Python Basics — Lambda & Functional Programming

Lambda & Functional Programming

Python supports a functional programming style through lambda expressions and built-in functions like map(), filter(), and reduce(). These tools let you write concise, expressive data transformations.

What You’ll Learn

#	Topic	What You’ll Master
1	Lambda Expressions	Anonymous single-expression functions
2	map() Function	Transform every item in an iterable
3	filter() Function	Select items that match a condition
4	reduce() Function	Accumulate items into a single result
5	sorted() with Key	Custom sorting with key functions
6	Practice Exercises	Test your understanding

Quick Preview

# Traditional approach
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Get squares of even numbers
result = []
for n in numbers:
    if n % 2 == 0:
        result.append(n ** 2)
print(result)  # [4, 16, 36, 64, 100]

# Functional approach
result = list(map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, numbers)))
print(result)  # [4, 16, 36, 64, 100]

# Pythonic approach (list comprehension)
result = [n ** 2 for n in numbers if n % 2 == 0]
print(result)  # [4, 16, 36, 64, 100]

When to Use Functional vs Imperative

Approach	Best For	Example
Lambda + map/filter	Quick inline transformations	map(str.upper, words)
List comprehension	Most Python code (Pythonic)	[x**2 for x in nums]
reduce()	Accumulating into a single value	Sum, product, concatenation
sorted() with key	Custom sort orders	sorted(students, key=lambda s: s["gpa"])
Regular functions	Complex logic, reusability	Multi-step processing

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Start Learning: Lambda Expressions →