pandas.DataFrame.to_dict

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

Convert DataFrame to dictionary.

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}}

  New in version 0.17.0.

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

into : class, default dict

The collections.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.

New in version 0.21.0.

Returns:

result : collections.Mapping like {column -> {index -> value}}

Examples

>>> df = pd.DataFrame(
        {'col1': [1, 2], 'col2': [0.5, 0.75]}, index=['a', 'b'])
>>> df
   col1  col2
a     1   0.1
b     2   0.2
>>> df.to_dict()
{'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': a    1
b    2
Name: col1, dtype: int64, 'col2': a    0.50
b    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'columns': ['col1', 'col2'],
'data': [[1.0, 0.5], [2.0, 0.75]],
'index': ['a', 'b']}
>>> df.to_dict('records')
[{'col1': 1.0, 'col2': 0.5}, {'col1': 2.0, 'col2': 0.75}]
>>> df.to_dict('index')
{'a': {'col1': 1.0, 'col2': 0.5}, 'b': {'col1': 2.0, 'col2': 0.75}}

You can also specify the mapping type.

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

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

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<type 'list'>, {'col2': 0.5, 'col1': 1.0}),
defaultdict(<type 'list'>, {'col2': 0.75, 'col1': 2.0})]