Due to the upcoming of Machine Learning, focus has now moved on handling the certain values than ever before, the reason behind this is that it is the essential step of data preprocessing before it is fed into further techniques to perform. Hence removal of certain values in essential and knowledge of it is a must. Lets discuss certain ways in which removal of odd values is achieved.
Method #1 : Naive Method In naive method, we iterate through whole list and append all the filtered, non odd values into a new list, hence ready to be performed with subsequent operations.
Python3
# Python3 code to demonstrate # Odd elements removal in List# using naive method # initializing listtest_list = [1, 9, 4, 7, 6, 5, 8, 3]# printing original list print ("The original list is : " + str(test_list))# using naive method # Odd elements removal in Listres = []for val in test_list: if not (val % 2 != 0) : res.append(val)# printing resultprint ("List after removal of Odd values : " + str(res)) |
The original list is : [1, 9, 4, 7, 6, 5, 8, 3] List after removal of Odd values : [4, 6, 8]
Time complexity: O(n), where n is the length of the test_list.
Auxiliary Space: O(n), extra space of size n is required
Method #2 : Using list comprehension The longer task of using the naive method and increasing line of codes can be done in compact way using this method. We just check for non odd values and construct the new filtered list.
Python3
# Python3 code to demonstrate # Odd elements removal in List# using list comprehension# initializing listtest_list = [1, 9, 4, 7, 6, 5, 8, 3]# printing original list print ("The original list is : " + str(test_list))# using list comprehension# Odd elements removal in Listres = [i for i in test_list if not (i % 2 != 0)]# printing resultprint ("List after removal of odd values : " + str(res)) |
The original list is : [1, 9, 4, 7, 6, 5, 8, 3] List after removal of Odd values : [4, 6, 8]
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#3: Using Recursive Method
Python3
# Python3 code to demonstrate # Odd elements removal in List#Using Recursive Methoddef remove_odd(lst,newlst=[],start=0): if start==len(lst): #base condition return newlst if lst[start]%2==0: #checking if no is even or not newlst.append(lst[start]) return remove_odd(lst,newlst,start+1)# initializing listtest_list = [1, 9, 4, 7, 6, 5, 8, 3]# printing original list print ('The original list is : ' + str(test_list))#Using Recursive Method# Odd elements removal in Listres = remove_odd(test_list)# printing resultprint ('List after removal of odd values : ' + str(res))#this code contributed by tvsk |
The original list is : [1, 9, 4, 7, 6, 5, 8, 3] List after removal of odd values : [4, 6, 8]
Time Complexity: O(n)
Auxiliary Space: O(n)
Method#4: Using filter():
Python3
test_list = [1, 9, 4, 7, 6, 5, 8, 3]result = list(filter(lambda x: x % 2 == 0, test_list))# printing original listprint ('The original list is : ' + str(test_list))print("List after removal of odd values: ", result)#This code is contributed by Jyothi pinjala. |
The original list is : [1, 9, 4, 7, 6, 5, 8, 3] List after removal of odd values: [4, 6, 8]
Time Complexity: O(n)
Auxiliary Space: O(n)
Method #5: Using numpy
- Define a list of integers called “test_list”.
- Convert the list to a numpy array using the “np.array” function.
- Use boolean indexing to filter the array and keep only the even values: create a boolean mask by
- applying the modulo operator (%) to the array and checking if each element is equal to zero. Use this mask to select only the elements of the array that are even.
- Convert the resulting numpy array back to a list using the “tolist” method.
- Print the new list.
Python3
import numpy as nptest_list = [1, 9, 4, 7, 6, 5, 8, 3]# printing original listprint ('The original list is : ' + str(test_list))arr = np.array(test_list)res = arr[arr % 2 == 0].tolist()print("List after removal of odd values: " + str(res))#This code is contributed by Vinay pinjala. |
Output
The original list is : [1, 9, 4, 7, 6, 5, 8, 3] List after removal of odd values: [4, 6, 8]
The time complexity of this algorithm is O(n), where n is the length of the input list. This is because the algorithm needs to iterate over every element of the input list once in order to create the numpy array, filter the array using boolean indexing, and convert the resulting array back to a list.
The auxiliary space complexity of this algorithm is O(n), where n is the number of even values in the input list. This is because the algorithm creates a new numpy array containing only the even values, and the size of this array is proportional to the number of even values in the input list.
