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# Changes

## v0.2.1

- 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.

## v0.2.0

Warnings:

- 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 features:

- 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.

## v0.1.0

> First public release.

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