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Fonction to_pickle - module pandas

Signature de la fonction to_pickle

def to_pickle(obj: Any, filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], compression: Union[str, Dict[str, Any], NoneType] = 'infer', protocol: int = 5, storage_options: Optional[Dict[str, Any]] = None) 

Description

to_pickle.__doc__

Pickle (serialize) object to file.

Parameters
----------
obj : any object
    Any python object.
filepath_or_buffer : str, path object or file-like object
    File path, URL, or buffer where the pickled object will be stored.

    .. versionchanged:: 1.0.0
       Accept URL. URL has to be of S3 or GCS.

compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
    If 'infer' and 'path_or_url' is path-like, then detect compression from
    the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
    compression) If 'infer' and 'path_or_url' is not path-like, then use
    None (= no decompression).
protocol : int
    Int which indicates which protocol should be used by the pickler,
    default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
    values for this parameter depend on the version of Python. For Python
    2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
    For Python >= 3.4, 4 is a valid value. A negative value for the
    protocol parameter is equivalent to setting its value to
    HIGHEST_PROTOCOL.

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

    .. [1] https://docs.python.org/3/library/pickle.html

See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> pd.to_pickle(original_df, "./dummy.pkl")

>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9

>>> import os
>>> os.remove("./dummy.pkl")