</code>, <code>numexpr</code>, and <code>simplegist</code> to work completely.</p>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "apicook.help()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output from the help function is a set cells from the `api_cookbook.ipynb` notebook. In this particular case, it's a single markdown cell rendered as HTML, but it could be much, much more.\n",
    "\n",
    "To demonstrate, let's look at the `apicook.` autocomplete list again. We see an `instaquery` entry in the list. Let's use the IPython `?` feature to review its docstring in the Jupyter Notebook *pager*, the popover area that appears at the bottom of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "apicook.instaquery?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running the above cell, we can read a bit about what the the `instaquery` function does and what its parameters are. Still, the docstring alone leaves us wanting for an example of what it can do.\n",
    "\n",
    "Notice the last line of the doc string: \"Run instaquery.help() for a detailed example.\" Let's do that now, prefixing it with our module name of course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    .output .input_prompt,\n",
       "    .output .output .output_prompt,\n",
       "    .output .output .prompt { display: none; }\n",
       "    .output .input_area pre { padding: 0.4em; }\n",
       "</style>\n",
       "\n",
       "<div class=\"cell border-box-sizing text_cell rendered\">\n",
       "<div class=\"prompt input_prompt\">\n",
       "</div>\n",
       "<div class=\"inner_cell\">\n",
       "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
       "<p>The <code>instaquery</code> function is handy for quick, interactive exploration of a dataset. A quick introduction to the function appears below.  See <a href=\"https://github.com/ibm-et/jupyter-samples/blob/master/hacks/instaquery.ipynb\">https://github.com/ibm-et/jupyter-samples/blob/master/hacks/instaquery.ipynb</a> for full details.</p>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[5]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"kn\">import</span> <span class=\"nn\">pandas</span> <span class=\"k\">as</span> <span class=\"nn\">pd</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">seaborn</span> <span class=\"k\">as</span> <span class=\"nn\">sns</span>\n",
       "<span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">sns</span><span class=\"o\">.</span><span class=\"n\">load_dataset</span><span class=\"p\">(</span><span class=\"s\">&#39;iris&#39;</span><span class=\"p\">)</span>\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing text_cell rendered\">\n",
       "<div class=\"prompt input_prompt\">\n",
       "</div>\n",
       "<div class=\"inner_cell\">\n",
       "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
       "<p>Pass the DataFrame <code>instaquery</code> to create three widgets: a query text box, a group-by dropdown, and a button. Then try the following:</p>\n",
       "<ol>\n",
       "<li>Click <em>Run instaquery</em> without entering anything else.</li>\n",
       "<li>Enter <code>petal_length &gt; 3 &amp; sepal_length &gt; 7</code> in the query box and and click Run.</li>\n",
       "</ol>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[6]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"n\">instaquery</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "<div class=\"output_wrapper\">\n",
       "<div class=\"output\">\n",
       "\n",
       "\n",
       "<div class=\"output_area\"><div class=\"prompt\"></div>\n",
       "\n",
       "<div class=\"output_html rendered_html output_subarea \">\n",
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing text_cell rendered\">\n",
       "<div class=\"prompt input_prompt\">\n",
       "</div>\n",
       "<div class=\"inner_cell\">\n",
       "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
       "<p>Pass the DataFrame and a rendering function to control the output from <code>instaquery</code>. Then try the following:</p>\n",
       "<ol>\n",
       "<li>Click <em>Run instaquery</em> without entering anything else.</li>\n",
       "<li>Select <code>species</code> in the group-by dropdown.</li>\n",
       "<li>Enter <code>petal_length &gt; 3</code> in the query box and and click Run.</li>\n",
       "</ol>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[7]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"n\">instaquery</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"k\">lambda</span> <span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">by</span><span class=\"p\">:</span> <span class=\"n\">sns</span><span class=\"o\">.</span><span class=\"n\">pairplot</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"mf\">2.5</span><span class=\"p\">,</span> <span class=\"n\">hue</span><span class=\"o\">=</span><span class=\"n\">by</span><span class=\"p\">))</span>\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
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       "\"\n",
       ">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apicook.instaquery.help()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By invoking `help()`, we get a whole bunch of rich text giving examples of how to use the function. The output includes text, Python code, tables, and even plots. Wow! &#x1F389;\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Note</strong> that by accessing this help, we have <strong>not</strong> run the code it contains to generate the plot. The <tt>help</tt> function just retrieves content from the imported notebook document file and displays it here. This is particularly useful for seeing what a cookbook can do before having to install all of its dependencies.</div>\n",
