# Overview¶

`patsy` 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 formula mini-language used in R and S.

For instance, if we have some variable y, and we want to regress it against some other variables x, a, b, and the interaction of a and b, then we simply write:

```
patsy.dmatrices("y ~ x + a + b + a:b", data)
```

and Patsy takes care of building appropriate matrices. Furthermore, it:

- Allows data transformations to be specified using arbitrary Python
code: instead of
`x`, we could have written`log(x)`,`(x > 0)`, or even`log(x) if x > 1e-5 else log(1e-5)`, - Provides a range of convenient options for coding categorical variables, including automatic detection and removal of redundancies,
- 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),
- Has an incremental mode to handle data sets which are too large to fit into memory at one time,
- Provides a language for symbolic, human-readable specification of linear constraint matrices,
- 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
- Features a simple API for integration into statistical packages.

What Patsy *won’t* 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 decision trees,
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 Theano, SymPy, 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.

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.

## Download¶

The current release may be downloaded from the Python Package index at

Or the latest *development version* may be found in our Git
repository:

```
git clone git://github.com/pydata/patsy.git
```

## Installation¶

If you have `pip` installed, then a simple

```
pip install --upgrade patsy
```

should get you the latest version. Otherwise, download and unpack the source distribution, and then run

```
python setup.py install
```

## Contact¶

Post your suggestions and questions directly to the pydata mailing list (pydata@googlegroups.com, gmane archive), or to our bug tracker. You could also contact Nathaniel J. Smith 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.

## License¶

2-clause BSD. See the file LICENSE.txt for details.

## Users¶

We currently know of the following projects using Patsy to provide a high-level interface to their statistical code:

If you’d like your project to appear here, see our documentation for
*library developers*!