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  <div class="section" id="overview">
<h1>Overview<a class="headerlink" href="#overview" title="Permalink to this headline"></a></h1>
<blockquote>
<div><a class="reference external" href="https://en.wikipedia.org/wiki/Patsy_%28Monty_Python%29"><em>&#8220;It&#8217;s only a model.&#8221;</em></a></div></blockquote>
<p><a class="reference internal" href="API-reference.html#module-patsy" title="patsy"><tt class="xref py py-mod docutils literal"><span class="pre">patsy</span></tt></a> is a Python package for describing statistical models
(especially linear models, or models that have a linear component)
and building design matrices. It is closely inspired by and compatible
with the <a class="reference external" href="http://cran.r-project.org/doc/manuals/R-intro.html#Formulae-for-statistical-models">formula</a> mini-language used in <a class="reference external" href="http://www.r-project.org/">R</a> and <a class="reference external" href="https://secure.wikimedia.org/wikipedia/en/wiki/S_programming_language">S</a>.</p>
<p>For instance, if we have some variable <cite>y</cite>, and we want to regress it
against some other variables <cite>x</cite>, <cite>a</cite>, <cite>b</cite>, and the <a class="reference external" href="https://secure.wikimedia.org/wikipedia/en/wiki/Interaction_%28statistics%29">interaction</a>
of <cite>a</cite> and <cite>b</cite>, then we simply write:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">patsy</span><span class="o">.</span><span class="n">dmatrices</span><span class="p">(</span><span class="s">&quot;y ~ x + a + b + a:b&quot;</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
<p>and Patsy takes care of building appropriate matrices. Furthermore,
it:</p>
<ul class="simple">
<li>Allows data transformations to be specified using arbitrary Python
code: instead of <tt class="docutils literal"><span class="pre">x</span></tt>, we could have written <tt class="docutils literal"><span class="pre">log(x)</span></tt>, <tt class="docutils literal"><span class="pre">(x</span> <span class="pre">&gt;</span>
<span class="pre">0)</span></tt>, or even <tt class="docutils literal"><span class="pre">log(x)</span> <span class="pre">if</span> <span class="pre">x</span> <span class="pre">&gt;</span> <span class="pre">1e-5</span> <span class="pre">else</span> <span class="pre">log(1e-5)</span></tt>,</li>
<li>Provides a range of convenient options for coding <a class="reference external" href="https://secure.wikimedia.org/wikipedia/en/wiki/Level_of_measurement#Nominal_scale">categorical</a>
variables, including automatic detection and removal of
redundancies,</li>
<li>Knows how to apply &#8216;the same&#8217; transformation used on original data
to new data, even for tricky transformations like centering or
standardization (critical if you want to use your model to make
predictions),</li>
<li>Has an incremental mode to handle data sets which are too large to
fit into memory at one time,</li>
<li>Provides a language for symbolic, human-readable specification of
linear constraint matrices,</li>
<li>Has a thorough test suite (&gt;97% statement coverage) and solid
underlying theory, allowing it to correctly handle corner cases that
even R gets wrong, and</li>
<li>Features a simple API for integration into statistical packages.</li>
</ul>
<p>What Patsy <em>won&#8217;t</em> do is, well, statistics &#8212; it just lets you
describe models in general terms. It doesn&#8217;t know or care whether you
ultimately want to do linear regression, time-series analysis, or fit
a forest of <a class="reference external" href="https://secure.wikimedia.org/wikipedia/en/wiki/Decision_tree_learning">decision trees</a>,
and it certainly won&#8217;t do any of those things for you &#8212; it just
gives a high-level language for describing which factors you want your
underlying model to take into account. It&#8217;s not suitable for
implementing arbitrary non-linear models from scratch; for that,
you&#8217;ll be better off with something like <a class="reference external" href="http://deeplearning.net/software/theano/">Theano</a>, <a class="reference external" href="http://sympy.org/">SymPy</a>, or just plain Python. But if you&#8217;re using a
statistical package that requires you to provide a raw model matrix,
then you can use Patsy to painlessly construct that model matrix; and
if you&#8217;re the author of a statistics package, then I hope you&#8217;ll
consider integrating Patsy as part of your front-end.</p>
<p>Patsy&#8217;s goal is to become the standard high-level interface to
describing statistical models in Python, regardless of what particular
