Getting mean in list of lists-CodePudding

How can i write this code in a shorter for loop? The code is calculating the mean of x and y values at each index

cluster_list = [[(1,2),(3,4)],[(5,6),(7,8)]]

#Creating lists to calculate mean x and y values for centroids : 
mean_x_0 =[]
mean_y_0 =[]
mean_x_1 =[]
mean_y_1 =[]

# mean calculation
for i in cluster_list[0]:
    mean_x_0.append(i[0])
    mean_cluster_x_0 = sum(mean_x_0)/len(mean_x_0)
for i in cluster_list[0]:
    mean_y_0.append(i[1])
    mean_cluster_y_0 = sum(mean_y_0)/len(mean_y_0)
for i in cluster_list[1]:
    mean_x_1.append(i[0])
    mean_cluster_x_1 = sum(mean_x_1)/len(mean_x_1)
for i in cluster_list[1]:
    mean_y_1.append(i[1])
    mean_cluster_y_1 = sum(mean_y_1)/len(mean_y_1)

print(mean_cluster_x_0,mean_cluster_y_0)
print(mean_cluster_x_1,mean_cluster_y_1)

CodePudding user response：

use statistics.mean

import statistics

cluster_list = [ [ (1,2),(3,4) ] , [(5,6),(7,8)] ]

# convert the x of cluster[0] to list
cluster_x_0 = [cluster_list[0][i][0] for i in range(len(cluster_list[0]))]

# convert the y of cluster[0] to list
cluster_y_0 = [cluster_list[0][i][1] for i in range(len(cluster_list[0]))]

print (statistics.mean(cluster_x_0) , statistics.mean(cluster_y_0))

CodePudding user response：

Seems like the right opportunity to learn numpy:

import numpy as np

np.array(cluster_list)[0].mean(axis=0)

Output: array([2., 3.])

Or, as variables:

mean_cluster_x_0, mean_cluster_y_0 = np.array(cluster_list)[0].mean(axis=0)

CodePudding user response：

Here is an tweaked version of @