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Calculate Mean Value Of 2D Numpy Arrays Stored In A List

- 1 answer

a:

[array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
        [0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
        [0.11664   , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
        [0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
 array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
        [0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
        [0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534],
                         ...

I can do a[0].mean and I will get desired result. By I want to do it to the whole length of the 'a' with for loop.

I have tried:

mean_all = []

for i in len(dist):
    mean = dist[i].mean
    mean_all.append(mean)

TypeError: 'int' object is not iterable

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Answer

First of all, dist[0].mean returns a function and NOT the mean. You need, in general, dist[0].mean().

You can avoid the for loop easily using list comprehension:

from numpy import array

dist = [array([[0.10865657, 0.10638294, 0.10471012, 0.09508586, 0.09283491],
               [0.10892282, 0.10664408, 0.10496752, 0.09531553, 0.09305617],
               [0.11664   , 0.1143077 , 0.11259081, 0.1026154 , 0.10025029],
               [0.11626453, 0.11392252, 0.11219875, 0.10217754, 0.09980005]]),
        array([[0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
               [0.04213751, 0.04178241, 0.04158858, 0.04331489, 0.04447674],
               [0.04267657, 0.04255925, 0.04253528, 0.04520177, 0.04655534]])]

mean_all = [dist[i].mean() for i in range(len(dist))]

print(mean_all)
[0.10536720549999998, 0.04307523133333334]

If you really want to use the for loop, use this:

mean_all = []
for i in range(len(dist)):
    mean = dist[i].mean()
    mean_all.append(mean)

print(mean_all)
[0.10536720549999998, 0.04307523133333334]
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source: stackoverflow.com
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