, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False)

Convert JSON string to pandas object

Parameters :

filepath_or_buffer : a VALID JSON string or file handle / StringIO. 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 : :

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

typ : type of object to recover (series or frame), default ‘frame’

dtype : if True, infer dtypes, if a dict of column to dtype, then use those,

if False, then don’t infer dtypes at all, default is True, apply only to the data

convert_axes : boolean, try to convert the axes to the proper dtypes, default is True

convert_dates : a list of columns to parse for dates; If True, then try to parse datelike columns

default is True

keep_default_dates : boolean, default True. If parsing dates,

then parse the default datelike columns

numpy : direct decoding to numpy arrays. default is False.Note 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

Returns :

result : Series or DataFrame