    "\n",
    "Let's enter the sample code here to reproduce the example. We can't quite copy/paste it verbatim, though: we have to prefix the `instaquery()` call in the example with `apicook.` because we namespaced the module on import."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p9jJ/zBx8VbkHWUMmYf/FnwGYfPWNbWKrRcMSDH9yOao3vQ/FXVPRsK/YJHtq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r+U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\nMcbM5n9HFr8HTasa/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f312750e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')\n",
    "apicook.instaquery(df, lambda df, by: sns.pairplot(df, size=2.5, hue=by))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try using the widgets in the manner described by the `apicook.instaquery.help()` output above. (And see the link in the help output for more detail about `instaquery` if you want a fun derailment.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inject recipes\n",
    "\n",
    "So now we know that we can treat notebooks as reusable modules. This approach is nice when we have a set of fixed functions that we want to use again and again. It can be inconvenient, however, when we have code with tons of parameters, don't want to spend time dressing up our work in a fully parameterized API, or want to share a notebook without shipping other notebook dependencies with it.\n",
    "\n",
    "To see how we can address these concerns, let's turn our attention to the other notebook we imported. Again we'll use the autocomplete list (`skcook.` <kbd>Tab</kbd>). This time we'll focus on the `scikit_pca` entry. Run the cell below to see its docstring in the pager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "skcook.scikit_pca?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting. This time we're instructed to invoke `scikit_pca` directly to see an example and then told to use a magic to \"inject\" it into this notebook. Let's do each of these in turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    .output .input_prompt,\n",
       "    .output .output .output_prompt,\n",
       "    .output .output .prompt { display: none; }\n",
       "    .output .input_area pre { padding: 0.4em; }\n",
       "</style>\n",
       "\n",
       "<div class=\"cell border-box-sizing text_cell rendered\">\n",
       "<div class=\"prompt input_prompt\">\n",
       "</div>\n",
       "<div class=\"inner_cell\">\n",
       "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
       "<h2 id=\"Principal-Component-Analysis-Plots\">Principal Component Analysis Plots<a class=\"anchor-link\" href=\"#Principal-Component-Analysis-Plots\">&#182;</a></h2><p>This recipe performs a <a href=\"http://en.wikipedia.org/wiki/Principal_component_analysis\">PCA</a> and plots the data against the first two principal components in a scatter plot. It then prints the <a href=\"http://www.quora.com/What-is-an-eigenvector-of-a-covariance-matrix\">eigenvalues and eigenvectors of the covariance matrix</a> and finally prints the precentage of total variance explained by each component.</p>\n",
       "<p>This recipe defaults to using the <a href=\"http://en.wikipedia.org/wiki/Iris_flower_data_set\">Iris data set</a>.   To use your own data, set <code>X</code> to your instance feature vectors, <code>y</code> to the instance classes as a factor, and <code>labels</code> to human-readable names of the classes.</p>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[6]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"kn\">import</span> <span class=\"nn\">warnings</span>\n",
       "<span class=\"n\">warnings</span><span class=\"o\">.</span><span class=\"n\">filterwarnings</span><span class=\"p\">(</span><span class=\"s\">&#39;ignore&#39;</span><span class=\"p\">)</span> <span class=\"c\">#notebook outputs warnings, let&#39;s ignore them</span>\n",
       "<span class=\"kn\">from</span> <span class=\"nn\">__future__</span> <span class=\"k\">import</span> <span class=\"n\">division</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">math</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">pandas</span> <span class=\"k\">as</span> <span class=\"nn\">pd</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">matplotlib.pyplot</span> <span class=\"k\">as</span> <span class=\"nn\">plt</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">sklearn.datasets</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">sklearn.metrics</span> <span class=\"k\">as</span> <span class=\"nn\">metrics</span>\n",
       "<span class=\"kn\">from</span> <span class=\"nn\">sklearn.decomposition</span> <span class=\"k\">import</span> <span class=\"n\">PCA</span>\n",
       "<span class=\"kn\">from</span> <span class=\"nn\">sklearn.preprocessing</span> <span class=\"k\">import</span> <span class=\"n\">StandardScaler</span>\n",
       "\n",
       "<span class=\"c\"># load the iris dataset</span>\n",
       "<span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">sklearn</span><span class=\"o\">.</span><span class=\"n\">datasets</span><span class=\"o\">.</span><span class=\"n\">load_iris</span><span class=\"p\">()</span>\n",
