to_json(self, path_or_buf: 'FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None' = None, orient: 'str | None' = None, date_format: 'str | None' = None, double_precision: 'int' = 10, force_ascii: 'bool_t' = True, date_unit: 'str' = 'ms', default_handler: 'Callable[[Any], JSONSerializable] | None' = None, lines: 'bool_t' = False, compression: 'CompressionOptions' = 'infer', index: 'bool_t' = True, indent: 'int | None' = None, storage_options: 'StorageOptions' = None) -> 'str | None'
Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.
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.
String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a 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'}.
Type of date conversion. 'epoch' = epoch milliseconds, 'iso' = ISO8601. The default depends on the :None:None:`orient`. For orient='table'
, the default is 'iso'. For all other orients, the default is 'epoch'.
The number of decimal places to use when encoding floating point values.
Force encoded string to be ASCII.
The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively.
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.
If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like.
For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', or '.zst' (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'
} and other key-value pairs are forwarded to zipfile.ZipFile
, gzip.GzipFile
, bz2.BZ2File
, or zstandard.ZstdDecompressor
, 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}
.
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'.
Length of whitespace used to indent each record.
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
as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec
. Please see fsspec
and urllib
for more details.
If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.
Convert the object to a JSON string.
read_json
Convert a JSON string to pandas object.
>>> import jsonThis example is valid syntax, but we were not able to check execution
... df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
... parsed = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP { "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 = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP [ { "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 = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP { "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 = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP { "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 = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP [ [ "a", "b" ], [ "c", "d" ] ]
Encoding with Table Schema:
This example is valid syntax, but we were not able to check execution>>> result = df.to_json(orient="table")See :
... parsed = json.loads(result)
... json.dumps(parsed, indent=4) # doctest: +SKIP { "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" } ] }
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