How To Convert DataFrame To Pickle File?


How To Convert DataFrame To Pickle File?

Table Of Contents:

  1. Syntax ‘isna( )’ Method In Pandas.
  2. Examples ‘isna( )’ Method.

(1) Syntax:

DataFrame.to_pickle(path, compression='infer', protocol=5, storage_options=None)

Description:

  • Convert DataFrame To Pickle File.

Parameters:

  • path: str, path object, or file-like object – String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored.
  • compression: str 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 to zipfile.ZipFilegzip.GzipFilebz2.BZ2Filezstandard.ZstdCompressor or tarfile.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}.
  • protocol: int – 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.
    https://docs.python.org/3/library/pickle.html.
  • storage_options: dict, 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.

(2) Examples Of to_pickle() Method:

Example-1:

original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
original_df

Output:

# Pickling The DataFrame

original_df.to_pickle("./dummy.pkl")  

Output:

# UnPickling The DataFrame

unpickled_df = pd.read_pickle("./dummy.pkl")
unpickled_df

Output:

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