       "<span class=\"c\"># define feature vectors (X) and target (y)</span>\n",
       "<span class=\"n\">X</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">data</span>   \n",
       "<span class=\"n\">y</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span> \n",
       "<span class=\"n\">labels</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target_names</span>  \n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[7]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"c\"># define the number of components to compute, recommend n_components &lt; y_features</span>\n",
       "<span class=\"n\">pca</span> <span class=\"o\">=</span> <span class=\"n\">PCA</span><span class=\"p\">(</span><span class=\"n\">n_components</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">X_pca</span> <span class=\"o\">=</span> <span class=\"n\">pca</span><span class=\"o\">.</span><span class=\"n\">fit_transform</span><span class=\"p\">(</span><span class=\"n\">X</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c\"># plot the first two principal components</span>\n",
       "<span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">()</span>\n",
       "<span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">scatter</span><span class=\"p\">(</span><span class=\"n\">X_pca</span><span class=\"p\">[:,</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"n\">X_pca</span><span class=\"p\">[:,</span><span class=\"mi\">1</span><span class=\"p\">])</span>\n",
       "<span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">grid</span><span class=\"p\">()</span>\n",
       "<span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">title</span><span class=\"p\">(</span><span class=\"s\">&#39;PCA of the dataset&#39;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlabel</span><span class=\"p\">(</span><span class=\"s\">&#39;Component #1&#39;</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylabel</span><span class=\"p\">(</span><span class=\"s\">&#39;Component #2&#39;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">()</span>\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
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       "4GW8zMXvUtU94s1+txK4FC9lwy5VnS8ifwOcp6pni8jVwEuqekXu/6Wqe/Cynn4NL5fSicD+qvq/\n",
       "S7mAhpGLtSSM6US+HDOLgBvU4zlgK/AWf5+fquqzqroXeBxv9j6Ah4CWrGN/D0BVH8MzJsf5x/2O\n",
       "v/4RvJkRj/Hr366qL6nqy3hv+C14+amOB+4SkW3AJ4Ajs3R+3/97f9a5oXDiwxP9c8wFnJtXxahe\n",
       "rCVhTCd+Bnwgz/bcB23GqLyctW4sa3mM/L+RzP5hD/Ds476SdawhVf3LAvtk1w9FRC4ClgNzgJ8A\n",
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       "O/zjftQ/1zF4rYIdIcdVvAf5Iv8YiMj+EXpfvQQcGPI/Xwr8L+Ba4FRgu6qeZAbCiAszEsZ0Yxlw\n",
       "mt8F9iGgD3hGVTfiuWG24wV5P++7nZRwN1X2NgWexDMstwDn+O6pfwHqROQBvDmxO/35sQOPq6q7\n",
       "gS7gRj+l9V3AsQXOvRlY5gejFwXUXYxnrBYA/5W70dFU40aVYKnCDSMCInIdsFlVv1+wsmFMI6wl\n",
       "YRiGYYRiLQnDMAwjFGtJGIZhGKGYkTAMwzBCMSNhGIZhhGJGwjAMwwjFjIRhGIYRihkJwzAMI5T/\n",
       "D94sBHwwsomPAAAAAElFTkSuQmCC\n",
       "\"\n",
       ">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[8]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"c\"># eigendecomposition on the covariance matrix</span>\n",
       "<span class=\"n\">cov_mat</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">cov</span><span class=\"p\">(</span><span class=\"n\">X_pca</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">eig_vals</span><span class=\"p\">,</span> <span class=\"n\">eig_vecs</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">linalg</span><span class=\"o\">.</span><span class=\"n\">eig</span><span class=\"p\">(</span><span class=\"n\">cov_mat</span><span class=\"p\">)</span>\n",
       "<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s\">&#39;Eigenvectors </span><span class=\"se\">\\n</span><span class=\"s\">%s&#39;</span> <span class=\"o\">%</span><span class=\"k\">eig_vecs</span>)\n",
       "<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s\">&#39;</span><span class=\"se\">\\n</span><span class=\"s\">Eigenvalues </span><span class=\"se\">\\n</span><span class=\"s\">%s&#39;</span> <span class=\"o\">%</span><span class=\"k\">eig_vals</span>)\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "<div class=\"output_wrapper\">\n",
       "<div class=\"output\">\n",
       "\n",
       "\n",
       "<div class=\"output_area\"><div class=\"prompt\"></div>\n",
       "<div class=\"output_subarea output_stream output_stdout output_text\">\n",
       "<pre>Eigenvectors \n",
       "[[  1.00000000e+00  -6.47341838e-17]\n",
       " [  0.00000000e+00   1.00000000e+00]]\n",
       "\n",
       "Eigenvalues \n",
       "[ 4.22484077  0.24224357]\n",
       "</pre>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<div class=\"cell border-box-sizing code_cell rendered\">\n",
       "<div class=\"input\">\n",
       "<div class=\"prompt input_prompt\">In&nbsp;[9]:</div>\n",
       "<div class=\"inner_cell\">\n",
       "    <div class=\"input_area\">\n",
       "<div class=\" highlight hl-ipython3\"><pre><span class=\"c\"># prints the percentage of overall variance explained by each component</span>\n",
       "<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">pca</span><span class=\"o\">.</span><span class=\"n\">explained_variance_ratio_</span><span class=\"p\">)</span>\n",
       "</pre></div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "<div class=\"output_wrapper\">\n",
       "<div class=\"output\">\n",
       "\n",
       "\n",
       "<div class=\"output_area\"><div class=\"prompt\"></div>\n",
