s], 'column_names' -> [column.names]}
            - 'records' : list like
              [{column -> value}, ... , {column -> value}]
            - 'index' : dict like {index -> {column -> value}}

            .. versionadded:: 1.4.0
                'tight' as an allowed value for the ``orient`` argument

        into : class, default dict
            The collections.abc.Mapping subclass used for all Mappings
            in the return value.  Can be the actual class or an empty
            instance of the mapping type you want.  If you want a
            collections.defaultdict, you must pass it initialized.

        index : bool, default True
            Whether to include the index item (and index_names item if `orient`
            is 'tight') in the returned dictionary. Can only be ``False``
            when `orient` is 'split' or 'tight'.

            .. versionadded:: 2.0.0

        Returns
        -------
        dict, list or collections.abc.Mapping
            Return a collections.abc.Mapping object representing the DataFrame.
            The resulting transformation depends on the `orient` parameter.

        See Also
        --------
        DataFrame.from_dict: Create a DataFrame from a dictionary.
        DataFrame.to_json: Convert a DataFrame to JSON format.

        Examples
        --------
        >>> df = pd.DataFrame({'col1': [1, 2],
        ...                    'col2': [0.5, 0.75]},
        ...                   index=['row1', 'row2'])
        >>> df
              col1  col2
        row1     1  0.50
        row2     2  0.75
        >>> df.to_dict()
        {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

        You can specify the return orientation.

        >>> df.to_dict('series')
        {'col1': row1    1
                 row2    2
        Name: col1, dtype: int64,
        'col2': row1    0.50
                row2    0.75
        Name: col2, dtype: float64}

        >>> df.to_dict('split')
        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
         'data': [[1, 0.5], [2, 0.75]]}

        >>> df.to_dict('records')
        [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

        >>> df.to_dict('index')
        {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

        >>> df.to_dict('tight')
        {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
         'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}

        You can also specify the mapping type.

        >>> from collections import OrderedDict, defaultdict
        >>> df.to_dict(into=OrderedDict)
        OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
                     ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

        If you want a `defaultdict`, you need to initialize it:

        >>> dd = defaultdict(list)
        >>> df.to_dict('records', into=dd)
        [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
         defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
        r