# 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)

  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

  > - Series
  >   - default is `'index'`
  >   - allowed values are: `{'split','records','index'}`
  >   - The Series index must be unique for orient `'index'`.
  > - DataFrame
  >   - default is `'columns'`
  >   - allowed values are: {‘split’,’records’,’index’,’columns’,’values’}
  >   - The DataFrame index must be unique for orients ‘index’ and ‘columns’.
  >   - The DataFrame columns must be unique for orients ‘index’, ‘columns’, and ‘records’.
  > - 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 : 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.

  Returns: | result : Series or DataFrame