In [9]: from pandas.io.json import json_normalize In [10]: data = [{ ....: 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text', (continues on next page) 6 Chapter 1. What’s new json_normalize(data, max_level=1) Out[11]: CreatedBy.Name Lookup.TextField Lookup.UserField Image.a 0 User001 Some text {'Id': 'ID001', 'Name': 'Name001'} b [1 rows x 4 columns] 1.1.6 Series.explode to split read_excel() supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide for more details (GH9070) • Interval, IntervalIndex, and IntervalArray have gained an is_empty

In [9]: from pandas.io.json import json_normalize In [10]: data = [{ ....: 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text', ....: 'UserField': {'Id': 'ID001', 'Name': 'Name001'}} json_normalize(data, max_level=1) Out[11]: CreatedBy.Name Lookup.TextField Lookup.UserField Image.a 0 User001 Some text {'Id': 'ID001', 'Name': 'Name001'} b [1 rows x 4 columns] 1.1.6 Series.explode to split read_excel() supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide for more details (GH9070) • Interval, IntervalIndex, and IntervalArray have gained an is_empty

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 17 Trellis plotting interface 347 17.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 22 rpy2 / R interface 431 22.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 22.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 23 Related

and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data rolling.apply and expanding.apply We’ve added an engine keyword to apply() and apply() that allows the user to execute the routine using Numba instead of Cython. Using the Numba engine can yield significant (or Kleene logic). For example: In [8]: pd.NA | True Out[8]: True For more, see NA section in the user guide on missing data. 1.3.2 Dedicated string data type We’ve added StringDtype, an extension type

around a numpy.ndarray. PandasArray isn’t especially useful on its own, but it does provide the same interface as any extension array defined in pandas or by a third-party library. In [23]: ser = pd.Series([1 just a thin (no-copy) wrapper around a numpy.ndarray that satisfies the pandas exten- sion array interface. In [28]: pd.array([1, 2, 3]) Out[28]: [1, 2, 3] Length: 3, dtype: int64 On their information (GH15475, GH15506). • DataFrame.to_parquet() now accepts index as an argument, allowing the user to override the engine’s default behavior to include or omit the dataframe’s indexes from the resulting

Community tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data and process your data. In pandas, a data table is called a DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources

Community tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data and process your data. In pandas, a data table is called a DataFrame. To introduction tutorial To user guide Straight to tutorial... pandas supports the integration with many file formats or data sources

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 23.7 Trellis plotting interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 24 IO Tools . . . . . 891 29 rpy2 / R interface 893 29.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 29.2 R interface with rpy2 . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 29.6 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 30 pandas

Tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 2 User Guide 225 2.1 IO tools (text, CSV, HDF5, ...) . . . . . . . . . . . . . . . . . . . . . . . . . and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data and process your data. In Pandas, a data table is called a DataFrame. To introduction tutorial To user guide Straight to tutorial... Pandas supports the integration with many file formats or data sources

Tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 2 User Guide 225 2.1 IO tools (text, CSV, HDF5, ...) . . . . . . . . . . . . . . . . . . . . . . . . . and data analysis tools for the Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data and process your data. In Pandas, a data table is called a DataFrame. To introduction tutorial To user guide Straight to tutorial... Pandas supports the integration with many file formats or data sources