# pandas.Panel.to_json

`Panel.``to_json`(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)

  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 : the path or buffer to write the result string

  > if this is None, return the converted string

  orient : string

  > - 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.

  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 : The number of decimal places to use when encoding

  > floating point values, default 10.

  force_ascii : force encoded string to be ASCII, default True.

  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.

  > New in version 0.19.0.

  compression : {None, ‘gzip’, ‘bz2’, ‘xz’}

  > A string representing the compression to use in the output file, only used when the first argument is a filename

  > New in version 0.21.0.

  Returns: | same type as input object with filtered info axis

  See also

  `pd.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 `'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 `'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 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"}]}'

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