Python Split Array use For Loop to Mean every split and put it back together

Question

I'm coding for an Coherence project and I'm now stuck at the problem to mean my array of values in splitted parts.

So ym task would be: 1. Take my array of values (R) split it in a certain number of array parts (split by epoch) 2. enter a loop to run it automatically. 3. in the loop every value of each split part of the original array should be avaraged

Maybe the solution is damn simple, but I got stuck and I miss the wood for the forest.

Here is my approach (Rxx, epochs are defined above):

epoch_Rxx = np.array_split(Rxx,epochs)
for i in range(0,epochs):
Rxx_mean = np.zeros(epochs)
Rxx_mean[i] = np.mean(Rxx[i])

In the end I want from the e.g. Rxx = 100 values and epochs = 10

--> Rxx_mean = 10 values each to be the avaraged value of each epoch.

Greetings,

Daniel

Can you give a Minimal, Complete, and Verifiable example? or show your expected output? — Kasravnd
– Kasravnd, Commented May 27, 2015 at 11:48
there is no error, but the for loop serves the results of || array[0 ,0 ,0 ,0, 0.1521] for e.g. epoch is 5 — Daniel Velden
– Daniel Velden, Commented May 27, 2015 at 12:03

Rob · Accepted Answer · 2015-05-27 11:57:19Z

2

Is this what you are after?

import numpy as np

Rxx = np.arange(100)
epochs = 10
Rxx_mean = []

epoch_Rxx = np.array_split(Rxx,epochs)
for i in range(0,epochs):
  Rxx_mean.append(np.mean(epoch_Rxx[i]))

print Rxx_mean

answered May 27, 2015 at 11:57

Rob

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