major_axis=date_range(’20010102’,periods=4), ....: minor_axis=[’A’,’B’,’C’,’D’]) ....: In [60]: p core.panel.Panel’> Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis) Items axis: ItemA to ItemC Major_axis print option: In [37]: pd.set_option(’expand_frame_repr’, False) In [38]: wide_frame core.frame.DataFrame’> Int64Index: 5 entries, 0 to 4 Data columns (total 16 columns): 0 5 non-null values /1/2000’, periods=5), ....: minor_axis=[’A’, ’B’, ’C’, ’D’]) ....: In [51]: wp core.panel.Panel’> Dimensions: 2 (items) x 5 (major_axis) x 4 (minor_axis) Items axis: Item1 to Item2 Major_axis

Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 34 1.2.3.1 Modules Privacy Has Changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 1.2.3.2 pandas.errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 . . . . . . . . . . . . 1635 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .

Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 35 1.3.3.1 Modules Privacy Has Changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.3.3.2 pandas.errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 . . . . . . . . . . . . 1766 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .

Reorganization of the library: Privacy Changes . . . . . . . . . . . . . . . . . . . . . . . . 64 1.5.3.1 Modules Privacy Has Changed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 1.5.3.2 pandas.errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 . . . . . . . . . . . . 1903 34.15.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . .

Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.13.1 core.frame.DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A float64 B float64 in 0.13.0) In [12]: pd.set_option(’max_info_rows’,max_info_rows) In [13]: df.info() core.frame.DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A 7 non-null float64 DataFrame({’col’:[np.nan, 0, ’foo’]}, index=[2,1,0]) In [27]: df.equals(df) Out[27]: True In [28]: import pandas.core.common as com In [29]: com.array_equivalent(np.array([0, np.nan]), np.array([0, np.nan])) Out[29]:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 is deprecated and will be replaced by the pandas-datareader pack- age. This will allow the data modules to be independently updated to your pandas installation. The API for pandas-datareader v0.1.1 is [6]: df['B'].dtype Out[6]: datetime64[ns, US/Eastern] In [7]: type(df['B'].dtype) Out[7]: pandas.core.dtypes.DatetimeTZDtype Note: There is a slightly different string repr for the underlying DatetimeIndex

point indexes can only be indexed by integers / labels In [1]: Series(1,np.arange(5))[3.0] pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not 0] pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point Out[2]: 1 In [3]: Series(1,np.arange(5)).iloc[3.0:4] pandas/core/index.py:527: display the null counts In [10]: pd.set_option(’max_info_rows’,0) In [11]: df.info() core.frame.DataFrame’> Int64Index: 10 entries, 0 to 9 Data columns (total 3 columns): A float64 B float64

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 31.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Rolling.sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 pandas.core.window.Rolling.median . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1050 31.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1050 . . . . . . . . . . . . . . . . . 1705 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1705 pandas.core.window.Rolling.sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1706 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1706 pandas.core.window.Rolling.median . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 29.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 grade very bad 1 bad NaN medium NaN good 2 very good 3 dtype: float64 • pandas.core.group_agg and pandas.core.factor_agg were removed. As an alternative, con- struct a dataframe and use df.groupby() DataFrame(data) In [40]: df[’categorical’] = df[’object’].astype(’category’) In [41]: df.info() core.frame.DataFrame’> Int64Index: 5000 entries, 0 to 4999 Data columns (total 8 columns): bool 5000 non-null