Max of each 2D matrix in 4D NumPy array

Question

I have the 4D array, which is defined as follows: B = np.array( [[[[0.5000, 0.5625], [0.5000, 0.5625]], [[1.2500, 0.5000], [0.5625, 0.6875]], [[0.5625, 0.6250], [0.5000, 0.5625]]]] ) I want to take a max of each 2D matrix, such that I get the result of: array([0.5625, 1.250, 0.6250]) Similarly, I want to take the min of each 2D matrix, such that I get the result of: array([0.5000, 0.5000, 0.5000]) However, when doing np.max(B, axis=0), np.max(B, axis=1), np.max(B, axis=2), or np.max(B, axis=3) -- none of these gives a right answer. Is there another argument I need to specify to do this operation? The correct solution should not use any loops and ideally one function call. Select the correct answer from above options

Answer 1

I think, issue is the misunderstanding of how a axis argument works. For most of these aggregation methods of axis keyword is a axis (or axes) to project along, i.e. these axes are "removed" from a result. So in this case you want to call something like: In [7]: B.max((0, 2, 3)) Out[7]: array([0.5625, 1.25 , 0.625 ]) same thing for min In [8]: B.min((0, 2, 3)) Out[8]: array([0.5, 0.5, 0.5]) Or you can call the numpy method directly In [9]: np.max(B, axis=(0, 2, 3)) Out[9]: array([0.5625, 1.25 , 0.625 ]) Do check out Data Science with Python course which helps you understand from scratch.