v0.7.3.rst

v.0.7.3 (April 12, 2012)

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This is a minor release from 0.7.2 and fixes many minor bugs and adds a number of nice new features. There are also a couple of API changes to note; these should not affect very many users, and we are inclined to call them "bug fixes" even though they do constitute a change in behavior. See the :ref:`full release notes <release>` or issue tracker on GitHub for a complete list.

New features

• New :ref:`fixed width file reader <io.fwf>`, read_fwf
• New :ref:`scatter_matrix <visualization.scatter_matrix>` function for making a scatter plot matrix

from pandas.tools.plotting import scatter_matrix
scatter_matrix(df, alpha=0.2)        # noqa F821

• Add stacked argument to Series and DataFrame's plot method for :ref:`stacked bar plots <visualization.barplot>`.

df.plot(kind='bar', stacked=True)    # noqa F821

df.plot(kind='barh', stacked=True)   # noqa F821

• Add log x and y :ref:`scaling options <visualization.basic>` to DataFrame.plot and Series.plot
• Add kurt methods to Series and DataFrame for computing kurtosis

NA Boolean Comparison API Change

Reverted some changes to how NA values (represented typically as NaN or None) are handled in non-numeric Series:

.. ipython:: python

   series = pd.Series(['Steve', np.nan, 'Joe'])
   series == 'Steve'
   series != 'Steve'

In comparisons, NA / NaN will always come through as False except with != which is True. Be very careful with boolean arithmetic, especially negation, in the presence of NA data. You may wish to add an explicit NA filter into boolean array operations if you are worried about this:

.. ipython:: python

   mask = series == 'Steve'
   series[mask & series.notnull()]

While propagating NA in comparisons may seem like the right behavior to some users (and you could argue on purely technical grounds that this is the right thing to do), the evaluation was made that propagating NA everywhere, including in numerical arrays, would cause a large amount of problems for users. Thus, a "practicality beats purity" approach was taken. This issue may be revisited at some point in the future.

Other API Changes

When calling apply on a grouped Series, the return value will also be a Series, to be more consistent with the groupby behavior with DataFrame:

.. ipython:: python
    :okwarning:

    df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                             'foo', 'bar', 'foo', 'foo'],
                       'B': ['one', 'one', 'two', 'three',
                             'two', 'two', 'one', 'three'],
                       'C': np.random.randn(8), 'D': np.random.randn(8)})
    df
    grouped = df.groupby('A')['C']
    grouped.describe()
    grouped.apply(lambda x: x.sort_values()[-2:])    # top 2 values

Contributors

.. contributors:: v0.7.2..v0.7.3