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# 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_bufferstr, path object, or file-like object

      String, path object (implementing `os.PathLike[str]`), or file-like object implementing a binary `readlines()` function. Also accepts URL. URL is not limited to S3 and GCS.

    compressionstr or dict, default ‘infer’

      For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ 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). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to `None` for no decompression. 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.ZstdDecompressor`, `lzma.LZMAFile` or `tarfile.TarFile`, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: `compression={'method': 'zstd', 'dict_data': my_compression_dict}`.

      Added in version 1.5.0: Added support for .tar files.

      Changed in version 1.4.0: Zstandard support.

    storage_optionsdict, 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.

  Returns:
    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 provided the object was serialized with to_pickle.

  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