pandas.Series.to_json

Series.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='infer', index=True) [source]

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'. 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 : 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. New in version 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. New in version 0.21.0. Changed in version 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’. New in version 0.23.0.

Returns:

None or str If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

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"}]}'