pandas.read_pickle#

pandas.read_pickle(filepath_or_buffer, compression='infer', storage_options=None)[source]#

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', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor 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}.

New 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.

New in version 1.2.0.

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