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pydata-tutorial-simpsons-ANSWERS.html
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pydata-tutorial-simpsons-ANSWERS.html
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<!DOCTYPE html>
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<title>pydata-tutorial-simpsons-ANSWERS</title>
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<h1 id="D’oh!-Unevenly-spaced-time-series-analysis-of-The-Simpsons-in-Pandas">D’oh! Unevenly spaced time series analysis of The Simpsons in Pandas<a class="anchor-link" href="#D’oh!-Unevenly-spaced-time-series-analysis-of-The-Simpsons-in-Pandas">¶</a></h1><h4 id="PyData-Seattle-2017-tutorial"><a href="https://pydata.org/seattle2017/schedule/presentation/104/">PyData Seattle 2017 tutorial</a><a class="anchor-link" href="#PyData-Seattle-2017-tutorial">¶</a></h4><h4 id="Joe-McCarthy,-Data-Scientist,-Indeed"><a href="http://interrelativity.com/joe/">Joe McCarthy</a>, Data Scientist, <a href="https://www.indeed.com">Indeed</a><a class="anchor-link" href="#Joe-McCarthy,-Data-Scientist,-Indeed">¶</a></h4>
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<h2 id="Motivations">Motivations<a class="anchor-link" href="#Motivations">¶</a></h2>
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<h3 id="Unevenly-spaced-opportunities-to-do-unevenly-spaced-time-series-data-analysis">Unevenly spaced opportunities to do unevenly spaced time series data analysis<a class="anchor-link" href="#Unevenly-spaced-opportunities-to-do-unevenly-spaced-time-series-data-analysis">¶</a></h3><p><a href="http://pandas.pydata.org/"><img src="http://pandas.pydata.org/_static/pandas_logo.png" style="margin: 0px 0px 10px 20px; width: 150px; float: right;" title="pandas" alt="pandas_logo.png" /></a>I occasionally need to analyze gaps between events of interest, but not so often that I remember how to do it. I end up searching through my notebooks to remind myself which Pandas methods to use, and how to use them. I wanted to create a protypical example notebook that I can more easily reference .. and wanted to share it with others, in case they might also find it useful.</p><h3 id="Data.world">Data.world<a class="anchor-link" href="#Data.world">¶</a></h3><p><a href="https://data.world"><img src="https://d2ogkq1rg66kh0.cloudfront.net/site-resources/images/fb-image-share.7b15f964.jpg" style="margin: 0px 12px 5px 20px; width: 125px; float: right;" title="data.world" alt="data_world_logo.png" /></a>The last time I hunted down a notebook containing unevenly spaced time series analysis was around the time I first heard about the [data.world](https://data.world) platform for creating, sharing and collaborating on data sets .. I think of it as a GitHub for data.</p><p><a href="https://www.dataquest.io"><img src="http://data-science-hack.com/wp-content/uploads/2016/04/dataquest-io-1-1.png" style="margin: 0px 0px 5px 20px; width: 125px; float: right;" title="dataquest.io" alt="dataquest-io-logo.png" /></a>I was further inspired about using data.world after reading a great tutorial created by Josh Devlin at [Dataquest.io](https://www.dataquest.io) on [Turbocharge Your Data Acquisition using the data.world Python Library](https://www.dataquest.io/blog/datadotworld-python-tutorial/). This is also where I discovered the dataset about The Simpsons episodes, which I think is more entertaining and generally accessible than the datasets I typically work with at Indeed (although those are interesting, too).</p><h3 id="PyData-redemption">PyData redemption<a class="anchor-link" href="#PyData-redemption">¶</a></h3><p>I gave a tutorial on <a href="https://pydata.org/seattle2015/schedule/presentation/8/">Python for Data Science</a> at PyData Seattle 2015, wherein I tried to squeeze a 4-hour tutorial into a 2-hour slot. It didn't work out so well, though I did make the <a href="http://nbviewer.jupyter.org/github/gumption/Python_for_Data_Science/blob/master/Python_for_Data_Science_all.ipynb">Jupyter Notebook from the tutorial</a> publicly available, so attendees could go back over some of the things we rushed through or didn't even get to.</p>
<p>For PyData Seattle 2017, I decided to propose a topic that I estimated would take more like an hour, so that we can take a somewhat more leisurely approach .. and, with any luck, we'll be able to get to lunch before the other morning tutorials let out.</p>
<p>And I will be making this notebook publicly available as well.</p>
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<h2 id="Using-Python-and-Pandas-in-a-Jupyter-Notebook">Using Python and Pandas in a Jupyter Notebook<a class="anchor-link" href="#Using-Python-and-Pandas-in-a-Jupyter-Notebook">¶</a></h2>
