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 (implementing
os.PathLike[str]
), or file-like object implementing a binarywrite()
function. File path where the pickled object will be stored.- compressionstr or dict, default ‘infer’
For on-the-fly compression of the output data. If ‘infer’ and ‘path’ 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). Set to
None
for no compression. Can also be a dict with key'method'
set to one of {'zip'
,'gzip'
,'bz2'
,'zstd'
,'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
,gzip.GzipFile
,bz2.BZ2File
,zstandard.ZstdCompressor
ortarfile.TarFile
, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}
.New 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 possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
- 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 tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.New in version 1.2.0.
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 bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> original_df.to_pickle("./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