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: 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_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 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

unpickledsame 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")

Input/output pandas.read_table