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<p>Among the prerequisites for this tutorial are to have Python, Pandas and Jupyter Notebook installed on your computer. In case anyone has a computer that does not yet meet these requirements, I recommend downloading and installing <a href="https://www.continuum.io/downloads">Anaconda</a>, an open data science platform for Python, which includes Pandas, Jupyter Notebook and a variety of other useful open source Python libraries.</p>
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<h3 id="Python-2-vs.-Python-3">Python 2 vs. Python 3<a class="anchor-link" href="#Python-2-vs.-Python-3">¶</a></h3>
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<p>There are 2 major versions of Python in widespread use: <a href="https://docs.python.org/2/">Python 2</a> and <a href="https://docs.python.org/3/">Python 3</a>. Python 3 has some features that are not backward compatible with Python 2, and some Python 2 libraries have not been updated to work with Python 3. I have been using Python 2, primarily because I use some of those Python 2[-only] libraries, but an increasing proportion of them are migrating to Python 3, and I anticipate shifting to Python 3 in the near future.</p>
<p>For more on the topic, I recommend a very well documented IPython Notebook, which includes numerous helpful examples and links, by <a href="http://sebastianraschka.com/">Sebastian Raschka</a>, <a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb">Key differences between Python 2.7.x and Python 3.x</a>, the <a href="http://python-future.org/compatible_idioms.html">Cheat Sheet: Writing Python 2-3 compatible code</a> by Ed Schofield ... or <a href="https://www.google.com/q=python%202%20vs%203">googling Python 2 vs 3</a>.</p>
<p>In order to make this notebook compatible with Python 2 <em>or</em> Python 3, we will import the <a href="(https://docs.python.org/3/library/functions.html#print"><code>print_function</code></a>) from the <a href="https://docs.python.org/2/library/__future__.html"><strong><code>__future__</code></strong></a> module (in Python 2, <code>print</code> is a <a href="https://docs.python.org/2/reference/simple_stmts.html#print">statement</a> not a function). We will also import the <code>division</code> module from the <code>future</code>, as I find <a href="https://www.python.org/dev/peps/pep-0238/">the use of <code>/</code> for "true division"</a> - and the use of <code>//</code> for "floor division" - to be more aligned with my intuition.</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
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<p>We will also import - using the customary aliases - commonly used libraries in the python scientific stack, and specify that any plots will appear inline within the cell of this notebook (vs. popping up a separate window).</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<h3 id="Using-pandas">Using pandas<a class="anchor-link" href="#Using-pandas">¶</a></h3><p>This tutorial is designed for people who are already familiar with the <code>pandas</code> data analysis library.</p>
<p>A thorough review of <code>pandas</code> concepts is beyond the scope of this session, but for participants who are not sufficiently familiar with <code>pandas</code>, I highly recommend the pandas <a href="https://pandas.pydata.org/pandas-docs/stable/10min.html">tutorial ("10 Minutes to pandas")</a> and <a href="https://pandas.pydata.org/pandas-docs/stable/dsintro.html">Intro to Data Structures</a>.</p>
<p>Here are a few basic concepts:</p>
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<li>A <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html"><code>DataFrame</code></a> is a tabular (2-dimensional) data structure with 2 axes: rows (<code>axis=0</code>) and columns (<code>axis=1</code>)</li>
<li>The columns in a <code>DataFrame</code> are one-dimensional arrays called <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html"><code>Series</code></a></li>
<li>Elements of a <code>DataFrame</code> can be accessed using labels, positions or boolean vectors<ul>
<li>The labels for a <code>Series</code> or the rows of a <code>DataFrame</code> are accessible via their <code>index</code> attribute</li>
<li>The labels for each column in a <code>DataFrame</code> are accessible via its <code>columns</code> attribute</li>
<li>Individual labels can be specified using brackets or - in most cases - dotted notation</li>
