CODEWITHSUNDEEP
python
Tolotos
Tolotos

Reputation: 53

Average over parts in list of lists

I have a big list of lists, something like 

import numpy as np
np.array([range(1,1000), range(1,1000), range(1,1000)])

And I'd like to calculate the average of 50 values each in each column. I'd like to get something like:

np.array([[np.mean(range(1,50)), np.mean(range(51,100)), ...], [[np.mean(range(1,50)), np.mean(range(51,100)), ...], ...])

But instead of values from 1-1000 I have several text files with one column each, and I packed them together in the np.array with 

average_list = np.array([ np.genfromtxt("1.txt"), np.genfromtxt("2.txt") ])

I tried looping over parts of the list and adding 50 values together, but it doesn't seem to do what I want it to

average_list = np.array([ np.genfromtxt("1.txt"), np.genfromtxt("2.txt") ])
new_list = []
n=100
for i in range(len(average_list)):
    for j in range(len(average_list[i])):
        while n < j < n+50:
            average_list[i,j]+=average_list[i,j+1]
            j+=1
            new_list.append(average_list[i,j])
        print new_list
        n+=50

Upvotes: 5

Views: 243

Answers (2)

AbdealiLoKo
AbdealiLoKo

Reputation: 3347

Simple and easy to read solution is to run a map over the outer list and run a for loop over the indices of the inner loop at every space of 50.

Here's a demo:

length = 3
a = np.array([range(1,10), range(1,10)])
map(lambda y: [np.mean(y[i:i+length]) for i in range(0, len(y), length)], a)

The above code takes the average of every 3 elements You could also use xrange if using python2

Upvotes: 4

tobias_k
tobias_k

Reputation: 82939

You can first reshape the array so that each row is one of the groups of 50 elements, and then apply np.mean to each of those rows, then reshape again.

>>> a = np.array([range(1000), range(1000), range(1000)])
>>> b = np.reshape(a, (60, 50))
>>> c = np.apply_along_axis(np.mean, 1, b)
>>> np.reshape(c, (3, 20))
array([[  24.5,   74.5,  124.5,  174.5,  224.5,  274.5,  324.5,  374.5,
         424.5,  474.5,  524.5,  574.5,  624.5,  674.5,  724.5,  774.5,
         824.5,  874.5,  924.5,  974.5],
       [  24.5,   74.5,  124.5,  174.5,  224.5,  274.5,  324.5,  374.5,
         424.5,  474.5,  524.5,  574.5,  624.5,  674.5,  724.5,  774.5,
         824.5,  874.5,  924.5,  974.5],
       [  24.5,   74.5,  124.5,  174.5,  224.5,  274.5,  324.5,  374.5,
         424.5,  474.5,  524.5,  574.5,  624.5,  674.5,  724.5,  774.5,
         824.5,  874.5,  924.5,  974.5]])

Upvotes: 0

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