Sometimes, while working with records, we can have a problem in which we need to average all the columns of a container of lists which are tuples. This kind of application is common in web development domain. Let’s discuss certain ways in which this task can be performed.
Method #1 : Using sum() + list comprehension + zip()
This task can be performed using combination of above functions. In this, we cumulate the like index elements, i.e columns using zip(), and then iterate through them using list comprehension and perform summation using sum(). We divide the each result with No of rows for average computation.
Python3
# Python3 code to demonstrate working of # Column Mean in tuple list # using list comprehension + sum() + zip() # initialize list test_list = [(1, 2, 3), (6, 7, 6), (1, 6, 8)] # printing original list print("The original list : " + str(test_list)) # Column Mean in tuple list # using list comprehension + sum() + zip() res = [sum(ele) / len(test_list) for ele in zip(*test_list)] # printing result print("The Cumulative column mean is : " + str(res)) |
The original list : [(1, 2, 3), (6, 7, 6), (1, 6, 8)] The Cumulative column mean is : [2.6666666666666665, 5.0, 5.666666666666667]
Time complexity: O(n), where n is the total number of elements in the input list of tuples.
Auxiliary space: O(m), where m is the number of columns in the input list of tuples.
Method #2 : Using zip() + map() + sum()
This method is similar to the above method. In this, the task performed by list comprehension is performed by map(), which extends the summation of columns to zipped elements. We divide the each result with No of rows for average computation.
Python3
# Python3 code to demonstrate working of # Column Mean in tuple list # using zip() + map() + sum() def avg(list): return sum(list)/len(list) # initialize list test_list = [(1, 2, 3), (6, 7, 6), (1, 6, 8)] # printing original list print("The original list : " + str(test_list)) # Column Mean in tuple list # using zip() + map() + sum() res = list(map(avg, zip(*test_list))) # printing result print("The Cumulative column mean is : " + str(res)) |
The original list : [(1, 2, 3), (6, 7, 6), (1, 6, 8)] The Cumulative column mean is : [2.6666666666666665, 5.0, 5.666666666666667]
Time complexity: O(n), where n is the total number of elements in the input list of tuples.
Auxiliary space: O(m), where m is the number of columns in the input list of tuples.
Approach 3: Using numpy library
We can use numpy library to calculate mean of columns in the list of tuples.
Python3
import numpy as np#initialize listtest_list = [(1, 2, 3), (6, 7, 6), (1, 6, 8)]#printing original listprint("The original list : " + str(test_list))#Column Mean in tuple list using numpyres = np.mean(test_list, axis=0)#printing resultprint("The Cumulative column mean is : " + str(res)) |
Output:
The original list : [(1, 2, 3), (6, 7, 6), (1, 6, 8)]
The Cumulative column mean is : [2.66666667 5. 5.66666667]
Time Complexity: O(n), where n is the number of elements in the list.
Auxiliary Space: O(n), numpy uses additional memory for computation.
Approach 4 : Using statistics.mean()
Python3
# Python3 code to demonstrate working of# Column Mean in tuple list# initialize listtest_list = [(1, 2, 3), (6, 7, 6), (1, 6, 8)]# printing original listprint("The original list : " + str(test_list))# Column Mean in tuple listres=[]for i in range(0,len(test_list)): x=[] for j in range(0,len(test_list[i])): x.append(test_list[j][i]) import statistics y=statistics.mean(x) res.append(y) # printing resultprint("The Cumulative column mean is : " + str(res)) |
The original list : [(1, 2, 3), (6, 7, 6), (1, 6, 8)] The Cumulative column mean is : [2.6666666666666665, 5, 5.666666666666667]
Time Complexity : O(N*N)
Auxiliary Space : O(N)
