While working with Python, we can have a problem in which we need to find mean of a list cumulative with float elements. This problem is common in Data Science domain. Let’s discuss certain ways in which this problem can be solved.
Method #1 : Using loop + formula The simpler manner to approach this problem is to employ the formula for finding mean and perform using loop shorthands. This is the most basic approach to solve this problem.
Python3
# Python3 code to demonstrate working of # Average of Float Numbers # using loop + formula import math# initialize list test_list = [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] # printing original list print("The original list is : " + str(test_list)) # Average of Float Numbers# using loop + formula sum = 0for ele in test_list: sum += eleres = sum / len(test_list)# printing result print("The mean of float list elements is : " + str(res)) |
The original list is : [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] The mean of float list elements is : 8.716666666666667
Time Complexity: O(n) where n is the number of elements in the list “test_list”.
Auxiliary Space: O(n) where n is the number of elements in the list “test_list”.
Method #2 : Using statistics.fmean() This task can also be performed using inbuilt function of fmean(). This is new in Python versions >= 3.8.
Python3
# Python3 code to demonstrate working of # Average of Float Numbers# using statistics.fmean() import statistics # initialize list test_list = [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] # printing original list print("The original list is : " + str(test_list)) # Average of Float Numbers# using statistics.fmean() res = statistics.fmean(test_list) # printing result print("The mean of float list elements is : " + str(res)) |
The original list is : [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] The mean of float list elements is : 8.716666666666667
Time Complexity: O(n), where n is the number of elements in the list “test_list”.
Auxiliary Space: O(1), where constant extra space is required
Method #3 : Using numpy.mean()
Note: Install numpy module using command “pip install numpy”
Python3
import numpy as np# initialize list test_list = [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] # printing original list print("The original list is : " + str(test_list)) # Average of Float Numbers# using numpy.mean() res = np.mean(test_list) # printing result print("The mean of float list elements is : " + str(res))#This code is contributed by Edula Vinay Kumar Reddy |
We can use numpy library’s mean function to calculate the mean of a list of floats.
Output:
The original list is : [6.1, 7.2, 3.3, 9.4, 10.6, 15.7]
The mean of float list elements is : 8.716666666666667
Time complexity: O(n) as we are iterating through the list only once.
Auxiliary Space: O(1) as we are not using any extra data structures.
Approach 4: Using a built-in Python function sum() and len()
Python3
import mathtest_list = [6.1, 7.2, 3.3, 9.4, 10.6, 15.7]print("The original list is : " + str(test_list))#Using sum() and len() functionsmean = sum(test_list) / len(test_list)print("The mean of float list elements is : " + str(mean))#This code is contributed by Edula Vinay Kumar Reddy |
The original list is : [6.1, 7.2, 3.3, 9.4, 10.6, 15.7] The mean of float list elements is : 8.716666666666667
Time complexity: O(n) as we are iterating through the list only once using the sum() function.
Auxiliary Space: O(1) as we are not using any extra data structures.
