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Python | Find missing and additional values in two lists

Last Updated : 13 Apr, 2023
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Given two lists, find the missing and additional values in both the lists. Examples:

Input : list1 = [1, 2, 3, 4, 5, 6] 
        list2 = [4, 5, 6, 7, 8] 
Output : Missing values in list1 = [8, 7] 
         Additional values in list1 = [1, 2, 3] 
         Missing values in list2 = [1, 2, 3] 
         Additional values in list2 = [7, 8] 

Explanation: 
 

Approach: To find the missing elements of list2 we need to get the difference of list1 from list2. To find the additional elements of list2, calculate the difference of list2 from list1. Similarly while finding missing elements of list1, calculate the difference of list2 from list1. To find the additional elements in list1, calculate the difference of list1 from list2. Insert the list1 and list2 to set and then use difference function in sets to get the required answer. Prerequisite : Python Set Difference 

Python3




# Python program to find the missing
# and additional elements
 
# examples of lists
list1 = [1, 2, 3, 4, 5, 6]
list2 = [4, 5, 6, 7, 8]
 
# prints the missing and additional elements in list2
print("Missing values in second list:", (set(list1).difference(list2)))
print("Additional values in second list:", (set(list2).difference(list1)))
 
# prints the missing and additional elements in list1
print("Missing values in first list:", (set(list2).difference(list1)))
print("Additional values in first list:", (set(list1).difference(list2)))
 






                        










Output:


Missing values in second list: {1, 2, 3}
Additional values in second list: {7, 8}
Missing values in first list: {7, 8}
Additional values in first list: {1, 2, 3}


Approach using numpy:


note: install numpy module using command “pip install numpy”


Algorithm:


Convert list1 and list2 to numpy arrays using numpy.array() function.
Calculate the missing values in list2 using numpy.setdiff1d() with arguments arr1 and arr2.
Calculate the additional values in list2 using numpy.setdiff1d() with arguments arr2 and arr1.
Calculate the missing values in list1 using numpy.setdiff1d() with arguments arr2 and arr1.
Calculate the additional values in list1 using numpy.setdiff1d() with arguments arr1 and arr2.
Convert the numpy arrays to lists using list() function and return the results.
 




Python3














                                    




                                    

                                    




                                    




                                    

                                    

                                    
                                




















import numpy as np


 


 


def find_missing_additional(list1, list2):


    # Convert list1 and list2 to numpy arrays


    arr1 = np.array(list1)


    arr2 = np.array(list2)


 


    # Calculate missing and additional values in list2


    missing_list2 = np.setdiff1d(arr1, arr2)


    additional_list2 = np.setdiff1d(arr2, arr1)


 


    # Calculate missing and additional values in list1


    missing_list1 = np.setdiff1d(arr2, arr1)


    additional_list1 = np.setdiff1d(arr1, arr2)


 


    # Convert numpy arrays to lists and return the results


    return list(missing_list2), list(additional_list2), list(missing_list1), list(additional_list1)


list1 = [1, 2, 3, 4, 5, 6]


list2 = [4, 5, 6, 7, 8]


 


missing_list2, additional_list2, missing_list1, additional_list1 = find_missing_additional(list1, list2)


print("Missing values in first list:", missing_list1)


print("Additional values in first list:", additional_list1)


print("Missing values in second list:", missing_list2)


print("Additional values in second list:", additional_list2)



















                        







                        










Output:


Missing values in first list: [7, 8]
Additional values in first list: [1, 2, 3]
Missing values in second list: [1, 2, 3]
Additional values in second list: [7, 8]


Time complexity:
The time complexity of the numpy.setdiff1d() function is O(nlogn) since it involves sorting the arrays. Therefore, the time complexity of this algorithm is O(nlogn) where n is the length of the input lists.


Auxiliary Space:
The space complexity of this algorithm is O(n) where n is the length of the input lists, as we are creating numpy arrays of length n.




                                    


                                                                        

                                        

                                            

                                                

                                                    
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