Given a list, compute the percentage of positive elements in the list.
Input : test_list = [4, 6, -2, 3, -8, -9, -1, 8, 9, 1]
Output : 60.0
Explanation : 6/10 elements are positive.Input : test_list = [-4, 6, -2, 3, -8, -9, -1, 8, 9, 1]
Output : 50.0
Explanation : 5/10 elements are positive.
Method #1 : Using len() + list comprehension
In this, we construct positive elements list using list comprehension and then compute the length of lists using len(), both lengths are divided and multiplied by 100 to get percentage count.
Python3
# Python3 code to demonstrate working of # Positive values percentage # Using len() + list comprehension# initializing listtest_list = [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]# printing original listprint("The original list is : " + str(test_list))# getting filtered list using comprehension and # division to get fractionres = (len([ele for ele in test_list if ele > 0]) / len(test_list)) * 100# printing result print("Positive elements percentage : " + str(res)) |
Output:
The original list is : [4, 6, -2, 3, -8, 0, -1, 8, 9, 1] Positive elements percentage : 60.0
Time Complexity: O(n)
Auxiliary Space: O(n)
Method #2 : Using filter() + lambda + len()
In this, we perform tasks of getting positive elements using filter() and lambda, rest all tasks are performed similar to the above methods.
Python3
# Python3 code to demonstrate working of# Positive values percentage# Using filter() + lambda + len()# initializing listtest_list = [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]# printing original listprint("The original list is : " + str(test_list))# getting filtered list using filter(), lambda and# division to get fractionres = (len(list(filter(lambda ele: ele > 0, test_list))) / len(test_list)) * 100# printing resultprint("Positive elements percentage : " + str(res)) |
Output:
The original list is : [4, 6, -2, 3, -8, 0, -1, 8, 9, 1] Positive elements percentage : 60.0
Time Complexity: O(n*n) where n is the number of elements in the list “test_list”. filter() + lambda + len() performs n*n number of operations.
Auxiliary Space: O(n), extra space is required where n is the number of elements in the list
Method #3 : Using list(),map(),find()
Initially convert each list element to string and check for – sign in string, if there is no – sign and string is not ‘0’ then elements are positive.Add all those elements to output list
Python3
# Python3 code to demonstrate working of# Positive values percentage# initializing listtest_list = [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]# printing original listprint("The original list is : " + str(test_list))res=[]x=list(map(str,test_list))for i in x: if(i.find("-")==-1 and i!="0"): res.append(int(i)) re=(len(res)*100)/len(test_list)# printing resultprint("Positive elements percentage : " + str(re)) |
The original list is : [4, 6, -2, 3, -8, 0, -1, 8, 9, 1] Positive elements percentage : 60.0
Method #4: Using for loop
Python3
test_list = [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]positive_count = 0for ele in test_list: if ele > 0: positive_count += 1res = (positive_count / len(test_list)) * 100print("Positive elements percentage : " + str(res))#This code is contributed by Vinay pinjala. |
Positive elements percentage : 60.0
Time complexity:O(n)
Space complexity: O(1)
Method #5: Using numpy and logical indexing:
Algorithm:
1.Convert the given list to a numpy array.
2.Count the number of positive elements in the numpy array using the expression ‘len(test_array[test_array > 0])’.
3.Calculate the percentage of positive elements in the original list using the formula ‘(positive_count / len(test_list)) * 100’.
4.Print the final result.
Python3
import numpy as nptest_list = [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]# printing original listprint("The original list is : " + str(test_list))test_array = np.array(test_list)positive_count = len(test_array[test_array > 0])res = (positive_count / len(test_list)) * 100print("Positive elements percentage : " + str(res))#This code is contributed by Jyothi pinjala. |
Output:
The original list is : [4, 6, -2, 3, -8, 0, -1, 8, 9, 1]
Positive elements percentage : 60.0
Time complexity: O(n)
Converting the list to a numpy array takes O(n) time.
Counting the number of positive elements using numpy array operations takes O(n) time.
The remaining operations take constant time.
Therefore, the overall time complexity is O(n).
Auxiliary Space: O(n)
Converting the list to a numpy array requires O(n) space.
The remaining space usage is constant.
Therefore, the overall space complexity is O(n).
