pandas.to_json官方文档阅读

例子

import pandas as pd
df = pd.DataFrame([['a', 'b'], ['c', 'd']],
                  index=['row 1', 'row 2'],
                  columns=['col 1', 'col 2'])
df

	col 1	col 2
row 1	a	b
row 2	c	d

orient : string

Indication of expected JSON string format.

• Series

  • default is ‘index’
  • allowed values are: {‘split’,’records’,’index’}

• DataFrame

  • default is ‘columns’
  • allowed values are: {‘split’,’records’,’index’,’columns’,’values’}

• The format of the JSON string

• ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

• ‘records’ : list like [{column -> value}, … , {column -> value}]

• ‘index’ : dict like {index -> {column -> value}}

• ‘columns’ : dict like {column -> {index -> value}}

• ‘values’ : just the values array

• ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}} describing the data, and the data component is like orient=’records’.

Changed in version 0.20.0.

split 将索引总结到索引，列名到列名，数据到数据。将三部分都分开了了

df.to_json(orient='split')

'{"columns":["col 1","col 2"],"index":["row ","row 2"],"data":[["a","b"],["c","d"]]}'

records 以columns：values的形式输出

df.to_json(orient='records')
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'

index 以index：{columns：values}...的形式输出

df.to_json(orient='index')
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'

colums 以columns:{index:values}的形式输出

df.to_json(orient='columns')
'{"col 1":{"row 1":"a","row 2":"c"},"col 2":{"row 1":"b","row 2":"d"}}'

values 直接输出值

df.to_json(orient='values')
'[["a","b"],["c","d"]]'
df.to_json(orient='table')
'{"schema": {"primaryKey":["index"],"fields":[{"name":"index","type":"string"},{"name":"col 1","type":"string"},{"name":"col 2","type":"string"}],"pandas_version":"0.20.0"}, "data": [{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}'

利用pandas读取数据库的数据，进行操作然后转化为json格式发送出去，真的是利器。
当然还有文件保存的操作：

pandas.to_json("filename.json")

源码：

def to_json(self, path_or_buf=None, orient=None, date_format=None,
                double_precision=10, force_ascii=True, date_unit='ms',
                default_handler=None, lines=False, compression=None,
                index=True):
        """
        Convert the object to a JSON string.
        Note NaN's and None will be converted to null and datetime objects
        will be converted to UNIX timestamps.
        Parameters
        ----------
        path_or_buf : string or file handle, optional
            File path or object. If not specified, the result is returned as
            a string.
        orient : string
            Indication of expected JSON string format.
            * Series
              - default is 'index'
              - allowed values are: {'split','records','index'}
            * DataFrame
              - default is 'columns'
              - allowed values are:
                {'split','records','index','columns','values'}
            * The format of the JSON string
              - 'split' : dict like {'index' -> [index],
                'columns' -> [columns], 'data' -> [values]}
              - 'records' : list like
                [{column -> value}, ... , {column -> value}]
              - 'index' : dict like {index -> {column -> value}}
              - 'columns' : dict like {column -> {index -> value}}
              - 'values' : just the values array
              - 'table' : dict like {'schema': {schema}, 'data': {data}}
                describing the data, and the data component is
                like ``orient='records'``.
                .. versionchanged:: 0.20.0
        date_format : {None, 'epoch', 'iso'}
            Type of date conversion. 'epoch' = epoch milliseconds,
            'iso' = ISO8601. The default depends on the `orient`. For
            ``orient='table'``, the default is 'iso'. For all other orients,
            the default is 'epoch'.
        double_precision : int, default 10
            The number of decimal places to use when encoding
            floating point values.
        force_ascii : boolean, default True
            Force encoded string to be ASCII.
        date_unit : string, default 'ms' (milliseconds)
            The time unit to encode to, governs timestamp and ISO8601
            precision.  One of 's', 'ms', 'us', 'ns' for second, millisecond,
            microsecond, and nanosecond respectively.
        default_handler : callable, default None
            Handler to call if object cannot otherwise be converted to a
            suitable format for JSON. Should receive a single argument which is
            the object to convert and return a serialisable object.
        lines : boolean, default False
            If 'orient' is 'records' write out line delimited json format. Will
            throw ValueError if incorrect 'orient' since others are not list
            like.
            .. versionadded:: 0.19.0
        compression : {None, 'gzip', 'bz2', 'zip', 'xz'}
            A string representing the compression to use in the output file,
            only used when the first argument is a filename.
            .. versionadded:: 0.21.0
        index : boolean, default True
            Whether to include the index values in the JSON string. Not
            including the index (``index=False``) is only supported when
            orient is 'split' or 'table'.
            .. versionadded:: 0.23.0
        See Also
        --------
        pandas.read_json
        Examples
        --------
        >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
        ...                   index=['row 1', 'row 2'],
        ...                   columns=['col 1', 'col 2'])

        >>> df.to_json(orient='split')
        '{"columns":["col 1","col 2"],
          "index":["row 1","row 2"],
          "data":[["a","b"],["c","d"]]}'
        Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
        Note that index labels are not preserved with this encoding.

        >>> df.to_json(orient='records')
        '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'
        Encoding/decoding a Dataframe using ``'index'`` formatted JSON:

        >>> df.to_json(orient='index')
        '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'
        Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:

        >>> df.to_json(orient='columns')
        '{"col 1":{"row 1":"a","row 2":"c"},"col 2":{"row 1":"b","row 2":"d"}}'
        Encoding/decoding a Dataframe using ``'values'`` formatted JSON:

        >>> df.to_json(orient='values')
        '[["a","b"],["c","d"]]'
        Encoding with Table Schema

        >>> df.to_json(orient='table')
        '{"schema": {"fields": [{"name": "index", "type": "string"},
                                {"name": "col 1", "type": "string"},
                                {"name": "col 2", "type": "string"}],
                     "primaryKey": "index",
                     "pandas_version": "0.20.0"},
          "data": [{"index": "row 1", "col 1": "a", "col 2": "b"},
                   {"index": "row 2", "col 1": "c", "col 2": "d"}]}'
        """

        from pandas.io import json
        if date_format is None and orient == 'table':
            date_format = 'iso'
        elif date_format is None:
            date_format = 'epoch'
        return json.to_json(path_or_buf=path_or_buf, obj=self, orient=orient,
                            date_format=date_format,
                            double_precision=double_precision,
                            force_ascii=force_ascii, date_unit=date_unit,
                            default_handler=default_handler,
                            lines=lines, compression=compression,
                            index=index)