Mean value of each element in multiple lists - Python

>>> a = [2,5,1,9]
>>> b = [4,9,5,10]
>>> [(g + h) / 2 for g, h in zip(a, b)]
[3.0, 7.0, 3.0, 9.5]

Referring to your title of the question, you can achieve this simply with:

import numpy as np

multiple_lists = [[2,5,1,9], [4,9,5,10]]
arrays = [np.array(x) for x in multiple_lists]
[np.mean(k) for k in zip(*arrays)]

Above script will handle multiple lists not just two. If you want to compare the performance of two approaches try:

%%time
import random
import statistics

random.seed(33)
multiple_list = []
for seed in random.sample(range(100), 100):
    random.seed(seed)
    multiple_list.append(random.sample(range(100), 100))

result = [statistics.mean(k) for k in zip(*multiple_list)]

or alternatively:

%%time
import random
import numpy as np

random.seed(33)
multiple_list = []
for seed in random.sample(range(100), 100):
    random.seed(seed)
    multiple_list.append(np.array(random.sample(range(100), 100)))

result = [np.mean(k) for k in zip(*multiple_list)]

To my experience numpy approach is much faster.

An alternate to using a list and for loop would be to use a numpy array.

import numpy as np
# an array can perform element wise calculations unlike lists. 
a, b = np.array([2,5,1,9]), np.array([4,9,5,10]) 
mean = (a + b)/2; print(mean)
>>>[ 3.   7.   3.   9.5]

What you want is the mean of two arrays (or vectors in math).

Since Python 3.4, there is a statistics module which provides a mean() function:

statistics.mean(data)

Return the sample arithmetic mean of data, a sequence or iterator of real-valued numbers.

You can use it like this:

import statistics

a = [2, 5, 1, 9]
b = [4, 9, 5, 10]

result = [statistics.mean(k) for k in zip(a, b)]
# -> [3.0, 7.0, 3.0, 9.5]

notice: this solution can be use for more than two arrays, because zip() can have multiple parameters.