Python – Find missing and additional values in two lists

Last Updated : 17 Dec, 2024

It’s common to compare two lists and determine which values are missing or additional in each. For example, we might have a list of expected items and another list showing what we have in stock. We can easily identify missing and additional items by comparing the two lists. List comprehension is a compact and easy way to find missing and additional values. This method works best for smaller lists.

Python

a = [1, 2, 3, 4, 5, 6]
b = [4, 5, 6, 7, 8]

# Missing values in a (values in b not in a)
missing_in_a = [x for x in b if x not in a]

# Additional values in a (values in a not in b)
additional_in_a = [x for x in a if x not in b]

# Missing values in b (values in a not in b)
missing_in_b = [x for x in a if x not in b]

# Additional values in b (values in b not in a)
additional_in_b = [x for x in b if x not in a]

print(missing_in_a)
print(additional_in_a)
print(missing_in_b)
print(additional_in_b)

Output

[7, 8]
[1, 2, 3]
[1, 2, 3]
[7, 8]

Other methods to find missing and additional values in two lists:

Table of Content

Using Sets
Using NumPy
Using Loops

Using Sets (Efficient for Larger Lists)

Sets in Python allow us to perform efficient set operations like difference and union. This method is faster than list comprehension when dealing with larger lists.

Python

a = [1, 2, 3, 4, 5, 6]
b = [4, 5, 6, 7, 8]

# Convert lists to sets
set_a = set(a)
set_b = set(b)

# Missing values in a (elements in b not in a)
missing_in_a = list(set_b - set_a)

# Additional values in a (elements in a not in b)
additional_in_a = list(set_a - set_b)

# Missing values in b (elements in a not in b)
missing_in_b = list(set_a - set_b)

# Additional values in b (elements in b not in a)
additional_in_b = list(set_b - set_a)

print(missing_in_a)
print(additional_in_a)
print(missing_in_b)
print(additional_in_b)

Output

[8, 7]
[1, 2, 3]
[1, 2, 3]
[8, 7]

Using NumPy (Efficient for Large Datasets)

For numerical data, NumPy is a powerful library that can make comparisons very fast. NumPy’s array operations are much faster than Python lists, especially when the data is large.

Python

import numpy as np

# List 1 and List 2
a = np.array([1, 2, 3, 4, 5, 6])
b = np.array([4, 5, 6, 7, 8])

# Missing values in a (values in b not in a)
missing_in_a = np.setdiff1d(b, a)

# Additional values in a (values in a not in b)
additional_in_a = np.setdiff1d(a, b)

# Missing values in b (values in a not in b)
missing_in_b = np.setdiff1d(a, b)

# Additional values in b (values in b not in a)
additional_in_b = np.setdiff1d(b, a)

print(missing_in_a)
print(additional_in_a)
print(missing_in_b)
print(additional_in_b)

Output

[7 8]
[1 2 3]
[1 2 3]
[7 8]

Using Loops

If we want to manually compare the two lists, we can use loops. This method is slower for large lists but helps understand the process clearly.

Python

a = [1, 2, 3, 4, 5, 6]
b = [4, 5, 6, 7, 8]

# Empty lists to store the missing and additional values
missing_in_a = []
additional_in_a = []
missing_in_b = []
additional_in_b = []

# Check for missing and additional values in both lists
for x in a:
    if x not in b:
        missing_in_b.append(x)

for x in b:
    if x not in a:
        missing_in_a.append(x)

print(missing_in_a)
print(additional_in_a)
print(missing_in_b)
print(additional_in_b)

Output

[7, 8]
[]
[1, 2, 3]
[]

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Python – Find missing and additional values in two lists

Other methods to find missing and additional values in two lists:

Using Sets (Efficient for Larger Lists)

Using NumPy (Efficient for Large Datasets)

Using Loops

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