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# pandas.Series.to_pickle

Series.to_pickle(path, *, compression='infer', protocol=5, storage_options=None)

  Pickle (serialize) object to file.

  Parameters:
    path str, path object, or file-like object

      String, path object (implementing `os.PathLike[str]`), or file-like object implementing a binary `write()` function. File path where the pickled object will be stored.

    compression str or dict, default ‘infer’

      For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to `None` for no compression. Can also be a dict with key `'method'` set to one of {`'zip'`, `'gzip'`, `'bz2'`, `'zstd'`, `'xz'`, `'tar'`} and other key-value pairs are forwarded to `zipfile.ZipFile`, `gzip.GzipFile`, `bz2.BZ2File`, `zstandard.ZstdCompressor`, `lzma.LZMAFile` or `tarfile.TarFile`, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: `compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}`.

    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 are 0, 1, 2, 3, 4, 5. 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.

    storage_options dict, optional

      Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to `urllib.request.Request` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec.open`. Please see `fsspec` and `urllib` for more details, and for more examples on storage options refer here.

  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
      >>> original_df.to_pickle("./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