Version 0.7.3 (April 12, 2012) — pandas 3.0.0.dev0+2104.ge637b4290d documentation (original) (raw)

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

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

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

NA boolean comparison API change#

Reverted some changes to how NA values (represented typically as NaN orNone) are handled in non-numeric Series:

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

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

In [3]: series != "Steve" Out[3]: 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()] Out[5]: 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 Out[7]: 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() Out[9]: count mean std min 25% 50% 75% max A bar 3.0 -0.530570 0.526860 -1.135632 -0.709248 -0.282863 -0.228039 -0.173215 foo 5.0 -0.150572 1.113308 -1.509059 -1.044236 0.119209 0.469112 1.212112

[2 rows x 8 columns]

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

Contributors#

A total of 15 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.