pandas.DataFrame.to_dict¶

DataFrame.to_dict(orient='dict', into=<class 'dict'>)[source]¶

Convert the DataFrame to a dictionary.

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

Parameters:

orientstr {‘dict’, ‘list’, ‘series’, ‘split’, ‘tight’, ‘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]}
‘tight’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values], ‘index_names’ -> [index.names], ‘column_names’ -> [column.names]}
‘records’ : list like [{column -> value}, … , {column -> value}]
‘index’ : dict like {index -> {column -> value}}

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

New in version 1.4.0: ‘tight’ as an allowed value for the orient argument

intoclass, 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}}

>>> 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})]

pandas.DataFrame.take

pandas.DataFrame.to_gbq