New stateful transforms for computing natural and cylic cubic splines with constraints, and tensor spline bases with constraints. (Thanks to @broessli <https://github.com/broessli> and GDF Suez for contributing this code.)
Dropped support for Python 2.5 and earlier.
Switched to using a single source tree for both Python 2 and Python 3.
Added a fast-path to skip NA detection for inputs with boolean dtypes (thanks to Matt Davis for patch).
Incompatible change: Sometimes when building a design matrix for a formula that does not depend on the data in any way, like "1 ~ 1", we have no way to determine how many rows the resulting matrix should have. In previous versions of patsy, when this occurred we simply returned a matrix with 1 row. In 0.3.0+, we instead refuse to guess, and raise an error.
Note that because of the next change listed, this situation occurs less frequently in 0.3.0 than in previous versions.
If the data argument to build_design_matrices() (or derived functions like dmatrix(), dmatrices()) is a pandas.DataFrame, then we now check its number of rows and index, and insist that the output design matrices match. This also means that if data is a DataFrame, then the error described in the first bullet above cannot occur – we will simply return a column of 1s that is the same size as the input dataframe.
Worked around some more limitations in py2exe/py2app and friends.
- Fixed a nasty bug in missing value handling where, if missing values were present, dmatrix(..., result_type="dataframe") would always crash, and dmatrices("y ~ 1") would produce left- and right-hand side matrices that had different numbers of rows. (As far as I can tell, this bug could not possibly cause incorrect results, only crashes, since it always involved the creation of matrices with incommensurate shapes. Therefore there is no need to worry about the accuracy of any analyses that were successfully performed with v0.2.0.)
- Modified patsy/__init__.py to work around limitations in py2exe/py2app/etc.
- The lowest officially supported Python version is now 2.5. So far as I know everything still works with Python 2.4, but as everyone else has continued to drop support for 2.4, testing on 2.4 has become so much trouble that I’ve given up.
- New support for automatically detecting and (optionally) removing missing values (see NAAction).
- New stateful transform for B-spline regression: bs(). (Requires scipy.)
- Added a core API to make it possible to run predictions on only a subset of model terms. (This is particularly useful for e.g. plotting the isolated effect of a single fitted spline term.) See DesignMatrixBuilder.subset().
- LookupFactor now allows users to mark variables as categorical directly.
- pandas.Categorical objects are now recognized as representing categorical data and handled appropriately.
- Better error reporting for exceptions raised by user code inside formulas. We now, whenever possible, tag the generated exception with information about which factor’s code raised it, and use this information to give better error reporting.
- EvalEnvironment.capture() now takes a reference argument, to make it easier to implement new dmatrix()-like functions.
Other: miscellaneous doc improvements and bug fixes.
First public release.