save_joblib

pyhelpers.store.save_joblib(data, path_to_file, verbose=False, **kwargs)[source]

Save data to a Joblib file.

Parameters:

data (Any) – Data that could be dumped by joblib.dump.
path_to_file (str | os.PathLike) – Path where a pickle file is saved.
verbose (bool | int) – Whether to print relevant information in console; defaults to False.
kwargs – [Optional] parameters of joblib.dump.

Examples:

>>> from pyhelpers.store import save_joblib
>>> from pyhelpers.dirs import cd
>>> from pyhelpers._cache import example_dataframe
>>> import numpy as np

>>> joblib_pathname = cd("tests\data", "dat.joblib")

>>> # Example 1:
>>> joblib_dat = example_dataframe().to_numpy()
>>> joblib_dat
array([[-0.1276474, 51.5073219],
       [-1.9026911, 52.4796992],
       [-2.2451148, 53.4794892],
       [-1.5437941, 53.7974185]])

>>> save_joblib(joblib_dat, joblib_pathname, verbose=True)
Saving "dat.joblib" to "tests\data\" ... Done.

>>> # Example 2:
>>> np.random.seed(0)
>>> joblib_dat = np.random.rand(100, 100)
>>> joblib_dat
array([[0.5488135 , 0.71518937, 0.60276338, ..., 0.02010755, 0.82894003,
        0.00469548],
       [0.67781654, 0.27000797, 0.73519402, ..., 0.25435648, 0.05802916,
        0.43441663],
       [0.31179588, 0.69634349, 0.37775184, ..., 0.86219152, 0.97291949,
        0.96083466],
       ...,
       [0.89111234, 0.26867428, 0.84028499, ..., 0.5736796 , 0.73729114,
        0.22519844],
       [0.26969792, 0.73882539, 0.80714479, ..., 0.94836806, 0.88130699,
        0.1419334 ],
       [0.88498232, 0.19701397, 0.56861333, ..., 0.75842952, 0.02378743,
        0.81357508]])

>>> save_joblib(joblib_dat, joblib_pathname, verbose=True)
Updating "dat.joblib" at "tests\data\" ... Done.