pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False, chunksize=None, compression='infer')
[source]
Convert a JSON string to pandas object
Parameters: |
path_or_buf : a valid JSON string or file-like, default: None The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be orient : string, Indication of expected JSON string format. Compatible JSON strings can be produced by
The allowed and default values depend on the value of the
New in version 0.23.0: ‘table’ as an allowed value for the
dtype : boolean or dict, default True 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. convert_axes : boolean, default True Try to convert the axes to the proper dtypes. convert_dates : boolean, default True List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if
keep_default_dates : boolean, default True If parsing dates, then parse the default datelike columns numpy : boolean, default False Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. precise_float : boolean, 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_unit : string, 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. lines : boolean, default False Read the file as a json object per line. New in version 0.19.0. encoding : str, default is ‘utf-8’ The encoding to use to decode py3 bytes. New in version 0.19.0. chunksize: integer, default None Return JsonReader object for iteration. See the line-delimted json docs for more information on New in version 0.21.0. compression : {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’ For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip, bz2, zip or xz if path_or_buf is a string ending in ‘.gz’, ‘.bz2’, ‘.zip’, or ‘xz’, respectively, and no decompression otherwise. If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to None for no decompression. New in version 0.21.0. |
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Returns: |
|
See also
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_'
.
>>> 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": "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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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.read_json.html