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4. Functional Programming Tools

4.1 Lambda Functions – Advanced Uses

Lambda functions are anonymous (nameless) one-line functions — ideal for short operations passed to higher-order functions.

Basic syntax lambda arguments: expression

Advanced & practical uses

1. Sorting with custom key

Python

students = [ {"name": "Anshuman", "marks": 92}, {"name": "Rahul", "marks": 85}, {"name": "Priya", "marks": 98} ] # Sort by marks descending sorted_students = sorted(students, key=lambda x: x["marks"], reverse=True) print([s["name"] for s in sorted_students]) # ['Priya', 'Anshuman', 'Rahul']

2. Immediate function call (IIFE-like)

Python

result = (lambda x, y: x * y + 10)(5, 3) print(result) # 25

3. With map/filter in one-liners

Python

numbers = [1, 2, 3, 4, 5] squared_even = list(map(lambda x: x**2, filter(lambda x: x % 2 == 0, numbers))) print(squared_even) # [4, 16]

4. As default value in dict (strategy pattern)

Python

operations = { "+": lambda x, y: x + y, "-": lambda x, y: x - y, "*": lambda x, y: x y } print(operations[""](10, 4)) # 40

Tip: Lambdas are great for short callbacks — but for complex logic (> 1–2 lines), use def for readability.

4.2 map(), filter(), reduce() (functools)

Higher-order functions that apply functions to iterables.

map(function, iterable, ...) → applies function to each item

Python

numbers = [1, 2, 3, 4] squares = map(lambda x: x**2, numbers) print(list(squares)) # [1, 4, 9, 16] # Multiple iterables names = ["anshuman", "rahul"] cities = ["Muzaffarpur", "Patna"] result = map(lambda n, c: f"{n.title()} from {c}", names, cities) print(list(result)) # ['Anshuman from Muzaffarpur', 'Rahul from Patna']

filter(function, iterable) → keeps items where function returns True

Python

numbers = [10, 15, 20, 25, 30] evens = filter(lambda x: x % 2 == 0, numbers) print(list(evens)) # [10, 20, 30]

reduce(function, iterable, initial) → from functools — reduces iterable to single value

Python

from functools import reduce numbers = [1, 2, 3, 4, 5] product = reduce(lambda x, y: x * y, numbers) print(product) # 120 # With initial value total_with_bonus = reduce(lambda acc, x: acc + x, numbers, 100) print(total_with_bonus) # 115

Modern note (2026): map & filter are still useful, but list comprehensions + reduce are more common. reduce is powerful for accumulations.

4.3 List, Dict & Set Comprehensions – Advanced Patterns

Comprehensions are Python’s most “functional” and readable way to create collections.

Advanced List Comprehension

Python

# Nested + condition matrix = [[1, 2], [3, 4], [5, 6]] flattened = [num for row in matrix for num in row if num % 2 == 0] print(flattened) # [2, 4, 6]

Dict Comprehension

Python

names = ["Anshuman", "Rahul", "Priya"] scores = [92, 85, 98] result = {name: score for name, score in zip(names, scores) if score >= 90} print(result) # {'Anshuman': 92, 'Priya': 98}

Set Comprehension

Python

words = ["apple", "banana", "cherry", "date"] unique_lengths = {len(word) for word in words} print(unique_lengths) # {5, 6, 4}

Conditional expression inside

Python

numbers = range(10) parity = ["even" if n % 2 == 0 else "odd" for n in numbers] print(parity[:5]) # ['even', 'odd', 'even', 'odd', 'even']

4.4 Generator Expressions vs List Comprehensions

List comprehension → creates full list in memory Generator expression → lazy, memory-efficient (yields one item at a time)

Python

# List comp → full list now squares_list = [x**2 for x in range(1000000)] # uses lots of memory # Generator expression → yields on demand squares_gen = (x**2 for x in range(1000000)) # almost no memory print(next(squares_gen)) # 0 print(next(squares_gen)) # 1

When to use generator expressions:

• Large datasets

• One-time iteration

• Passing to functions like sum(), max(), list(), tuple()

Python

total = sum(x**2 for x in range(1000000)) # efficient! print(total)

4.5 Generators & yield – Memory Efficient Iteration

Generators are functions that use yield instead of return — they pause and resume execution.

Python

def countdown(n): while n > 0: yield n n -= 1 for num in countdown(5): print(num) # 5 4 3 2 1

Generator function vs normal function

Python

def normal(): return [1, 2, 3] # computes everything, returns list def gen(): yield 1 yield 2 yield 3 # pauses after each yield

4.6 Generator Functions, Generator Expressions

Generator expression (tuple-like syntax)

Python

gen_exp = (x**2 for x in range(10) if x % 2 == 0) print(list(gen_exp)) # [0, 4, 16, 36, 64]

Infinite generator example

Python

def infinite_fibonacci(): a, b = 0, 1 while True: yield a a, b = b, a + b fib = infinite_fibonacci() print(next(fib)) # 0 print(next(fib)) # 1 print(next(fib)) # 1 print(next(fib)) # 2

4.7 yield from & Sub-generators

yield from delegates iteration to another iterable/generator — cleaner for chaining.

Python

def chain_generators(): yield from range(3) # 0,1,2 yield from "abc" # a,b,c yield from countdown(3) # 3,2,1 for val in chain_generators(): print(val) # 0 1 2 a b c 3 2 1

Flatten nested lists

Python

def flatten(nested): for sublist in nested: yield from sublist data = [[1,2], [3,4], [5]] print(list(flatten(data))) # [1, 2, 3, 4, 5]

4.8 itertools module – Powerful Iterators

itertools provides fast, memory-efficient iterators.

Common useful functions

1. count, cycle, repeat

Python

from itertools import count, cycle, repeat for num in count(10, 5): # 10, 15, 20, ... print(num) if num > 30: break for color in cycle(["red", "green", "blue"]): # repeats forever print(color) # break when needed

2. chain, zip_longest

Python

from itertools import chain, zip_longest print(list(chain([1,2], "abc", range(3)))) # [1, 2, 'a', 'b', 'c', 0, 1, 2] a = [1, 2] b = [10, 20, 30] print(list(zip_longest(a, b, fillvalue=0))) # [(1, 10), (2, 20), (0, 30)]

3. groupby (needs sorted input)

Python

from itertools import groupby from operator import itemgetter data = [("apple", 5), ("banana", 3), ("apple", 2), ("cherry", 8)] sorted_data = sorted(data, key=itemgetter(0)) for fruit, group in groupby(sorted_data, key=itemgetter(0)): print(fruit, list(group)) # apple [('apple', 5), ('apple', 2)] # banana [('banana', 3)] # cherry [('cherry', 8)]

Mini Project – Infinite Prime Generator with itertools

Python

from itertools import count, islice def is_prime(n): if n < 2: return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True primes = (n for n in count(2) if is_prime(n)) first_10_primes = list(islice(primes, 10)) print(first_10_primes) # [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]

This completes the full Functional Programming Tools section — now you can write concise, powerful, memory-efficient Python code!

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