Given a list, the task is to write a Python program to compute how deviated are each of them from its list mean.
Examples:
Input : test_list = [7, 5, 1, 2, 10, 3]
Output : [2.333333333333333, 0.33333333333333304, 3.666666666666667, 2.666666666666667, 5.333333333333333, 1.666666666666667]
Explanation : Mean is 4.66667, related differences are computed.
Input : test_list = [1, 2, 3, 4, 5]
Output : [2, 1, 0, 1, 2]
Explanation : Mean is 3, related differences are computed.
Method #1 : Using loop + mean() + abs()
In this, we perform iteration of each element and compute deviation from mean using abs(), the computation of mean is done using mean().
Python3
# Python3 code to demonstrate working of # Mean deviation of Elements # Using loop + mean() + abs() from statistics import mean # initializing list test_list = [7, 5, 1, 2, 10, 3] # printing original lists print("The original list is : " + str(test_list)) res = [] # getting mean mean_val = mean(test_list) for ele in test_list: # getting deviation res.append(abs(ele - mean_val)) # printing result print("Mean deviations : " + str(res)) |
Output:
The original list is : [7, 5, 1, 2, 10, 3]
Mean deviations : [2.333333333333333, 0.33333333333333304, 3.666666666666667, 2.666666666666667, 5.333333333333333, 1.666666666666667]
Method #2 : Using list comprehension + mean()
In this similar functionalities are used as above function, difference being list comprehension is used as one-liner to solve this problem.
Python3
# Python3 code to demonstrate working of # Mean deviation of Elements # Using list comprehension + mean() from statistics import mean # initializing list test_list = [7, 5, 1, 2, 10, 3] # printing original lists print("The original list is : " + str(test_list)) res = [] # getting mean mean_val = mean(test_list) # list comprehension used for 1 liner res = [abs(ele - mean_val) for ele in test_list] # printing result print("Mean deviations : " + str(res)) |
Output:
The original list is : [7, 5, 1, 2, 10, 3]
Mean deviations : [2.333333333333333, 0.33333333333333304, 3.666666666666667, 2.666666666666667, 5.333333333333333, 1.666666666666667]
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