<li>Positions can be specified using the <a href="https://docs.python.org/2/tutorial/introduction.html">slice operators</a> for python sequences</li>
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</li>
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<p>Examples of these concepts will be highlighted when we first encounter them.</p>
<p>Other important <code>pandas</code> concepts and operations used throughout this tutorial include <a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html">grouping (split-apply-combine)</a> and <a href="http://pandas.pydata.org/pandas-docs/stable/merging.html">merging</a></p>
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<h2 id="Using-the-data.world-Python-Library">Using the data.world Python Library<a class="anchor-link" href="#Using-the-data.world-Python-Library">¶</a></h2>
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<p>In this section, I will borrow heavily - with permission - from Josh Devlin's tutorial, <a href="https://www.dataquest.io/blog/datadotworld-python-tutorial/">Turbocharge Your Data Acquisition using the data.world Python library</a>.</p>
<p>There are additional elements incorporated from the <a href="https://data.world/nrippner/explore-the-data-world-python-sdk/workspace/file?filename=ddw_SDK.ipynb">Python SDK documentation</a> the <a href="https://data.world/nrippner/python-data-wrangling-tutorial/workspace/file?filename=datadotworld_wrangling_tutorial.ipynb">Data Wrangling tutorial</a> at data.world.</p>
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<h3 id="Installing-the-data.world-library">Installing the data.world library<a class="anchor-link" href="#Installing-the-data.world-library">¶</a></h3>
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<p>The data.world Python SDK can be installed via the command line using the <a href="https://conda.io/docs/using/pkgs.html#install-a-package-from-anaconda-org"><code>conda install</code> utility</a>:</p>
<p><code>conda install -c conda-forge datadotworld-py</code></p>
<p>Alternately, you can use <code>pip</code> to install the SDK:</p>
<p><code>pip install git+git://github.com/datadotworld/data.world-py.git</code></p>
<p>Next, you need to retrieve and store your data.world API token.</p>
<ul>
<li>From your data.world account, go to <a href="https://data.world/settings/advanced">Settings > Advanced</a> and get your API token (the image below is from Josh's tutorial):</li>
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<p><a href="https://data.world/settings/advanced"> <img src="https://www.dataquest.io/blog/images/data.world_tutorial/api_token.png" style="width: 600px;" title="data.world Advanced Settings" alt="data_world_api_token.png" /></a></p>
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<li>If you installed the data.world Python SDK in a virtualenv or Conda env, activate that environment. </li>
<li>Run <code>dw configure</code> on the command line, which will prompt you for your token:
<pre><code>~ (datadotworld) $ dw configure
API token (obtained at: https://data.world/settings/advanced): _</code></pre>
When you enter your token, a <code>~/.dw/</code> directory will created in your home directory and your token will be stored in your <code>~/.dw/config</code> file.</li>
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<p>Finally, if you used <code>pip install</code> above, you will need to run <code>pip install datadotworld[PANDAS]</code> on the command line.</p>
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<h3 id="The-Simpsons">The Simpsons<a class="anchor-link" href="#The-Simpsons">¶</a></h3>
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<p>For the benefit of anyone who is not familiar with The Simpsons, here is some context from the <a href="https://en.wikipedia.org/wiki/The_Simpsons">Wikipedia page</a> for the television show:</p>
<blockquote>
<p><a href="https://en.wikipedia.org/wiki/The_Simpsons"><img src="https://upload.wikimedia.org/wikipedia/en/thumb/0/0d/Simpsons_FamilyPicture.png/220px-Simpsons_FamilyPicture.png" style="margin: 0px 0px 10px 20px; width: 150px; float: right;" title="The Simpsons" alt="Simpsons_FamilyPicture.png" /></a>The Simpsons is an American animated sitcom created by Matt Groening for the Fox Broadcasting Company. The series is a satirical depiction of working-class life epitomized by the Simpson family, which consists of Homer, Marge, Bart, Lisa, and Maggie. The show is set in the fictional town of Springfield and parodies American culture, society, television, and the human condition.</p>
<p>...</p>
