# Numpy Sum Axis Intuition

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]…