Numpy Sum Axis Intuition

Typical Axis Order Example: [Batch, Height, Width, Feature]

I’ve always thought that axis 0 is row-wise, and axis 1 is column-wise.

 row-wise (axis 0) --->  [[ 0  1]   
                          [ 0  5]]  
                            ⭡
                           column-wise (axis 1)

However, what numpy.sum gives me is the exact opposite of what I thought it would be.

>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])

>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])

So what’s going on here? Am I the only one who is wondering this?

The way to understand what “axis” means in numpy sum is that it collapses the specified axis. So when it collapses the axis 0 (the row), it becomes just one row (it sums column-wise).

Why did numpy choose to act this way?

It is possible that this might be confusing when discussing 2-d arrays; however, when discussing 3-d, 4-d, n-d arrays, this is a more straightforward way to define the axis.

# Let's experiment with 3-d array.
  
In [5]: x = np.array([[[1,2],[3,4]],[[1,2],[3,4]]])

In [6]: x
Out[6]: 
array([[[1, 2],
        [3, 4]],

       [[1, 2],
        [3, 4]]])

In [7]: x.shape
Out[7]: (2, 2, 2)

In [8]: x[0]        # axis-0
Out[8]: 
array([[1, 2],
       [3, 4]])

In [9]: x[1]        # still axis-0
Out[9]: 
array([[1, 2],
       [3, 4]])

In [10]…