<p>Since its debut on December 17, 1989, 618 episodes of The Simpsons have been broadcast. Its 28th season began on September 25, 2016. It is the longest-running American sitcom and the longest-running American animated program, and, in 2009, it surpassed Gunsmoke as the longest-running American scripted primetime television series. The Simpsons Movie, a feature-length film, was released in theaters worldwide on July 27, 2007, and grossed over $527 million. On November 4, 2016, the series was renewed for a twenty-ninth and thirtieth season of 22 episodes each, extending the show to 2019.</p>
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<h3 id="The-Simpsons-data-set-at-data.world">The Simpsons data set at data.world<a class="anchor-link" href="#The-Simpsons-data-set-at-data.world">¶</a></h3>
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<p>Josh provides a brief history of this data set in <a href="https://www.dataquest.io/blog/datadotworld-python-tutorial/">his tutorial</a>:</p>
<blockquote><p>For this tutorial, we’ll be working with a data set of information on the TV show, <a href="https://en.wikipedia.org/wiki/The_Simpsons">The Simpsons</a>. The dataset was scraped by Tod Schenider for his post <a href="http://toddwschneider.com/posts/the-simpsons-by-the-data/">The Simpsons by the Data</a>, for which he made the scraper <a href="https://github.com/toddwschneider/flim-springfield">available on GitHub</a>. <a href="https://www.kaggle.com/wcukierski/the-simpsons-by-the-data">Kaggle user William Cukierski used the scraper to upload the data set</a>, which was then <a href="https://data.world/data-society/the-simpsons-by-the-data">rehosted on data.world</a>.</p>
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<p><a href="https://data.world/data-society/the-simpsons-by-the-data">The data set page on data.world</a> shows four CSV files in the data set:</p>
<ul>
<li><code>simpsons_characters.csv</code>: Every character appearing in The Simpsons.</li>
<li><code>simpsons_episodes.csv</code>: Every episode of the The Simpsons.</li>
<li><code>simpsons_locations.csv</code>: Every location appearing in The Simpsons.</li>
<li><code>simpsons_script_lines.csv</code>: Most lines from most scripts of the Simpsons.</li>
</ul>
<p>In <em>this</em> tutorial, we'll only be using the data from <code>simpsons_episodes.csv</code> and <code>simpsons_script_lines.csv</code>.</p>
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<h3 id="Using-data.world’s-Python-library-to-explore-the-data">Using data.world’s Python library to explore the data<a class="anchor-link" href="#Using-data.world’s-Python-library-to-explore-the-data">¶</a></h3>
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<p>Continuing with <a href="https://www.dataquest.io/blog/datadotworld-python-tutorial/">Josh's tutorial</a>:</p>
<blockquote><p>First, let’s import the <code>datadotworld</code> library:</p>
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<div class=" highlight hl-ipython2"><pre><span class="kn">import</span> <span class="nn">datadotworld</span> <span class="kn">as</span> <span class="nn">dw</span>
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<blockquote><p>We’re going to use the <code>load_dataset()</code> function to take a look at the data. When we use <code>load_dataset()</code> for the first time, it:</p>
<ul>
<li>Downloads the data set from data.world and caches it in our <code>~/.dw/</code> directory</li>
<li>Returns a <code>LocalDataset</code> object representing the data set</li>
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<p>Caching the data set locally is a really neat feature - it allows for quicker subsequent loading, lets you work on the data offline, ensures that your source data is the same each time you run your code, and in the future will support data set versioning. After the first time you call <code>load_dataset()</code> for a given dataset, it will load the dataset from the cached version. You can pass <code>True</code> to the optional <code>force_update</code> parameter if you wish to force a reload from the remote version and overwrite the changes.</p>
<p><code>load_dataset()</code> has one required parameter, <code>dataset_key</code> which you can extract from the URL of the data set on data.world. As an example, our simpsons data set has the URL <a href="https://data.world/data-society/the-simpsons-by-the-data">https://data.world/data-society/the-simpsons-by-the-data</a>, which makes its ID <code>data-society/the-simpsons-by-the-data</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">lds</span> <span class="o">=</span> <span class="n">dw</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s">'data-society/the-simpsons-by-the-data'</span><span class="p">)</span> <span class="c"># , force_update=True)</span>
