Artificial Intelligence

🚀 Welcome back to our AI Engineer Roadmap! ❤️ In the previous posts, we learned about functions and solved some tricky function-based MCQs. Now let's move to the next topic in Python fundamentals. 📖 Phase 1: Programming Fundamentals 📌 Topic 12: Lambda Functions A Lambda Function is a small, anonymous function that can be written in a single line. Unlike regular functions created using def, lambda functions are created using the lambda keyword. Why Do We Need Lambda Functions? Lambda functions are useful when: • You need a small function for a short task • You don't want to define a full function using def • You need a function temporarily • You're working with functions like map(), filter(), and sorted() 1. Creating a Lambda Function A normal function:

def square(x):
    return x * x

The same function using lambda:

square = lambda x: x * x
print(square(5))

Output: 25 Lambda Syntax

lambda arguments: expression

For example: lambda x: x + 10 • lambda → Keyword used to create the function • x → Argument • x + 10 → Expression that is returned 2. Lambda with Multiple Arguments A lambda function can accept multiple arguments.

add = lambda a, b: a + b
print(add(10, 20))

Output: 30

multiply = lambda x, y: x * y
print(multiply(5, 4))

Output: 20 3. Lambda with if-else Lambda functions can also contain conditional expressions.

check = lambda x: "Even" if x % 2 == 0 else "Odd"
print(check(10))
print(check(7))

Output:

Even
Odd

4. Lambda with map() map() applies a function to every item in an iterable.

numbers = [1, 2, 3, 4, 5]
squares = list(map(lambda x: x * x, numbers))
print(squares)

Output: [1, 4, 9, 16, 25] 5. Lambda with filter() filter() selects elements based on a condition.

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)

Output: [2, 4, 6] 6. Lambda with sorted() Lambda functions are very useful when sorting complex data. Example:

students = [
    ("Rahul", 80),
    ("Priya", 95),
    ("Amit", 70)
]

students.sort(key=lambda x: x[1])
print(students)

Output: [('Amit', 70), ('Rahul', 80), ('Priya', 95)] Here, lambda x: x[1] tells Python to sort using the second element of each tuple. Lambda vs Regular Function • Regular function:

def square(x):
    return x * x

• Lambda function:

square = lambda x: x * x

Both produce the same result. When Should You Use Lambda? Use lambda when: ✅ The function is very small ✅ The operation is simple ✅ You need the function temporarily ✅ You're working with map(), filter(), or sorted() Avoid lambda when: ❌ The logic becomes complicated ❌ The function needs multiple statements ❌ A meaningful function name and documentation would improve readability In those situations, a regular def function is usually better. Real-World AI/Data Example Lambda functions are commonly used while preprocessing data.

scores = [45, 67, 82, 91, 38]
updated_scores = list(map(lambda x: x / 100, scores))
print(updated_scores)

Output: [0.45, 0.67, 0.82, 0.91, 0.38] This kind of transformation can be useful when preparing data before feeding it into a Machine Learning model. Common Beginner Mistakes ❌ Trying to put complex logic into a lambda ❌ Forgetting that a lambda automatically returns its expression ❌ Confusing map() and filter() Key Takeaways • Lambda functions are small anonymous functions • They are created using the lambda keyword • They can accept multiple arguments • They return the result of a single expression • They're especially useful with map(), filter(), and sorted() • For complex logic, prefer a regular def function ➡️ Double Tap ❤️ For More