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

• Kasravnd: Can you give a Minimal, Complete, and Verifiable example? or show your expected output?
• chown: Whats the error that you're getting?
• Daniel Velden: 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

Answer 1

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