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<p>After loading the dataset, we can verify that it has been locally cached.</p>
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<div class=" highlight hl-ipython2"><pre><span class="o">!</span> ls -l ~/.dw/cache/data-society/the-simpsons-by-the-data/latest/data
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<pre>total 35060
-rw-r--r-- 1 joem staff 222548 Jul 3 15:30 simpsons_characters.csv
-rw-r--r-- 1 joem staff 125999 Jul 3 15:30 simpsons_episodes.csv
-rw-r--r-- 1 joem staff 182776 Jul 3 15:30 simpsons_locations.csv
-rw-r--r-- 1 joem staff 35363962 Jul 3 15:30 simpsons_script_lines.csv
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<h3 id="Getting-to-know-our-data">Getting to know our data<a class="anchor-link" href="#Getting-to-know-our-data">¶</a></h3>
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<blockquote><p>To take a closer look at our <code>LocalDataset</code> object , we can use the <code>LocalDataset.describe()</code> method, which returns a JSON object.</p>
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<div class=" highlight hl-ipython2"><pre><span class="c"># We use pprint as it makes our output easier to read </span>
<span class="kn">import</span> <span class="nn">pprint</span> <span class="kn">as</span> <span class="nn">pp</span>
<span class="n">pp</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">lds</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
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<pre>{u'description': u'Contains the characters, locations, episode details, and script lines for approximately 600 Simpsons episodes.\n\nOriginally, this dataset was scraped by Tod Schenider for his post [The Simpsons by the Data](http://toddwschneider.com/posts/the-simpsons-by-the-data/), for which he made the scraper [available on GitHub](https://github.com/toddwschneider/flim-springfield). [Kaggle user William Cukierski used the scraper to upload the data set](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data), which has been rehosted here.\n\nAn additional column, `chalkboard_gag`, has been added to the `simpsons_episodes.csv` file, scraped from [Simpsons Wiki](http://simpsons.wikia.com/wiki/List_of_chalkboard_gags).',
u'homepage': u'https://data.world/data-society/the-simpsons-by-the-data',
u'keywords': [u'data society',
u'simpsons',
u'text mining',
u'characters',
u'script lines',
u'regression',
u'classification'],
u'name': u'data-society_the-simpsons-by-the-data',
u'resources': [{u'format': u'csv',
u'name': u'simpsons_characters',
u'path': u'data/simpsons_characters.csv'},
{u'format': u'csv',
u'name': u'simpsons_episodes',
u'path': u'data/simpsons_episodes.csv'},
{u'format': u'csv',
u'name': u'simpsons_locations',
u'path': u'data/simpsons_locations.csv'},
{u'format': u'csv',
u'name': u'simpsons_script_lines',
u'path': u'data/simpsons_script_lines.csv'},
{u'bytes': 214869,
u'format': u'csv',
u'mediatype': u'text/csv',
u'name': u'original/simpsons_characters.csv',
u'path': u'original/simpsons_characters.csv'},
{u'bytes': 125398,
u'format': u'csv',
u'mediatype': u'text/csv',
u'name': u'original/simpsons_episodes.csv',
u'path': u'original/simpsons_episodes.csv'},
{u'bytes': 178199,
u'format': u'csv',
u'mediatype': u'text/csv',
u'name': u'original/simpsons_locations.csv',
u'path': u'original/simpsons_locations.csv'},
{u'bytes': 35185146,
u'format': u'csv',
u'mediatype': u'text/csv',
u'name': u'original/simpsons_script_lines.csv',
u'path': u'original/simpsons_script_lines.csv'}],
u'title': u'The Simpsons by the Data'}
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<p>The JSON object has additional information since Josh created his tutorial, which has been inserted below:</p>
<blockquote><p>Our JSON object has 6 key/value pairs at the top level:</p>
<ul>
<li><code>description</code></li>
<li><code>homepage</code> </li>
<li><code>keywords</code> </li>
<li><code>name</code> </li>
<li><code>resouces</code> </li>
<li><code>title</code></li>
</ul>
<p><code>resources</code> is a list that contains information about each file in our data.world data set:</p>
<ul>
<li><code>name</code> </li>
<li><code>format</code></li>
<li><code>path</code></li>
<li><code>mediatype</code></li>
<li><code>bytes</code></li>
</ul>
<p>Along with the <code>LocalDataset.describe()</code> function, there are three key attributes of our LocalDataset object which we can use to access the data itself:</p>
<ul>
<li><code>LocalDataset.dataframes</code></li>
