# pandas.read_pickle

pandas.read_pickle(filepath_or_buffer, compression='infer', storage_options=None)

  Load pickled pandas object (or any object) from file.

  Warning

  Loading pickled data received from untrusted sources can be unsafe. See here.

  Parameters
    filepath_or_buffer str, path object or file-like object

      File path, URL, or buffer where the pickled object will be loaded from.

      Changed in version 1.0.0: Accept URL. URL is not limited to S3 and 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).

    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` as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to `fsspec`. Please see `fsspec` and `urllib` for more details.

      New in version 1.2.0.

  Returns
    unpickled same type as object stored in file

  See also

  `DataFrame.to_pickle`

    Pickle (serialize) DataFrame object to file.

  `Series.to_pickle`

    Pickle (serialize) Series object to file.

  `read_hdf`

    Read HDF5 file into a DataFrame.

  `read_sql`

    Read SQL query or database table into a DataFrame.

  `read_parquet`

    Load a parquet object, returning a DataFrame.

  Notes

  read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3.

  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")