Python - Element-wise means of multiple matrices with Numpy

Question

I am implementing a sliding-window model, where I want to initialize a matrix @t as the element-wise means of the previous N matrices, where N is the window size. This is my initial attempt, which displays the last N matrices:

list_of_arrays = [np.array([]) for i in range(3)]
N=2 # window size
# past 3 matrices
list_of_arrays[0] = np.array([[0.1,0.2],[0.3,0.4]])
list_of_arrays[1] = np.array([[0.5,0.6],[0.7,0.8]])
list_of_arrays[2] = np.array([[0.9,1.0],[1.1,1.2]])

# at t=3, get element-wise means of previous N matrices
t=3
range1 = lambda start, end: range(start, end+1) # modified range function
answer = [list_of_arrays[t-j] for j in range1(1,N)]

The desired answer is the element-wise means of the past N matrices. For the series above, it is:

(list_of_arrays[2]+list_of_arrays[1]) / 2 = [[0.7,0.8],[0.9,1.0]]

How should I modify the list comprehension on the answer line to get the desired answer?

Answer 1

I figured it out. This is the necessary modification to the indicated answer line in the question:

answer = np.mean([list_of_arrays[t-j] for j in range1(1,N)], axis = 0)
>> array([[ 0.7,  0.8],
>>        [ 0.9,  1. ]])