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<!DOCTYPE html>
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<title>Cenpy — cenpy v1.0.0post2 Manual</title>
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cenpy</a>
<span class="navbar-text navbar-version pull-left"><b>1.0.0post2</b></span>
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<li class="toctree-l2"><a class="reference internal" href="api.html#configuration-tools">Configuration Tools</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#product-american-community-survey">Product: American Community Survey</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#product-decennial-2010-census">Product: Decennial 2010 Census</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#architectural-component-apiconnection">Architectural Component: APIConnection</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#architectural-component-tigerconnection">Architectural Component: TigerConnection</a></li>
<li class="toctree-l2"><a class="reference internal" href="api.html#architectural-component-esrilayer">Architectural Component: ESRILayer</a></li>
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<h1>Cenpy<a class="headerlink" href="#cenpy" title="Permalink to this headline">¶</a></h1>
<p>Cenpy (pronounced sen-pie) is a package that automatically discovers US Census Bureau API endpoints and exposes them to Python in a consistent fashion. It also provides easy-to-use access to certain well-used data products, like the American Community Survey (ACS) and 2010 Decennial Census. To get started, check out one of the case studies shown below.</p>
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<img src="_static/images/product.png" class="img-responsive center-block">
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<h6>Getting Data using cenpy</h6>
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<a href="https://nbviewer.jupyter.org/github/ljwolf/cenpy/blob/master/notebooks/segregation.ipynb?flush_cache=true" class="thumbnail">
<img src="_static/images/segregation.png" class="img-responsive center-block">
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<h6>Segregation in Time and Space with cenpy and pysal</h6>
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class="img-responsive center-block">
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<h6>A Road to Frictionless Urban Data Science: osmnx and cenpy
</h6>
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</a>
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<a href="http://nbviewer.ipython.org/github/ljwolf/cenpy/blob/master/notebooks/automatic-wrapper.ipynb?flush_cache=true" class="thumbnail">
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<h6>The Underlying Architecture of cenpy
</h6>
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</a>
</div>
</div>
</div><p>Cenpy is easiest to install using <code class="docutils literal notranslate"><span class="pre">conda</span></code>, a commonly-used package manager for scientific python. First, <a class="reference external" href="https://www.anaconda.com/distribution/">install Anaconda</a>.</p>
<p>Then, <code class="docutils literal notranslate"><span class="pre">cenpy</span></code> is available on the <code class="docutils literal notranslate"><span class="pre">conda-forge</span></code> channel. You can install this using the <cite>Anaconda Prompt</cite>, or from within the Anaconda Navigator program. If you want to install the package from within the Anaconda Prompt, you can use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">cenpy</span>
</pre></div>
</div>
<p>Alternatively, you can install cenpy <em>via</em> <code class="docutils literal notranslate"><span class="pre">pip</span></code>, the python package manager, if you have installed <code class="docutils literal notranslate"><span class="pre">geopandas</span></code> and <code class="docutils literal notranslate"><span class="pre">rtree</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">cenpy</span>
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