Convert a JSON string to pandas 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.
Deprecated since version 2.1.0: Passing json literal strings is deprecated.
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'.
The type of object to recover.
If True, infer dtypes; if a dict of column to dtype, then use those; if False, then donât infer dtypes at all, applies only to the data.
For all orient values except 'table', default is True.
Try to convert the axes to the proper dtypes.
For all orient values except 'table', default is 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).
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'.
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.
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 âsâ, âmsâ, âusâ or ânsâ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively.
The encoding to use to decode py3 bytes.
How encoding errors are treated. List of possible values .
Added in version 1.3.0.
Read the file as a json object per line.
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.
For on-the-fly decompression of on-disk data. If âinferâ and âpath_or_bufâ is path-like, then detect compression from the following extensions: â.gzâ, â.bz2â, â.zipâ, â.xzâ, â.zstâ, â.tarâ, â.tar.gzâ, â.tar.xzâ or â.tar.bz2â (otherwise no compression). If using âzipâ or âtarâ, 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 to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor, lzma.LZMAFile 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}.
Added in version 1.5.0: Added support for .tar files.
Changed in version 1.4.0: Zstandard support.
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.
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 âs3://â, and âgcs://â) 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.
Back-end data type applied to the resultant DataFrame (still experimental). Behaviour is as follows:
"numpy_nullable": returns nullable-dtype-backed DataFrame (default).
"pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame.
Added in version 2.0.
Parser engine to use. The "pyarrow" engine is only available when lines=True.
Added in version 2.0.
A JsonReader is returned when chunksize is not 0 or None. Otherwise, the type returned depends on the value of typ.
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
>>> 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
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