pandas.Series.to_pickle#

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

Pickle (serialize) object to file.

Parameters:

pathstr, path object, or file-like object

String, path object (implementingos.PathLike[str]), or file-likeobject implementing a binarywrite() function. File path wherethe pickled object will be stored.

compressionstr or dict, default ‘infer’

For on-the-fly compression of the output data. If ‘infer’ and ‘path’ ispath-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 toNone for no compression.Can also be a dict with key'method' setto one of {'zip','gzip','bz2','zstd','xz','tar'} andother key-value pairs are forwarded tozipfile.ZipFile,gzip.GzipFile,bz2.BZ2File,zstandard.ZstdCompressor,lzma.LZMAFile ortarfile.TarFile, respectively.As an example, the following could be passed for faster compression and to createa reproducible gzip archive:compression={'method':'gzip','compresslevel':1,'mtime':1}.

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

protocolint

Int which indicates which protocol should be used by the pickler,default HIGHEST_PROTOCOL (see[1] paragraph 12.1.2). The possiblevalues are 0, 1, 2, 3, 4, 5. A negative value for the protocolparameter is equivalent to setting its value to HIGHEST_PROTOCOL.

[1]

https://docs.python.org/3/library/pickle.html.

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 pairsare forwarded tourllib.request.Request as header options. For otherURLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs areforwarded tofsspec.open. Please seefsspec andurllib for moredetails, and for more examples on storage options referhere.

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  bar0    0    51    1    62    2    73    3    84    4    9>>>original_df.to_pickle("./dummy.pkl")

>>>unpickled_df=pd.read_pickle("./dummy.pkl")>>>unpickled_df   foo  bar0    0    51    1    62    2    73    3    84    4    9