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 fixed width file reader, read_fwf
read_fwf
New 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 stacked bar plots.
stacked
plot
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
DataFrame.plot
Series.plot
Add kurt methods to Series and DataFrame for computing kurtosis
kurt
Reverted some changes to how NA values (represented typically as NaN or None) are handled in non-numeric Series:
NaN
None
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:
False
!=
True
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.
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:
apply
groupby
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
A total of 15 people contributed patches to this release. People with a “+” by their names contributed a patch for the first time.
Abraham Flaxman +
Adam Klein
Andreas H. +
Chang She
Dieter Vandenbussche
Jacques Kvam +
K.-Michael Aye +
Kamil Kisiel +
Martin Blais +
Skipper Seabold
Thomas Kluyver
Wes McKinney
Wouter Overmeire
Yaroslav Halchenko
lgautier +