model or library is being used underneath.</p>
<div class="section" id="download">
<h2>Download<a class="headerlink" href="#download" title="Permalink to this headline"></a></h2>
<p>The current release may be downloaded from the Python Package index at</p>
<blockquote>
<div><a class="reference external" href="http://pypi.python.org/pypi/patsy/">http://pypi.python.org/pypi/patsy/</a></div></blockquote>
<p>Or the latest <em>development version</em> may be found in our <a class="reference external" href="https://github.com/pydata/patsy">Git
repository</a>:</p>
<div class="highlight-python"><div class="highlight"><pre>git clone git://github.com/pydata/patsy.git
</pre></div>
</div>
</div>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline"></a></h2>
<p>Installing <a class="reference internal" href="API-reference.html#module-patsy" title="patsy"><tt class="xref py py-mod docutils literal"><span class="pre">patsy</span></tt></a> requires:</p>
<ul class="simple">
<li><a class="reference external" href="http://python.org/">Python</a> (version 2.4 or later; Python 3 is
fully supported)</li>
<li><a class="reference external" href="http://numpy.scipy.org/">NumPy</a></li>
</ul>
</div>
<div class="section" id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline"></a></h2>
<p>If you have <tt class="docutils literal"><span class="pre">pip</span></tt> installed, then a simple</p>
<div class="highlight-python"><div class="highlight"><pre>pip install --upgrade patsy
</pre></div>
</div>
<p>should get you the latest version. Otherwise, download and unpack the
source distribution, and then run</p>
<div class="highlight-python"><div class="highlight"><pre>python setup.py install
</pre></div>
</div>
</div>
<div class="section" id="contact">
<h2>Contact<a class="headerlink" href="#contact" title="Permalink to this headline"></a></h2>
<p>Post your suggestions and questions directly to the <a class="reference external" href="https://groups.google.com/group/pydata">pydata mailing
list</a>
(<a class="reference external" href="mailto:pydata&#37;&#52;&#48;googlegroups&#46;com">pydata<span>&#64;</span>googlegroups<span>&#46;</span>com</a>, <a class="reference external" href="http://news.gmane.org/gmane.comp.python.pydata">gmane archive</a>), or to our <a class="reference external" href="https://github.com/pydata/patsy/issues">bug
tracker</a>. You could also
contact <a class="reference external" href="mailto:njs&#37;&#52;&#48;pobox&#46;com">Nathaniel J<span>&#46;</span> Smith</a> directly, but
really the mailing list is almost always a better bet, because more
people will see your query and others will be able to benefit from any
answers you get.</p>
</div>
<div class="section" id="license">
<h2>License<a class="headerlink" href="#license" title="Permalink to this headline"></a></h2>
<p>2-clause BSD. See the file <a class="reference external" href="https://github.com/pydata/patsy/blob/master/LICENSE.txt">LICENSE.txt</a> for details.</p>
</div>
<div class="section" id="users">
<h2>Users<a class="headerlink" href="#users" title="Permalink to this headline"></a></h2>
<p>We currently know of the following projects using Patsy to provide a
high-level interface to their statistical code:</p>
<ul class="simple">
<li><a class="reference external" href="http://statsmodels.sourceforge.net/">Statsmodels</a></li>
<li><a class="reference external" href="https://github.com/pymc-devs/pymc/tree/pymc3/">PyMC3</a> (<a class="reference external" href="http://twiecki.github.io/blog/2013/09/12/bayesian-glms-1/">tutorial</a>)</li>
<li><a class="reference external" href="https://github.com/hddm-devs/hddm">HDDM</a></li>
<li><a class="reference external" href="https://github.com/rerpy/rerpy">rERPy</a></li>
<li><a class="reference external" href="https://github.com/synthicity/urbansim">UrbanSim</a></li>
</ul>
<p>If you&#8217;d like your project to appear here, see our documentation for
<a class="reference internal" href="library-developers.html#library-developers"><em>library developers</em></a>!</p>
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<li><a class="reference internal" href="#">Overview</a><ul>
<li><a class="reference internal" href="#download">Download</a></li>
<li><a class="reference internal" href="#requirements">Requirements</a></li>
<li><a class="reference internal" href="#installation">Installation</a></li>
<li><a class="reference internal" href="#contact">Contact</a></li>
<li><a class="reference internal" href="#license">License</a></li>
<li><a class="reference internal" href="#users">Users</a></li>
</ul>
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