pandas.Panel.to_json¶
-
Panel.
to_json
(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression=None)[source]¶ Convert the object to a JSON string.
Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.
Parameters: path_or_buf : the path or buffer to write the result string
if this is None, return the converted string
orient : string
Series
- default is ‘index’
- allowed values are: {‘split’,’records’,’index’}
DataFrame
- default is ‘columns’
- allowed values are: {‘split’,’records’,’index’,’columns’,’values’}
The format of the JSON string
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}} describing the data, and the data component is like
orient='records'
.Changed in version 0.20.0.
date_format : {None, ‘epoch’, ‘iso’}
Type of date conversion. epoch = epoch milliseconds, iso = ISO8601. The default depends on the orient. For orient=’table’, the default is ‘iso’. For all other orients, the default is ‘epoch’.
double_precision : The number of decimal places to use when encoding
floating point values, default 10.
force_ascii : force encoded string to be ASCII, default True.
date_unit : string, default ‘ms’ (milliseconds)
The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.
default_handler : callable, default None
Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
lines : boolean, default False
If ‘orient’ is ‘records’ write out line delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list like.
New in version 0.19.0.
compression : {None, ‘gzip’, ‘bz2’, ‘xz’}
A string representing the compression to use in the output file, only used when the first argument is a filename
New in version 0.21.0.
Returns: same type as input object with filtered info axis
See also
pd.read_json
Examples
>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df.to_json(orient='split') '{"columns":["col 1","col 2"], "index":["row 1","row 2"], "data":[["a","b"],["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"}}'
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"}]'
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"}]}'