def to_json(self, path_or_buf=None, orient=None, date_format=None,<EOL>double_precision=<NUM_LIT:10>, force_ascii=True, date_unit='<STR_LIT>',<EOL>default_handler=None, lines=False, compression='<STR_LIT>',<EOL>index=True):
from pandas.io import json<EOL>if date_format is None and orient == '<STR_LIT>':<EOL><INDENT>date_format = '<STR_LIT>'<EOL><DEDENT>elif date_format is None:<EOL><INDENT>date_format = '<STR_LIT>'<EOL><DEDENT>return json.to_json(path_or_buf=path_or_buf, obj=self, orient=orient,<EOL>date_format=date_format,<EOL>double_precision=double_precision,<EOL>force_ascii=force_ascii, date_unit=date_unit,<EOL>default_handler=default_handler,<EOL>lines=lines, compression=compression,<EOL>index=index)<EOL>
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','table'}

    * DataFrame

      - default is 'columns'
      - allowed values are:
        {'split','records','index','columns','values','table'}

    * 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 : bool, 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 : bool, 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 : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}

    A string representing the compression to use in the output file,
    only used when the first argument is a filename. By default, the
    compression is inferred from the filename.

    .. versionadded:: 0.21.0
    .. versionchanged:: 0.24.0
       'infer' option added and set to default
index : bool, 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
--------
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"}]}'