v.0.7.3 (April 12, 2012)

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 full release notes or issue tracker on GitHub for a complete list.

New features

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 stacked bar plots.
df.plot(kind='bar', stacked=True)    # noqa F821
df.plot(kind='barh', stacked=True)   # noqa F821
  • Add log x and y scaling options 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:

In [1]: series = pd.Series(['Steve', np.nan, 'Joe'])

In [2]: series == 'Steve'
0     True
1    False
2    False
Length: 3, dtype: bool

In [3]: series != 'Steve'
0    False
1     True
2     True
Length: 3, dtype: bool

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:

In [4]: mask = series == 'Steve'

In [5]: series[mask & series.notnull()]
0    Steve
Length: 1, dtype: object

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:

In [6]: 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)})

In [7]: df
     A      B         C         D
0  foo    one  0.469112 -0.861849
1  bar    one -0.282863 -2.104569
2  foo    two -1.509059 -0.494929
3  bar  three -1.135632  1.071804
4  foo    two  1.212112  0.721555
5  bar    two -0.173215 -0.706771
6  foo    one  0.119209 -1.039575
7  foo  three -1.044236  0.271860

[8 rows x 4 columns]

In [8]: grouped = df.groupby('A')['C']

In [9]: grouped.describe()
     count      mean       std  ...       50%       75%       max
A                               ...                              
bar    3.0 -0.530570  0.526860  ... -0.282863 -0.228039 -0.173215
foo    5.0 -0.150572  1.113308  ...  0.119209  0.469112  1.212112

[2 rows x 8 columns]

In [10]: grouped.apply(lambda x: x.sort_values()[-2:])    # top 2 values
bar  1   -0.282863
     5   -0.173215
foo  0    0.469112
     4    1.212112
Name: C, Length: 4, dtype: float64


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