pandas.DataFrame.__dataframe__#

DataFrame.__dataframe__(nan_as_null=False, allow_copy=True)[source]#

Return the dataframe interchange object implementing the interchange protocol.

Parameters:
nan_as_nullbool, default False

Whether to tell the DataFrame to overwrite null values in the data with NaN (or NaT).

allow_copybool, default True

Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail.

Returns:
DataFrame interchange object

The object which consuming library can use to ingress the dataframe.

Notes

Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html

nan_as_null currently has no effect; once support for nullable extension dtypes is added, this value should be propagated to columns.

Examples

>>> df_not_necessarily_pandas = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
>>> interchange_object = df_not_necessarily_pandas.__dataframe__()
>>> interchange_object.column_names()
Index(['A', 'B'], dtype='object')
>>> df_pandas = (pd.api.interchange.from_dataframe
...              (interchange_object.select_columns_by_name(['A'])))
>>> df_pandas
     A
0    1
1    2

These methods (column_names, select_columns_by_name) should work for any dataframe library which implements the interchange protocol.