Skip to main content
`Ctrl`+`K`

# pandas.Series.to_json

Series.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='infer', index=None, indent=None, storage_options=None, mode='w')

  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 str, path object, file-like object, or None, default None

      String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.

    orient str

      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, where data component is like `orient='records'`.

    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’.

      Deprecated since version 3.0.0: ‘epoch’ date format is deprecated and will be removed in a future version, please use ‘iso’ instead.

    double_precision int, default 10

      The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError.

    force_ascii bool, default True

      Force encoded string to be ASCII.

    date_unit str, 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.

    compression str or dict, default ‘infer’

      For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to `None` for no compression. Can also be a dict with key `'method'` set to one of {`'zip'`, `'gzip'`, `'bz2'`, `'zstd'`, `'xz'`, `'tar'`} and other key-value pairs are forwarded to `zipfile.ZipFile`, `gzip.GzipFile`, `bz2.BZ2File`, `zstandard.ZstdCompressor`, `lzma.LZMAFile` or `tarfile.TarFile`, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: `compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}`.

    index bool or None, default None

      The index is only used when ‘orient’ is ‘split’, ‘index’, ‘column’, or ‘table’. Of these, ‘index’ and ‘column’ do not support index=False. The string ‘index’ as a column name with empty `Index` or if it is ‘index’ will raise a `ValueError`.

    indent int, optional

      Length of whitespace used to indent each record.

    storage_options dict, optional

      Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to `urllib.request.Request` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec.open`. Please see `fsspec` and `urllib` for more details, and for more examples on storage options refer here.

    mode str, default ‘w’ (writing)

      Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’.

  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`

    Convert a JSON string to pandas object.

  Notes

  The behavior of `indent=0` varies from the stdlib, which does not indent the output but does insert newlines. Currently, `indent=0` and the default `indent=None` are equivalent in pandas, though this may change in a future release.

  `orient='table'` contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.

  Examples

      >>> from json import loads, dumps
      >>> df = pd.DataFrame(
      ...     [["a", "b"], ["c", "d"]],
      ...     index=["row 1", "row 2"],
      ...     columns=["col 1", "col 2"],
      ... )

      >>> result = df.to_json(orient="split")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      {
          "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.

      >>> result = df.to_json(orient="records")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      [
          {
              "col 1": "a",
              "col 2": "b"
          },
          {
              "col 1": "c",
              "col 2": "d"
          }
      ]

  Encoding/decoding a Dataframe using `'index'` formatted JSON:

      >>> result = df.to_json(orient="index")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      {
          "row 1": {
              "col 1": "a",
              "col 2": "b"
          },
          "row 2": {
              "col 1": "c",
              "col 2": "d"
          }
      }

  Encoding/decoding a Dataframe using `'columns'` formatted JSON:

      >>> result = df.to_json(orient="columns")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      {
          "col 1": {
              "row 1": "a",
              "row 2": "c"
          },
          "col 2": {
              "row 1": "b",
              "row 2": "d"
          }
      }

  Encoding/decoding a Dataframe using `'values'` formatted JSON:

      >>> result = df.to_json(orient="values")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      [
          [
              "a",
              "b"
          ],
          [
              "c",
              "d"
          ]
      ]

  Encoding with Table Schema:

      >>> result = df.to_json(orient="table")
      >>> parsed = loads(result)
      >>> dumps(parsed, indent=4)
      {
          "schema": {
              "fields": [
                  {
                      "name": "index",
                      "type": "string"
                  },
                  {
                      "name": "col 1",
                      "type": "string"
                  },
                  {
                      "name": "col 2",
                      "type": "string"
                  }
              ],
              "primaryKey": [
                  "index"
              ],
              "pandas_version": "1.4.0"
          },
          "data": [
              {
                  "index": "row 1",
                  "col 1": "a",
                  "col 2": "b"
              },
              {
                  "index": "row 2",
                  "col 1": "c",
                  "col 2": "d"
              }
          ]
      }