xorbits.pandas.DataFrame.to_dict#

DataFrame.to_dict(orient: Literal['dict', 'list', 'series', 'split', 'tight', 'index'] = 'dict', into: type[dict] = <class 'dict'>) → dict[source]#
DataFrame.to_dict(orient: Literal['records'], into: type[dict] = <class 'dict'>) → list[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', '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}}

    New in version 1.4.0(pandas): ‘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’.

    New in version 2.0.0(pandas).

Returns

Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

Return type

dict, list or collections.abc.Mapping

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})]

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.