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. 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 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 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'table'
: dict like{'schema': {schema}, 'data': {data}}
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'
, orit 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 .
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 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.- 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'
,'xz'
,'tar'
} and other 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 a custom 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 if lines=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 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 tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.- dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’
Back-end data type applied to the resultant
DataFrame
(still experimental). Behaviour is as follows:"numpy_nullable"
: returns nullable-dtype-backedDataFrame
(default)."pyarrow"
: returns pyarrow-backed nullableArrowDtype
DataFrame.
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 when
chunksize
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 to
orient='table'
, if aDataFrame
with a literalIndex
name of index gets written withto_json()
, the subsequent read operation will incorrectly set theIndex
name toNone
. This is because index is also used byDataFrame.to_json()
to denote a missingIndex
name, and the subsequentread_json()
operation cannot distinguish between the two. The same limitation is encountered with aMultiIndex
and any names beginning with'level_'
.Examples
>>> from io import StringIO >>> 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 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(StringIO(_), 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(StringIO(_), 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"}]}'
The following example uses
dtype_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 e 0 0 1 2.5 True a 1577.2 1 1 <NA> 4.5 False b 1577.1