def read_json(path_or_buf=None, orient=None, typ='<STR_LIT>', dtype=None,<EOL>convert_axes=None, convert_dates=True, keep_default_dates=True,<EOL>numpy=False, precise_float=False, date_unit=None, encoding=None,<EOL>lines=False, chunksize=None, compression='<STR_LIT>'):
if orient == '<STR_LIT>' and dtype:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if orient == '<STR_LIT>' and convert_axes:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if dtype is None and orient != '<STR_LIT>':<EOL><INDENT>dtype = True<EOL><DEDENT>if convert_axes is None and orient != '<STR_LIT>':<EOL><INDENT>convert_axes = True<EOL><DEDENT>compression = _infer_compression(path_or_buf, compression)<EOL>filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(<EOL>path_or_buf, encoding=encoding, compression=compression,<EOL>)<EOL>json_reader = JsonReader(<EOL>filepath_or_buffer, orient=orient, typ=typ, dtype=dtype,<EOL>convert_axes=convert_axes, convert_dates=convert_dates,<EOL>keep_default_dates=keep_default_dates, numpy=numpy,<EOL>precise_float=precise_float, date_unit=date_unit, encoding=encoding,<EOL>lines=lines, chunksize=chunksize, compression=compression,<EOL>)<EOL>if chunksize:<EOL><INDENT>return json_reader<EOL><DEDENT>result = json_reader.read()<EOL>if should_close:<EOL><INDENT>try:<EOL><INDENT>filepath_or_buffer.close()<EOL><DEDENT>except:  <EOL><INDENT>pass<EOL><DEDENT><DEDENT>return result<EOL>
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,
    gcs, 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'``.

    * 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'``.

    .. versionadded:: 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 None
    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.

    For all ``orient`` values except ``'table'``, default is True.

    .. versionchanged:: 0.25.0

       Not applicable for ``orient='table'``.

convert_axes : boolean, default None
    Try to convert the axes to the proper dtypes.

    For all ``orient`` values except ``'table'``, default is True.

    .. versionchanged:: 0.25.0

       Not applicable for ``orient='table'``.

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.
encoding : str, default is 'utf-8'
    The encoding to use to decode py3 bytes.

    .. versionadded:: 0.19.0

lines : boolean, default False
    Read the file as a json object per line.

    .. versionadded:: 0.19.0

chunksize : integer, default None
    Return JsonReader object for iteration.
    See the `line-delimted json docs
    <http://pandas.pydata.org/pandas-docs/stable/io.html#io-jsonl>`_
    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.

    .. versionadded:: 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.

    .. versionadded:: 0.21.0

Returns
-------
result : Series or DataFrame, depending on the value of `typ`.

See Also
--------
DataFrame.to_json

Notes
-----
Specific to ``orient='table'``, if a :class:`DataFrame` with a literal
:class:`Index` name of `index` gets written with :func:`to_json`, the
subsequent read operation will incorrectly set the :class:`Index` name to
``None``. This is because `index` is also used by :func:`DataFrame.to_json`
to denote a missing :class:`Index` name, and the subsequent
:func:`read_json` operation cannot distinguish between the two. The same
limitation is encountered with a :class:`MultiIndex` 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"}]}'