Python Numpy: Operation on each sequential element without for loop?

Question

This is an extremely simple question, but extensive search has not provided me with a satisfactory answer.

I have an array of numbers that evolve "over time" e.g. x = [1, 2, 3, 4, 5] and I want to calculate the mean at each timepoint. With a for-loop I would simply do

import numpy as np

x = [1, 2, 3, 4, 5]
y = np.empty(5)

for i in range(5):

    y[i] = np.mean(x[0:i+1])

print(y)

[ 1.   1.5  2.   2.5  3. ]

In the processes I am working with, the numbers do not necessarily follow a simple dynamic like in the above. I wonder if there is some general way of applying a operation (such as calculating the mean) in a 'running' fashion, that is quicker than a for-loop?

"I wonder if there is some general way of applying a operation (such as calculating the mean) in a 'running' fashion, that is quicker than a for-loop?" Many numpy ufuncs have an accumulate method. — Paul Panzer
– Paul Panzer, Commented Apr 14, 2017 at 11:39

Oliver W. · Accepted Answer · 2017-04-14 10:25:11Z

2

How about

a = np.array([1, 2, 3, 4, 5])
np.cumsum(a)/(np.arange(1, a.size + 1))

?

That will work for calculating a running average.

I wonder if there is some general way of applying a operation (such as calculating the mean) in a 'running' fashion

I can't provide an answer to this. It depends on the operation.

answered Apr 14, 2017 at 10:25

Oliver W.

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