xorbits.numpy.savez_compressed(file, *args, **kwds)#

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

Provide arrays as keyword arguments to store them under the corresponding name in the output file: savez(fn, x=x, y=y).

If arrays are specified as positional arguments, i.e., savez(fn, x, y), their names will be arr_0, arr_1, etc.

  • file (str or file) – Either the filename (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 filename if it is not already there.

  • args (Arguments, optional) – Arrays to save to the file. Please use keyword arguments (see kwds below) to assign names to arrays. Arrays specified as args will be named “arr_0”, “arr_1”, and so on.

  • kwds (Keyword arguments, optional) – Arrays to save to the file. Each array will be saved to the output file with its corresponding keyword name.

Return type


See also


Save a single array to a binary file in NumPy format.


Save an array to a file as plain text.


Save several arrays into an uncompressed .npz file format


Load the files created by savez_compressed.


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.

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.


>>> 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']))  
>>> print(np.array_equal(test_vector, loaded['b']))  


This method has not been implemented yet. Xorbits will try to execute it with numpy.

This docstring was copied from numpy.