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numpy.savez_compressed

numpy.savez_compressed(file, *args, **kwds)[source]

Save several arrays into a single file in compressed .npz format.

If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored file names are arr_0, arr_1, etc.

Parameters:

file : str or file

Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the .npz extension will be appended to the file name if it is not already there.

args : Arguments, optional

Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside savez, the arrays will be saved with names “arr_0”, “arr_1”, and so on. These arguments can be any expression.

kwds : Keyword arguments, optional

Arrays to save to the file. Arrays will be saved in the file with the keyword names.

Returns:

None

See also

numpy.save
Save a single array to a binary file in NumPy format.
numpy.savetxt
Save an array to a file as plain text.
numpy.savez
Save several arrays into an uncompressed .npz file format
numpy.load
Load the files created by savez_compressed.

Notes

The .npz file format is a zipped archive of files named after the variables they contain. The archive is compressed with zipfile.ZIP_DEFLATED and each file in the archive contains one variable in .npy format. For a description of the .npy format, see numpy.lib.format or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html

When opening the saved .npz file with load a NpzFile object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the .files attribute), and for the arrays themselves.

Examples

>>> test_array = np.random.rand(3, 2)
>>> test_vector = np.random.rand(4)
>>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
>>> loaded = np.load('/tmp/123.npz')
>>> print(np.array_equal(test_array, loaded['a']))
True
>>> print(np.array_equal(test_vector, loaded['b']))
True