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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"}]}'
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