def to_pickle(obj, path, compression='infer',
              protocol=pickle.HIGHEST_PROTOCOL):
    """
    Pickle (serialize) object to file.

    Parameters
    ----------
    obj : any object
        Any python object.
    path : str
        File path where the pickled object will be stored.
    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
        A string representing the compression to use in the output file. By
        default, infers from the file extension in specified path.

        .. versionadded:: 0.20.0
    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.

        .. [1] https://docs.python.org/3/library/pickle.html
        .. versionadded:: 0.21.0

    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")
    """
    path = _stringify_path(path)
    f, fh = _get_handle(path, 'wb',
                        compression=compression,
                        is_text=False)
    if protocol < 0:
        protocol = pickle.HIGHEST_PROTOCOL
    try:
        f.write(pickle.dumps(obj, protocol=protocol))
    finally:
        f.close()
        for _f in fh:
            _f.close()
def to_pickle ( obj , path , compression = 'infer' , protocol = pickle . HIGHEST_PROTOCOL ) : path = _stringify_path ( path ) f , fh = _get_handle ( path , 'wb' , compression = compression , is_text = False ) if protocol < 0 : protocol = pickle . HIGHEST_PROTOCOL try : f . write ( pickle . dumps ( obj , protocol = protocol ) ) finally : f . close ( ) for _f in fh : _f . close ( )
Pickle (serialize) object to file.

    Parameters
    ----------
    obj : any object
        Any python object.
    path : str
        File path where the pickled object will be stored.
    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
        A string representing the compression to use in the output file. By
        default, infers from the file extension in specified path.

        .. versionadded:: 0.20.0
    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.

        .. [1] https://docs.python.org/3/library/pickle.html
        .. versionadded:: 0.21.0

    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")