Lecture 7 – Lambda, Map, Filter, Reduce (Functional Programming in Python)

Learn Python functional programming in Lecture 7. Understand lambda expressions, map(), filter(), and reduce() with easy examples, real Data Science use-cases, MCQs, and practice tasks for beginners.

What You Will Learn

By the end of this lecture, you will understand:

What lambda (anonymous functions) are
How to write lambda expressions
When to use lambda vs normal functions
How map() transforms data
How filter() extracts useful data
How reduce() combines data
Functional programming patterns
Real Data Science examples
MCQs + practice problems

1) What Is a Lambda Function?

A lambda is a small, anonymous function without a name.

Normal Function:

def square(x):
    return x * x

Lambda version:

square = lambda x: x * x

Both give the same result.

2) Basic Lambda Examples

Example 1 – Square a number

square = lambda x: x * x
print(square(6))

Example 2 – Add two numbers

add = lambda a, b: a + b
print(add(5, 3))

Example 3 – Even/odd check

is_even = lambda n: n % 2 == 0
print(is_even(10))

Example 4 – Get last character of a string

last_char = lambda text: text[-1]
print(last_char("Python"))

3) Lambda in Sorting

Example – Sort list of tuples by second value

data = [(1, 4), (3, 1), (2, 5)]
sorted_data = sorted(data, key=lambda x: x[1])
print(sorted_data)

Python Documentation

4) map() Function – Transform Each Item in a Collection

Syntax:

map(function, iterable)

Example 1 – Square all numbers

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

Example 2 – Convert string numbers to integers

raw = ["10", "20", "30"]
clean = list(map(lambda x: int(x), raw))
print(clean)

Example 3 – Convert Celsius → Fahrenheit

temps = [0, 10, 25, 30]
fahrenheit = list(map(lambda c: (c * 9/5) + 32, temps))
print(fahrenheit)

5) filter() Function – Keep Only True Items

Syntax:

filter(function, iterable)

Example 1 – Keep even numbers

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

Example 2 – Extract names starting with “A”

names = ["Ali", "Sara", "Ayesha", "Hamza"]
a_names = list(filter(lambda x: x.startswith("A"), names))
print(a_names)

Example 3 – Keep positive values

values = [-5, 0, 10, -2, 15]
positive = list(filter(lambda x: x > 0, values))
print(positive)

Lecture 6 – Functions

6) reduce() Function – Combine All Items into One

(Must import from functools)

Syntax:

from functools import reduce
reduce(function, iterable)

Example 1 – Sum all numbers

from functools import reduce

nums = [1, 2, 3, 4]
total = reduce(lambda a, b: a + b, nums)
print(total)

Example 2 – Multiply all numbers

product = reduce(lambda a, b: a * b, nums)
print(product)

Example 3 – Longest word in list

words = ["AI", "Python", "ElecturesAI"]
longest = reduce(lambda a, b: a if len(a) > len(b) else b, words)
print(longest)

7) Real Data Science Examples

Example 1 – Clean missing values

data = ["12", "15", "N/A", "20"]
clean = list(filter(lambda x: x != "N/A", data))
clean = list(map(lambda x: int(x), clean))
print(clean)

Example 2 – Normalize dataset values

nums = [10, 20, 30, 40]

normalized = list(map(lambda x: x / max(nums), nums))
print(normalized)

Example 3 – Remove outliers

values = [10, 12, 13, 500, 11, 14]

filtered = list(filter(lambda x: x < 100, values))
print(filtered)

Example 4 – Sum of squared values

from functools import reduce

nums = [2, 3, 4]

result = reduce(lambda a, b: a + b, map(lambda x: x*x, nums))
print(result)

Example 5 – Convert names to uppercase

names = ["talha", "ali", "sara"]
upper = list(map(lambda x: x.upper(), names))
print(upper)

Lambda functions are:

a) Named functions
b) Anonymous functions
c) Loops
d) Classes

Answer: b) Anonymous functions

map() is used to:

a) Filter items
b) Loop manually
c) Transform each item
d) Delete items

Answer: c) Transform each item

Which module contains reduce()?

a) math
b) numpy
c) functools
d) itertools

Answer: c) functools

What will this return?

list(filter(lambda x: x > 3, [1,2,3,4,5]))

Answer: [4, 5]

Next Lecture 8 – Modules & Packages

nums = [1, 2, 3, 4]
cubes = list(map(lambda x: x**3, nums))
print(cubes)

words = ["python", "ai", "data", "science"]
result = list(filter(lambda x: len(x) > 5, words))
print(result)

from functools import reduce

nums = [10, 55, 42, 99, 3]
max_val = reduce(lambda a, b: a if a > b else b, nums)
print(max_val)

check = lambda x: x % 3 == 0
print(check(9))

nums = [2, 3, 4, 5]

result = list(
    filter(lambda x: x > 20,
           map(lambda x: x*x, nums))
)

print(result)