<li><code>LocalDataset.tables</code></li>
<li><code>LocalDataset.raw_data</code></li>
</ul>
<p>Each of these attributes work the same way, but return the data in a different format.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">[</span><span class="n">lds</span><span class="o">.</span><span class="n">dataframes</span><span class="p">,</span> <span class="n">lds</span><span class="o">.</span><span class="n">tables</span><span class="p">,</span> <span class="n">lds</span><span class="o">.</span><span class="n">raw_data</span><span class="p">]:</span>
<span class="k">print</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="s">'</span><span class="se">\n</span><span class="s">'</span><span class="p">)</span> <span class="c"># pprint does not workon lazy-loaded dicts</span>
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<pre>{u'simpsons_episodes': LazyLoadedValue(<pandas.DataFrame>), u'simpsons_characters': LazyLoadedValue(<pandas.DataFrame>), u'simpsons_script_lines': LazyLoadedValue(<pandas.DataFrame>), u'simpsons_locations': LazyLoadedValue(<pandas.DataFrame>)}
{u'simpsons_episodes': LazyLoadedValue(<list of rows>), u'simpsons_characters': LazyLoadedValue(<list of rows>), u'simpsons_script_lines': LazyLoadedValue(<list of rows>), u'simpsons_locations': LazyLoadedValue(<list of rows>)}
{u'simpsons_episodes': LazyLoadedValue(<bytes>), u'original/simpsons_locations.csv': LazyLoadedValue(<bytes>), u'simpsons_characters': LazyLoadedValue(<bytes>), u'original/simpsons_episodes.csv': LazyLoadedValue(<bytes>), u'simpsons_locations': LazyLoadedValue(<bytes>), u'simpsons_script_lines': LazyLoadedValue(<bytes>), u'original/simpsons_script_lines.csv': LazyLoadedValue(<bytes>), u'original/simpsons_characters.csv': LazyLoadedValue(<bytes>)}
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<blockquote><p><code>LocalDataset.dataframes</code> returns a dictionary of pandas DataFrame objects, where as <code>LocalDataset.tables</code> and <code>LocalDataset.raw_data</code> returns the data in dictionaries of Python lists and bytes format respectively. Lists can be useful if we don’t want to use pandas, and bytes is great if we have binary data like images or database files.</p>
<p>Because of the power of the pandas library, let’s use <code>LocalDataset.dataframes</code> to explore and have some fun with our data!</p>
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<h2 id="Exploratory-analysis-of-The-Simpsons-data-using-pandas">Exploratory analysis of The Simpsons data using <code>pandas</code><a class="anchor-link" href="#Exploratory-analysis-of-The-Simpsons-data-using-pandas">¶</a></h2>
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<p>data.world provides a number of ways of working with data, including their own version of SQL (dwSQL), SPARQL and an R SDK (see their <a href="https://data.world/jonloyens/an-intro-to-dataworld-dataset">Intro to data.world Dataset tutorial</a>), but in this tutorial we will focus only on using the Python SDK and using the <code>pandas DataFrames</code> in their datasets.</p>
<p>We will create a <code>DataFrame</code> variable, <code>df</code>, to work with data from the <code>simpsons_script_lines</code> CSV file.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">df</span> <span class="o">=</span> <span class="n">lds</span><span class="o">.</span><span class="n">dataframes</span><span class="p">[</span><span class="s">'simpsons_script_lines'</span><span class="p">]</span>
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<pre>/Users/joem/anaconda/lib/python2.7/site-packages/datadotworld/models/dataset.py:192: UserWarning: Unable to set data frame dtypes automatically using simpsons_script_lines schema. Data types may need to be adjusted manually. Error: Integer column has NA values in column 7
'Error: {}'.format(resource_name, e))
/Users/joem/anaconda/lib/python2.7/site-packages/datadotworld/util.py:136: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.
return self._loader_func()
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<p>The warnings show that we have some columns with mixed types in this <code>DataFrame</code>.</p>
<pre><code>/Users/joem/anaconda/lib/python2.7/site-packages/datadotworld/models/dataset.py:192: UserWarning: Unable to set data frame dtypes automatically using simpsons_script_lines schema. Data types may need to be adjusted manually. Error: Integer column has NA values in column 7
'Error: {}'.format(resource_name, e))
/Users/joem/anaconda/lib/python2.7/site-packages/datadotworld/util.py:136: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.
