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numpy.savez_compressed
numpy.savez_compressed(file, *args, **kwds)[source]- 
    
Save several arrays into a single file in compressed
.npzformat.If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored filenames are arr_0, arr_1, etc.
- Parameters
 - 
      
filestr 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
.npzextension will be appended to the filename if it is not already there. argsArguments, 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. kwdsKeyword 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
.npzfile format numpy.load- 
       
Load the files created by savez_compressed.
 
Notes
The
.npzfile format is a zipped archive of files named after the variables they contain. The archive is compressed withzipfile.ZIP_DEFLATEDand each file in the archive contains one variable in.npyformat. For a description of the.npyformat, seenumpy.lib.format.When opening the saved
.npzfile withloadaNpzFileobject is returned. This is a dictionary-like object which can be queried for its list of arrays (with the.filesattribute), 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 
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 https://numpy.org/doc/1.20/reference/generated/numpy.savez_compressed.html