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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>“It’s only a model.”</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">"y ~ x + a + b + a:b"</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">></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">></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 ‘the same’ 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 (>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’t</em> do is, well, statistics — it just lets you
describe models in general terms. It doesn’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’t do any of those things for you — it just
gives a high-level language for describing which factors you want your
underlying model to take into account. It’s not suitable for
implementing arbitrary non-linear models from scratch; for that,
you’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’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’re the author of a statistics package, then I hope you’ll
consider integrating Patsy as part of your front-end.</p>
<p>Patsy’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%40googlegroups.com">pydata<span>@</span>googlegroups<span>.</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%40pobox.com">Nathaniel J<span>.</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’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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<ul>
<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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