to_dict(self, orient: 'str' = 'dict', into=<class 'dict'>)

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.

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 :None:None:`orient` parameter.

Convert the DataFrame to a dictionary.

See Also

DataFrame.from_dict

Create a DataFrame from a dictionary.

DataFrame.to_json

Convert a DataFrame to JSON format.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

This example is valid syntax, but we were not able to check execution

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}

This example is valid syntax, but we were not able to check execution

>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}

This example is valid syntax, but we were not able to check execution

>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

This example is valid syntax, but we were not able to check execution

>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

This example is valid syntax, but we were not able to check execution

>>> 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.

This example is valid syntax, but we were not able to check execution

>>> 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 :None:None:`defaultdict`, you need to initialize it:

This example is valid syntax, but we were not able to check execution

>>> 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})]

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.common.standardize_mapping

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File: /pandas/core/frame.py#1762
type: <class 'function'>
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