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=<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. ValidURL schemes include http, ftp, s3, and file. For file URLs, a host isexpected. A local file could be:file://localhost/path/to/table.json.

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

By file-like object, we refer to objects with aread() method,such as a file handle (e.g. via builtinopen function)orStringIO.

Deprecated since version 2.1.0:Passing json literal strings is deprecated.

orientstr, optional

Indication of expected JSON string format.Compatible JSON strings can be produced byto_json() with acorresponding 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

• 'table' : dict like{'schema':{schema},'data':{data}}

The allowed and default values depend on the valueof thetyp parameter.

• whentyp=='series',

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

  • default is'index'

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

• whentyp=='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 allorient values except'table', default is True.

convert_axesbool, default None

Try to convert the axes to the proper dtypes.

For allorient values except'table', default is True.

convert_datesbool or list of str, default True

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

keep_default_datesbool, default True

If parsing dates (convert_dates is not False), then try to parse thedefault 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 whendecoding string to double values. Default (False) is to use fast butless precise builtin functionality.

date_unitstr, default None

The timestamp unit to detect if converting dates. The default behaviouris to try and detect the correct precision, but if this is not desiredthen 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 .

Added 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 theline-delimited json docsfor more information onchunksize.This can only be passed iflines=True.If this is None, the file will be read into memory all at once.

compressionstr or dict, default ‘infer’

For on-the-fly decompression of on-disk data. If ‘infer’ and ‘path_or_buf’ ispath-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 toNone for no decompression.Can also be a dict with key'method' setto one of {'zip','gzip','bz2','zstd','xz','tar'} andother key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdDecompressor,lzma.LZMAFile ortarfile.TarFile, respectively.As an example, the following could be passed for Zstandard decompression using acustom compression dictionary:compression={'method':'zstd','dict_data':my_compression_dict}.

Added 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 iflines=True.If this is None, all the rows will be returned.

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 pairsare forwarded tourllib.request.Request as header options. For otherURLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs areforwarded tofsspec.open. Please seefsspec andurllib for moredetails, and for more examples on storage options referhere.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultantDataFrame(still experimental). Behaviour is as follows:

• "numpy_nullable": returns nullable-dtype-backedDataFrame(default).

• "pyarrow": returns pyarrow-backed nullableArrowDtypeDataFrame.

Added in version 2.0.

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

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

Added in version 2.0.

Returns:

Series, DataFrame, or pandas.api.typing.JsonReader

A JsonReader is returned whenchunksize is not0 orNone.Otherwise, the type returned depends on the value oftyp.

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 toorient='table', if aDataFrame with a literalIndex name ofindex gets written withto_json(), thesubsequent read operation will incorrectly set theIndex name toNone. This is becauseindex is also used byDataFrame.to_json()to denote a missingIndex name, and the subsequentread_json() operation cannot distinguish between the two. The samelimitation is encountered with aMultiIndex and any namesbeginning with'level_'.

Examples

>>>fromioimportStringIO>>>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(StringIO(_),orient='split')      col 1 col 2row 1     a     brow 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(StringIO(_),orient='index')      col 1 col 2row 1     a     brow 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(StringIO(_),orient='records')  col 1 col 20     a     b1     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"}]}'

The following example usesdtype_backend="numpy_nullable"

>>>data='''{"index": {"0": 0, "1": 1},...       "a": {"0": 1, "1": null},...       "b": {"0": 2.5, "1": 4.5},...       "c": {"0": true, "1": false},...       "d": {"0": "a", "1": "b"},...       "e": {"0": 1577.2, "1": 1577.1}}'''>>>pd.read_json(StringIO(data),dtype_backend="numpy_nullable")   index     a    b      c  d       e0      0     1  2.5   True  a  1577.21      1  <NA>  4.5  False  b  1577.1