For loop to obtain sum and mean on np 3d array - numpy

Question

I have the following array
arr = np.array([[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]])
I want to go through each element and sum on axis 0, so I do:
lst = []
for x in arr:
for y in np.sum(x,axis=0):
lst.append(y)
where now the lst is
[5, 7, 9, 17, 19, 21]
However I want the output to be in the following form:
[[5, 7, 9], [17, 19, 21]]
to then take the mean of its axis 0 namely (5+17)/2 and so on. The final output should look like
[11., 13., 15.]
I wonder how can I do this? Is it possible to write this whole operation in a compact form as list comprehension?
Update: To get the final output I can do:
np.mean(np.reshape(lst, (len(arr),-1)),axis=0)
Yet I am sure there is a Pythonic way of doing this

Answer 1

In [5]: arr = np.array([[[1, 2, 3], [4, 5, 6]],
...: [[7, 8, 9], [10, 11, 12]]])
In [7]: arr
Out[7]:
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
The for iterates on the 1st dimension, as though it was a list of arrays:
In [8]: for x in arr:print(x)
[[1 2 3]
[4 5 6]]
[[ 7 8 9]
[10 11 12]]
list(arr) also makes a list (but it is slower than `arr.tolist()).
One common way of iterating on other dimensions is to use an index:
In [10]: for i in range(2):print(arr[:,i])
[[1 2 3]
[7 8 9]]
[[ 4 5 6]
[10 11 12]]
You could also transpose the array placing the desired axis first.
But you don't need to iterate
In [13]: arr.sum(axis=1)
Out[13]:
array([[ 5, 7, 9],
[17, 19, 21]])
In [14]: arr.sum(axis=1).mean(axis=0)
Out[14]: array([11., 13., 15.])