Calculating Mean of arrays with different lengths
arrays, mean, numpy, python
Solution
numpy.ma.mean allows you to compute the mean of non-masked array elements. However, to use `numpy.ma.mean`, you have to first combine your three numpy arrays into one masked array:
import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([[1, 2, 3], [3, 4, 5]])
z = np.array([[7], [8]])
arr = np.ma.empty((2,3,3))
arr.mask = True
arr[:x.shape[0],:x.shape[1],0] = x
arr[:y.shape[0],:y.shape[1],1] = y
arr[:z.shape[0],:z.shape[1],2] = z
print(arr.mean(axis = 2))
yields
[[3.0 2.0 3.0]
[4.66666666667 4.0 5.0]]
Problem
Is it possible to calculate the mean of multiple arrays, when they may have different lengths? I am using numpy. So let's say I have: ``` numpy.array([[1, 2, 3, 4, 8], [3, 4, 5, 6, 0]]) numpy.array([[5, 6, 7, 8, 7, 8], [7, 8, 9, 10, 11, 12]]) numpy.array([[1, 2, 3, 4], [5, 6, 7, 8]]) ``` Now I want to calculate the mean, but ignoring elements that are 'missing' (Naturally, I can not just append zeros as this would mess up the mean) Is there a way to do this without iterating through the arrays? PS. These arrays are all 2-D, but will always have the same amount of coordinates for that array. I.e. the 1st array is 5 and 5, 2nd is 6 and 6, 3rd is 4 and 4. An example: ``` np.array([[1, 2], [3, 4]]) np.array([[1, 2, 3], [3, 4, 5]]) np.array([[7], [8]]) ``` This must give ``` (1+1+7)/3 (2+2)/2 3/1 (3+3+8)/3 (4+4)/2 5/1 ``` And graphically: ``` [1, 2] [1, 2, 3] [7] [3, 4] [3, 4, 5] [8] ``` Now imagine that these 2-D arrays are placed on top of each other with coordinates overlapping contributing to that coordinate's mean.