return self._loader_func()</code></pre>
<p>We can use the <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html"><code>pandas.DataFrame.info()</code></a> function to get more information on the contents of these columns.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 158271 entries, 0 to 158270
Data columns (total 13 columns):
id 158271 non-null int64
episode_id 158271 non-null int64
number 158271 non-null int64
raw_text 158271 non-null object
timestamp_in_ms 158271 non-null object
speaking_line 158271 non-null object
character_id 140750 non-null object
location_id 157864 non-null float64
raw_character_text 140749 non-null object
raw_location_text 157863 non-null object
spoken_words 132112 non-null object
normalized_text 132087 non-null object
word_count 132112 non-null object
dtypes: float64(1), int64(3), object(9)
memory usage: 15.7+ MB
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<p>9 columns have type <code>object</code>, which is usually an indicator that the column contains a mix of data types.</p>
<p>Sometimes this happens when columns include null (<code>NaN</code>) values, so let's show the number of null values along with the number of data types, and include the number of unique values in each column.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">column_label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="s">'{:2d}: {:20} {:6d} unique values, {:6d} null values, {:2d} data type(s)'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">i</span><span class="p">,</span>
<span class="n">column_label</span> <span class="o">+</span> <span class="s">':'</span><span class="p">,</span>
<span class="n">df</span><span class="p">[</span><span class="n">column_label</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">(),</span>
<span class="n">df</span><span class="p">[</span><span class="n">column_label</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span>
<span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">column_label</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())))</span>
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<pre> 0: id: 158271 unique values, 0 null values, 1 data type(s)
1: episode_id: 564 unique values, 0 null values, 1 data type(s)
2: number: 395 unique values, 0 null values, 1 data type(s)
3: raw_text: 148280 unique values, 0 null values, 1 data type(s)
4: timestamp_in_ms: 2704 unique values, 0 null values, 2 data type(s)
5: speaking_line: 5 unique values, 0 null values, 2 data type(s)
6: character_id: 7185 unique values, 17521 null values, 2 data type(s)
7: location_id: 4459 unique values, 407 null values, 1 data type(s)
8: raw_character_text: 6765 unique values, 17522 null values, 2 data type(s)
9: raw_location_text: 4498 unique values, 408 null values, 2 data type(s)
10: spoken_words: 122000 unique values, 26159 null values, 2 data type(s)
11: normalized_text: 119804 unique values, 26184 null values, 2 data type(s)
12: word_count: 114 unique values, 26159 null values, 2 data type(s)
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<p>We can also see what kinds of values are stored in the columns that have mixed data types.</p>
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<div class=" highlight hl-ipython2"><pre><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">column_label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">column_label</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">column_label</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">column_label</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="k">print</span><span class="p">()</span>
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<pre>4 timestamp_in_ms
<type 'int'> 131072
<type 'str'> 27199
Name: timestamp_in_ms, dtype: int64
5 speaking_line
<type 'bool'> 131072
<type 'str'> 27199
Name: speaking_line, dtype: int64
6 character_id
<type 'float'> 133921
<type 'str'> 24350
Name: character_id, dtype: int64
8 raw_character_text
<type 'str'> 140749
<type 'float'> 17522
Name: raw_character_text, dtype: int64
9 raw_location_text
<type 'str'> 157863
<type 'float'> 408
Name: raw_location_text, dtype: int64
10 spoken_words
<type 'str'> 132112
<type 'float'> 26159
Name: spoken_words, dtype: int64
11 normalized_text
<type 'str'> 132087
<type 'float'> 26184
Name: normalized_text, dtype: int64
12 word_count
<type 'str'> 132112
<type 'float'> 26159
Name: word_count, dtype: int64
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<h2 id="Data-Preparation-&-Cleaning">Data Preparation & Cleaning<a class="anchor-link" href="#Data-Preparation-&-Cleaning">¶</a></h2>
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<p>There are a number of columns which have more than one data type, e.g., <code>str</code> and <code>float</code>. There are some other data quality issues in the <code>DataFrame</code> created from <code>simpsons_script_lines.csv</code>.</p>
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<div class=" highlight hl-ipython2"><pre><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>id</th>
<th>episode_id</th>
<th>number</th>
<th>raw_text</th>
<th>timestamp_in_ms</th>
<th>speaking_line</th>