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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. 
 NotesThe .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/1.19/reference/generated/numpy.savez_compressed.html