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.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}}

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=['a', 'b'])
>>> df
   col1  col2
a     1   0.50
b     2   0.75
>>> 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')
{'index': ['a', 'b'], 'columns': ['col1', 'col2'],
 'data': [[1.0, 0.5], [2.0, 0.75]]}

>>> 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(<class 'list'>, {'col1': 1.0, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2.0, 'col2': 0.75})]