       "<div class=\"output_subarea output_stream output_stdout output_text\">\n",
       "<pre>[ 0.92461621  0.05301557]\n",
       "</pre>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skcook.scikit_pca()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result above is much like the what we saw for `instaquery.help()` above. In this case, there's rich documentation of how to perform a principal component analysis using scikit-learn on a sample dataset. Yet in this case, the recipe does not make reference to a `sklearn_pca` function at all.\n",
    "\n",
    "Before we try the `%inject` magic suggested by the docstring, take note of the blue hint a couple cells down. Now let's uncomment and run the inject cell. The, before proceeding, let's delete the inject cell entirely from our notebook: it's not meant to be part of the permanent notebook record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#%inject skcook.scikit_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Hint:</strong> I am/was the cell after the <tt>%inject</tt> example. Find me to continue reading the tutorial after running the <tt>%inject</tt> cell.</div>\n",
    "\n",
    "We see that `%inject skcook.scikit_pca` has added a bunch of new cells to our notebook. In fact, the cells added are the same ones we saw as output when we ran `skcook.scikit_pca()`. Except now they are live cells in our notebook that we can run to reproduce the sample output. Even better, we can edit the cells to use our own data, play with the plot parameters, add new code to standardize the data, and so on. And we can do all of this starting from a working recipe set of code rather than from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search to import\n",
    "\n",
    "We've now covered two ways of reusing notebooks:\n",
    "\n",
    "1. Treating notebooks as typical Python modules\n",
    "2. Treating notebooks as cookbooks of recipes\n",
    "\n",
    "Before we look at how to markup notebooks so that they become reusable, let's look at one more convenience provided by the `jupyter_cms` extension.\n",
    "\n",
    "1. Click on the empty code cell below.\n",
    "2. Click the magnifying glass icon (<i class=\"fa-search fa\"></i>) in the Notebook toolbar.\n",
    "3. Enter the search string `cook*`.\n",
    "4. Locate the `api_cookbook.ipynb` result entry.\n",
    "5. In that entry, click the *Insert Import* link.import mywb.cookbooks_demo.sklearn_cookbook as sklearn_cookbook\n",
    "6. Click the *X* in the top right of the dialog or press <kbd>Escape</kbd> to dismiss the dialog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should see the code necessary to load the notebook as a module written out for us in the cell above. This feature can help relieve much of the burden of writing out lengthy import statements or `load_notebook` path parameters. (Not to mention, search is handy in general as well!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part #2: How to write reusable notebooks\n",
    "\n",
    "We've now seen how to reuse notebooks as modules and cookbooks. Next let's switch to learning how to define reusable notebooks. Basically, the process boils down to adding annotations to a notebook in the form of code comments which the `jupyter_cms` extension knows how to find and parse.\n",
    "\n",
    "We'll practice using these annotations right here in this notebook. At the end, we'll import this notebook as module to see the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add module level `<help>`\n",
    "\n",
    "We'll start by adding module level rich help: what gets output when we invoke `help()` on the imported module itself. We do so by adding a `# <help>` comment to a code cell right below this markdown block. This annotation indicates that any markdown cell directly above the annotated code cell plus any content within the code cell itself should be included in the `help()` output.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Recall</strong> the <tt>apicook.help()</tt> function we used earlier. Its output is defined in the same manner in the <tt>api_cookbook.ipynb</tt> notebook.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# <help>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're not limited to a single block of markdown. If we include an additional code cells with `# <help>` annotations, they are also included in the output of `help()` in top-to-bottom notebook order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am also included in the help!\n"
     ]
    }
   ],
   "source": [
    "# <help>\n",
    "print(\"I am also included in the help!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep this repeat annotation rule in mind as it applies to all of the other annotations described below as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define `<api>` cells\n",
    "\n",
    "Next let's mark some cells as `# <api>` indicating that they should execute when this notebook gets imported as a module. Here we'll configure matplotlib, import a few packages, and define a `plot_random` function for reuse by other notebooks.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Recall</strong> the <tt>apicook.instaquery()</tt> function we used earlier. It is defined in the same manner in the <tt>api_cookbook.ipynb</tt> notebook.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# <api>\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# <api>\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# <api>\n",
