# pandas.DataFrame.to_dict

DataFrame.to_dict(orient='dict', into=<class 'dict'>)

  Convert the DataFrame to a dictionary.

  The type of the key-value pairs can be customized with the parameters (see below).

  Parameters
    orient str {‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’}

      Determines the type of the values of the dictionary.

      - ‘dict’ (default) : dict like {column -> {index -> value}}

      - ‘list’ : dict like {column -> [values]}

      - ‘series’ : dict like {column -> Series(values)}

      - ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

      - ‘records’ : list like [{column -> value}, … , {column -> value}]

      - ‘index’ : dict like {index -> {column -> value}}

      Abbreviations are allowed. s indicates series and sp indicates split.

    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.

  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}}

  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})]