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Fonction read_json - module pandas

Signature de la fonction read_json

def read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates: bool = True, numpy: bool = False, precise_float: bool = False, date_unit=None, encoding=None, lines: bool = False, chunksize: Optional[int] = None, compression: Union[str, Dict[str, Any], NoneType] = 'infer', nrows: Optional[int] = None, storage_options: Optional[Dict[str, Any]] = None) 

Description

read_json.__doc__

Convert a JSON string to pandas object.

Parameters
----------
path_or_buf : a valid JSON str, path object or file-like object
    Any valid string path is acceptable. The string could be a URL. Valid
    URL schemes include http, ftp, s3, and file. For file URLs, a host is
    expected. A local file could be:
    ``file://localhost/path/to/table.json``.

    If you want to pass in a path object, pandas accepts any
    ``os.PathLike``.

    By file-like object, we refer to objects with a ``read()`` method,
    such as a file handle (e.g. via builtin ``open`` function)
    or ``StringIO``.
orient : str
    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'``.

typ : {'frame', 'series'}, default 'frame'
    The type of object to recover.

dtype : bool 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 : bool, 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 : bool or list of str, default True
    If True then default datelike columns may be converted (depending on
    keep_default_dates).
    If False, no dates will be converted.
    If a list of column names, then those columns will be converted and
    default datelike columns may also be converted (depending on
    keep_default_dates).

keep_default_dates : bool, default True
    If parsing dates (convert_dates is not False), then try to parse the
    default datelike columns.
    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'``.

numpy : bool, 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.

    .. deprecated:: 1.0.0

precise_float : bool, 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 : str, 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.

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

chunksize : int, optional
    Return JsonReader object for iteration.
    See the `line-delimited json docs
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#line-delimited-json>`_
    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.

    .. versionchanged:: 1.2

       ``JsonReader`` is a context manager.

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.

nrows : int, optional
    The number of lines from the line-delimited jsonfile that has to be read.
    This can only be passed if `lines=True`.
    If this is None, all the rows will be returned.

    .. versionadded:: 1.1

storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc., if using a URL that will
    be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
    will be raised if providing this argument with a non-fsspec URL.
    See the fsspec and backend storage implementation docs for the set of
    allowed keys and values.

    .. versionadded:: 1.2.0

Returns
-------
Series or DataFrame
    The type returned depends on the value of `typ`.

See Also
--------
DataFrame.to_json : Convert a DataFrame to a JSON string.
Series.to_json : Convert a Series to a JSON string.

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"}]}'