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pandas中dataframe转字典df.to_dict('records')

pandas中dataframe转字典df.to_dict('records')

有这么一个需求，pandas用dataframe处理了数据，转成dict，再用json输出到http.

• DataFrame.to_dict(orient='dict', into=)[source]

• Convert the DataFrame to a dictionary.

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

  Parameters:
  Returns:

• dict, list or collections.Mapping

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

• 
  

• ‘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}}

• orient : str {‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’}

• Determines the type of the values of the dictionary.

  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.

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}}

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(, {'col1': 1, 'col2': 0.5}), defaultdict(, {'col1': 2, 'col2': 0.75})]