    "def plot_random(series=3, samples=5):\n",
    "    '''Draws N series with M samples in each. Just as a demo.'''\n",
    "    data = np.random.rand(samples, series)\n",
    "    df = pd.DataFrame(data)\n",
    "    df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attach `<help:object>` to `plot_random`\n",
    "\n",
    "We'll want to provide rich help on how to use our `plot_random` function. We do so using `# <help:plot_random>` annotations. They behave exactly the same as `# <help>` annotations, and capture both code and output within their cells as well as markdown directly above.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Recall</strong> the <tt>apicook.instaquery.help()</tt> function we used earlier. Its output is defined in the same manner in the <tt>api_cookbook.ipynb</tt> notebook.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot three series of five random samples each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w41TNB0beQ3p1ZwP0HEMehQ1X+DRv6/VdNUcHjMCYvAtBpepxY5G+unXsfcIE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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f310d0cfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# <help:plot_random>\n",
    "plot_random()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot five series with ten points each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d7mZol0sb1zcfk0gE7r6yGlFkRw7SWNbTt+GdvhY7JqmX6IoQEFKa1AFkymy0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F7qNPLsOYSUEI39almjOSeX/B/+A8a0dEIeD6mf/xty//4HGB2OwWMm0k/vdfn4pLyF9CQrq0oi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\nk2Ug9VJYM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3111079438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# <help:plot_random>\n",
    "plot_random(series=5, samples=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add an injectable `<help:recipe>`\n",
    "\n",
    "Finally, let's add a recipe that we can inject into another notebook, one that reminds us how to merge two DataFrames by index instead of by column name. We annotate the cells in this recipe using `# <help:pandas_index_merge>` which, like the other help annotations, grabs the code in its cell plus any markdown cell directly above it.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"><strong>Recall</strong> the <tt>%inject skcook.scikit_pca</tt> magic we used earlier. Its content is defined in the same manner in the <tt>sklearn_cookbook.ipynb</tt> notebook.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick HOWTO reminder of merging pandas DataFrames by index instead of by column name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Abe</td>\n",
       "      <td>Lincoln</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>George</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Teddy</td>\n",
       "      <td>Roosevelt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Jefferson</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name   last_name\n",
       "b        Abe     Lincoln\n",
       "a     George  Washington\n",
       "d      Teddy   Roosevelt\n",
       "c     Thomas   Jefferson"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# <help:pandas_index_merge>\n",
    "import pandas as pd\n",
    "\n",
    "df1 = pd.DataFrame(data=['George', 'Abe', 'Thomas', 'Teddy'], \n",
    "                   index=['a', 'b', 'c', 'd'],\n",
    "                   columns=['first_name'])\n",
    "df2 = pd.DataFrame(data=['Lincoln', 'Washington', 'Roosevelt', 'Jefferson'],\n",
    "                   index=['b', 'a', 'd', 'c'],\n",
    "                   columns=['last_name'])\n",
    "pd.merge(df1, df2, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try your work\n",
    "\n",
    "We've now done the following in this notebook to make it reusable:\n",
    "\n",
    "1. defined rich module help\n",
    "2. defined and exposed as API a `plot_random` function\n",
    "3. defined rich help for `plot_random`\n",
    "4. defined an injectable `pandas_index_merge` recipe\n",
    "\n",
    "Let's test our handiwork by importing this notebook as a module and using each feature. We can actually import this notebook within itself and use it like a module by running the code below. (Or, if we think recursion of notebook-importing-itself is too mind-blowing, we can open a separate notebook and execute the code there. &#x1F616;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "me = load_notebook('./use_cookbooks.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.help()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.plot_random.help()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.plot_random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "me.pandas_index_merge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%inject me.pandas_index_merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn more\n",
    "\n",
    "Congrats! You've reached the end of this tutorial. &#x1F388;\n",
    "\n",
    "If you'd like to see additional examples, check out the accompanying [api_cookbook.ipynb](api_cookbook.ipynb) and [sklearn_cookbook.ipynb](sklearn_cookbook.ipynb) notebooks. If you create your own reusable notebooks, don't hesitate to share them with us and others. Remember, nothing about the comment-based markup is incompatible with existing tools in the Jupyter ecosystem."
   ]
  }
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