pandas.read_json#

pandas.read_json(path_or_buf, *, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates=True, precise_float=False, date_unit=None, encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer', nrows=None, storage_options=None, dtype_backend=_NoDefault.no_default, engine='ujson')[source]#

Convert a JSON string to pandas object.

Parameters
path_or_bufa valid JSON str, path object or file-like object

Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.json.

If you want to pass in a path object, pandas accepts any os.PathLike.

By file-like object, we refer to objects with a read() method, such as a file handle (e.g. via builtin open function) or StringIO.

orientstr, optional

Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is:

  • '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

The allowed and default values depend on the value of the typ parameter.

  • when typ == 'series',

    • allowed orients are {'split','records','index'}

    • default is 'index'

    • The Series index must be unique for orient 'index'.

  • when typ == 'frame',

    • allowed orients are {'split','records','index', 'columns','values', 'table'}

    • default is 'columns'

    • The DataFrame index must be unique for orients 'index' and 'columns'.

    • The DataFrame columns must be unique for orients 'index', 'columns', and 'records'.

typ{‘frame’, ‘series’}, default ‘frame’

The type of object to recover.

dtypebool or dict, default None

If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don’t infer dtypes at all, applies only to the data.

For all orient values except 'table', default is True.

convert_axesbool, default None

Try to convert the axes to the proper dtypes.

For all orient values except 'table', default is True.

convert_datesbool or list of str, default True

If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates).

keep_default_datesbool, default True

If parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if

  • it ends with '_at',

  • it ends with '_time',

  • it begins with 'timestamp',

  • it is 'modified', or

  • it is 'date'.

precise_floatbool, default False

Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality.

date_unitstr, default None

The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively.

encodingstr, default is ‘utf-8’

The encoding to use to decode py3 bytes.

encoding_errorsstr, optional, default “strict”

How encoding errors are treated. List of possible values .

New in version 1.3.0.

linesbool, default False

Read the file as a json object per line.

chunksizeint, optional

Return JsonReader object for iteration. See the line-delimited json docs for more information on chunksize. This can only be passed if lines=True. If this is None, the file will be read into memory all at once.

Changed in version 1.2: JsonReader is a context manager.

compressionstr or dict, default ‘infer’

For on-the-fly decompression of on-disk 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). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to None for no decompression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor or tarfile.TarFile, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: compression={'method': 'zstd', 'dict_data': my_compression_dict}.

New in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

nrowsint, optional

The number of lines from the line-delimited jsonfile that has to be read. This can only be passed if lines=True. If this is None, all the rows will be returned.

New in version 1.1.

storage_optionsdict, 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.

New in version 1.2.0.

dtype_backend{“numpy_nullable”, “pyarrow”}, defaults to NumPy backed DataFrames

Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when “numpy_nullable” is set, pyarrow is used for all dtypes if “pyarrow” is set.

The dtype_backends are still experimential.

New in version 2.0.

engine{“ujson”, “pyarrow”}, default “ujson”

Parser engine to use. The "pyarrow" engine is only available when lines=True.

New in version 2.0.

Returns
Series or DataFrame

The type returned depends on the value of typ.

See also

DataFrame.to_json

Convert a DataFrame to a JSON string.

Series.to_json

Convert a Series to a JSON string.

json_normalize

Normalize semi-structured JSON data into a flat table.

Notes

Specific to orient='table', if a DataFrame with a literal Index name of index gets written with to_json(), the subsequent read operation will incorrectly set the Index name to None. This is because index is also used by DataFrame.to_json() to denote a missing Index name, and the subsequent read_json() operation cannot distinguish between the two. The same limitation is encountered with a MultiIndex and any names beginning with 'level_'.

Examples

>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                   index=['row 1', 'row 2'],
...                   columns=['col 1', 'col 2'])

Encoding/decoding a Dataframe using 'split' formatted JSON:

>>> df.to_json(orient='split')
    '{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}'
>>> pd.read_json(_, orient='split')
      col 1 col 2
row 1     a     b
row 2     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"}}'

>>> pd.read_json(_, orient='index')
      col 1 col 2
row 1     a     b
row 2     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"}]'
>>> pd.read_json(_, orient='records')
  col 1 col 2
0     a     b
1     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":"1.4.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}'