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I'd like to ask for better way to bitwise and over all elements in matrix rows.

I have an array:

import numpy as np
A = np.array([[1,1,1,4],   #shape is (3, 5) - one sample
              [4,8,8,16],
              [4,4,4,4]], 
              dtype=np.uint16)
B = np.array([[[1,1,1,4],  #shape is (2, 3, 5) - two samples
              [4,8,8,16],
              [4,4,4,4]], 
             [[1,1,1,4],
              [4,8,8,16],
              [4,4,4,4]]]
              dtype=np.uint16)
Example and expected output:
resultA = np.bitwise_and(A, axis=through_rows) # doesn't work 
# expected output should be a bitwise and over elements in rows resultA:
  array([[0],
         [4]])
resultB = np.bitwise_and(B, axis=through_rows) # doesn't work
# expected output should be a bitwise and over elements in rows
# for every sample resultB:
  array([[[0],
          [4]],
        [[0],
         [4]]])
But my output is:
resultA = np.bitwise_and(A, axis=through_rows) # doesn't work
  File "<ipython-input-266-4186ceafed83>", line 13
dtype=np.uint16)
SyntaxError: invalid syntax
Because, numpy.bitwise_and(x1, x2[, out]) have two array as input. How can I get my expected output?
                it should be axis=1 -> I want to compute A[0,0] & A[0,1] & A[0,2] & A[0,3] for each row in sample. And expected outputs are under every example.
– Jan
                Jan 20, 2017 at 10:40
The dedicated function for this would be bitwise_and.reduce:
resultB = np.bitwise_and.reduce(B, axis=2)
Unfortunately in numpy prior to v1.12.0 bitwise_and.identity is 1, so this doesn't work. For those older versions a workaround is the following:
resultB = np.bitwise_and.reduceat(B, [0], axis=2)
        
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