pandas.read_json¶
-
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
file://localhost/path/to/table.json
orient : string,
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'
.
- allowed orients are
- 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'
.
- allowed orients are
New in version 0.23.0: ‘table’ as an allowed value for the
orient
argument- typ : type of object to recover (series or frame), default ‘frame’
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
- it ends with
'_at'
, - it ends with
'_time'
, - it begins with
'timestamp'
, - it is
'modified'
, or - it is
'date'
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
chunksize
. This can only be passed if lines=True. If this is None, the file will be read into memory all at once.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.
Returns: - result : Series or DataFrame, depending on the value of typ.
See also
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
>>> 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"}]}'