pandas.DataFrame.to_dict#

DataFrame.to_dict(orient='dict',*,into=<class'dict'>,index=True)[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}}

Added in version 1.4.0:‘tight’ as an allowed value for theorient argument

intoclass, default dict

The collections.abc.MutableMapping subclass used for all Mappingsin the return value. Can be the actual class or an emptyinstance of the mapping type you want. If you want acollections.defaultdict, you must pass it initialized.

indexbool, default True

Whether to include the index item (and index_names item iforientis ‘tight’) in the returned dictionary. Can only beFalsewhenorient is ‘split’ or ‘tight’.

Added in version 2.0.0.

Returns:

dict, list or collections.abc.MutableMapping

Return a collections.abc.MutableMapping object representing theDataFrame. The resulting transformation depends on theorientparameter.

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  col2row1     1  0.50row2     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    2Name: col1, dtype: int64,'col2': row1    0.50        row2    0.75Name: 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.

>>>fromcollectionsimportOrderedDict,defaultdict>>>df.to_dict(into=OrderedDict)OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want adefaultdict, 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})]