fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be DataFrame.from_records (GH893) • Add sort_columns parameter to allow unsorted plots (GH918) • Enable column access via attributes on GroupBy (GH882) • Can pass dict of values to DataFrame.fillna (GH661) Panel.join (GH115) • Added justify argument to DataFrame.to_string to allow different alignment of column headers • Add sort option to GroupBy to allow disabling sorting of the group keys for potential

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be Panel.join (GH115) • Added justify argument to DataFrame.to_string to allow different alignment of column headers • Add sort option to GroupBy to allow disabling sorting of the group keys for potential Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338) • Can pass a list of functions to aggregate with groupby on a DataFrame, yielding

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be DataFrame.from_records (GH893) • Add sort_columns parameter to allow unsorted plots (GH918) • Enable column access via attributes on GroupBy (GH882) • Can pass dict of values to DataFrame.fillna (GH661) Panel.join (GH115) • Added justify argument to DataFrame.to_string to allow different alignment of column headers • Add sort option to GroupBy to allow disabling sorting of the group keys for potential

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be TypeError (GH8938) • Bug in NDFrame: conflicting attribute/column names now behave consistently between getting and setting. Previously, when both a column and attribute named y existed, data.y would return the the attribute, while data.y = z would update the column (GH8994) 4 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.15.2 In [11]: data = pd.DataFrame({’x’:[1, 2, 3]})

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be Out[9]: jim joe 0 72 83 1 77 84 2 96 65 • groupby will not erroneously exclude columns if the column name conflics with the grouper name (GH8112): In [10]: df = pd.DataFrame({’jim’: range(5), ’joe’: 1 2 2 7 3 3 8 4 4 9 In [12]: gr = df.groupby(df[’jim’] < 2) previous behavior (excludes 1st column from output): In [4]: gr.apply(sum) Out[4]: joe jim False 24 True 11 current behavior: In [13]:

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be adding an implementation that natively supports datetime with timezones. A Series or a DataFrame column previously could be assigned a datetime with timezones, and would work as an object dtype. This had is now stable (GH10125) • merge now accepts the argument indicator which adds a Categorical-type column (by default called _merge) to the output object that takes on the values (GH8790) Observation Origin

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be a string name of such an object (ie, ‘jet’). The colormap is sampled to select the color for each column. Please see Colormaps for more information. (GH3860) • DataFrame.interpolate() is now deprecated by default. (GH3907) • Add the keyword allow_duplicates to DataFrame.insert to allow a duplicate column to be inserted if True, default is False (same as prior to 0.12) (GH3679) 1.1. v0.12.0 (July 24

. . . . . . . . . . . . . . . . . . . . . . . . . 8 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 10 read_csv supports parsing Categorical directly . . . Constructors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 9.2.8 Column selection, addition, deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 9.2.15 DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 434 9.3 Panel . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . 7 read_csv has improved support for duplicate column names . . . . . . . . . . . . . . . . . 9 read_csv supports parsing Categorical directly . . . Constructors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 9.2.8 Column selection, addition, deletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 9.2.15 DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 433 9.3 Panel . . . .

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index now internall share more common operations, e.g. have changed – Column names are now given precedence over locals – Local variables must be referred to explicitly. This means that even if you have a local variable that is not a column you must still