Functional Programming in Python

Functional Programming in Python

Explore functional programming paradigms in Python using map, filter, reduce, lambda functions, closures, and decorators. Apply these techniques to process election results and other data functionally.

Lambda Functions: Anonymous Functions

Lambda functions are small anonymous functions defined with the lambda keyword.

# Lambda syntax: lambda arguments: expression

# Simple lambda
square = lambda x: x ** 2
print(square(5))  # 25

# Lambda with multiple arguments
add = lambda x, y: x + y
print(add(10, 20))  # 30

# Lambda with default arguments
greet = lambda name, greeting='Hello': f'{greeting}, {name}'
print(greet('Aditya'))  # Hello, Aditya
print(greet('Priya', 'Hi'))  # Hi, Priya

# Lambda with conditional expression
absolute = lambda x: x if x >= 0 else -x
print(absolute(-10))  # 10

# Lambda with multiple statements (not recommended, use def instead)
# Instead, use conditional expressions or nested lambdas
max_value = lambda x, y: x if x > y else y
print(max_value(50, 30))  # 50 

Map Function: Transform Data

Map applies a function to every item in an iterable and returns an iterator.

# Convert list of strings to integers
numbers_str = ['10', '20', '30', '40', '50']
numbers = list(map(int, numbers_str))
print(numbers)  # [10, 20, 30, 40, 50]

# Apply square function using map
squares = list(map(lambda x: x**2, range(1, 6)))
print(squares)  # [1, 4, 9, 16, 25]

# Convert temperature from Celsius to Fahrenheit
celsius = [0, 10, 20, 30, 40]
fahrenheit = list(map(lambda c: (c * 9/5) + 32, celsius))
print(fahrenheit)  # [32.0, 50.0, 68.0, 86.0, 104.0]

# Map with multiple iterables
list1 = [1, 2, 3]
list2 = [10, 20, 30]
result = list(map(lambda x, y: x + y, list1, list2))
print(result)  # [11, 22, 33]

# Real function with map
def discount_price(price): '''Apply 20% discount''' return price * 0.8

original_prices = [100, 500, 1000, 2500]
discounted = list(map(discount_price, original_prices))
print(discounted)  # [80.0, 400.0, 800.0, 2000.0] 

Filter Function: Select Data

Filter selects items from an iterable based on a predicate function.

# Filter even numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
evens = list(filter(lambda x: x % 2 == 0, numbers))
print(evens)  # [2, 4, 6, 8, 10]

# Filter students with marks >= 80
marks = [92, 78, 85, 88, 70, 95, 72, 89]
passed = list(filter(lambda m: m >= 80, marks))
print(passed)  # [92, 85, 88, 95, 89]

# Filter words with more than 5 characters
words = ['apple', 'banana', 'cat', 'dragon', 'elephant']
long_words = list(filter(lambda w: len(w) > 5, words))
print(long_words)  # ['banana', 'dragon', 'elephant']

# Filter None values
data = [1, None, 3, None, 5, 0, 7]
cleaned = list(filter(lambda x: x is not None, data))
print(cleaned)  # [1, 3, 5, 0, 7]

# Real function with filter
def is_prime(n): '''Check if number is prime''' if n < 2: return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True

primes = list(filter(is_prime, range(1, 30)))
print(primes)  # [2, 3, 5, 7, 11, 13, 17, 19, 23, 29] 

Reduce Function: Aggregate Data

Reduce applies a function cumulatively to items in an iterable to produce a single value.

from functools import reduce

# Sum all numbers
numbers = [1, 2, 3, 4, 5]
total = reduce(lambda x, y: x + y, numbers)
print(total)  # 15

# Product of all numbers
product = reduce(lambda x, y: x * y, numbers)
print(product)  # 120

# Find maximum value
max_value = reduce(lambda x, y: x if x > y else y, numbers)
print(max_value)  # 5

# Concatenate strings
words = ['Python', 'is', 'powerful']
sentence = reduce(lambda x, y: x + ' ' + y, words)
print(sentence)  # Python is powerful

# With initial value
numbers = [1, 2, 3, 4, 5]
total = reduce(lambda x, y: x + y, numbers, 100)
print(total)  # 115 (100 + 1 + 2 + 3 + 4 + 5)

# Real-world: Calculate total votes
from functools import reduce

votes = { 'party_a': 50000, 'party_b': 45000, 'party_c': 35000
}

total_votes = reduce(lambda sum, v: sum + v, votes.values(), 0)
print(total_votes)  # 130000 

Processing Election Results Functionally

Analyze election data using functional programming techniques.

from functools import reduce

# Election data
election_results = [ {'candidate': 'Aditya Singh', 'party': 'Party A', 'votes': 45000}, {'candidate': 'Priya Sharma', 'party': 'Party B', 'votes': 42000}, {'candidate': 'Rajesh Kumar', 'party': 'Party C', 'votes': 38000}, {'candidate': 'Deepika Gupta', 'party': 'Party A', 'votes': 40000}, {'candidate': 'Eshan Patel', 'party': 'Party B', 'votes': 43000},
]

# Get candidates who got > 40000 votes
top_candidates = list(filter(lambda x: x['votes'] > 40000, election_results))
print('Top candidates:', [c['candidate'] for c in top_candidates])

# Get vote counts for each candidate
vote_counts = list(map(lambda x: x['votes'], election_results))
print('Vote counts:', vote_counts)

# Calculate total votes
total_votes = reduce(lambda sum, x: sum + x['votes'], election_results, 0)
print('Total votes:', total_votes)

# Get winner
winner = reduce(lambda a, b: a if a['votes'] > b['votes'] else b, election_results)
print(f"Winner: {winner['candidate']} with {winner['votes']} votes")

# Group votes by party (using map and reduce)
def get_party_votes(party): party_votes = list(filter(lambda x: x['party'] == party, election_results)) total = reduce(lambda sum, x: sum + x['votes'], party_votes, 0) return total

parties = set(map(lambda x: x['party'], election_results))
party_results = {party: get_party_votes(party) for party in parties}
print('Party results:', party_results)

# Calculate percentage for each party
percentages = {party: (votes/total_votes)*100 for party, votes in party_results.items()}
print('Percentages:', {p: f'{v:.1f}%' for p, v in percentages.items()}) 

Closures: Functions with Memory

Closures allow functions to access variables from their enclosing scope.

# Simple closure
def make_multiplier(n): def multiplier(x): return x * n return multiplier

times3 = make_multiplier(3)
times5 = make_multiplier(5)

print(times3(10))  # 30
print(times5(10))  # 50

# Practical closure: Counter
def make_counter(): count = 0 def increment(): nonlocal count count += 1 return count return increment

counter = make_counter()
print(counter())  # 1
print(counter())  # 2
print(counter())  # 3

# Closure with multiple variables
def make_bank_account(initial_balance): balance = initial_balance def deposit(amount): nonlocal balance balance += amount return balance def withdraw(amount): nonlocal balance if amount > balance: return 'Insufficient funds' balance -= amount return balance return {'deposit': deposit, 'withdraw': withdraw}

account = make_bank_account(10000)
print(account['deposit'](5000)) # 15000
print(account['withdraw'](3000)) # 12000
print(account['withdraw'](20000))  # Insufficient funds 

Introduction to Decorators

Decorators modify function behavior using closures and higher-order functions.

# Simple decorator
def timing_decorator(func): def wrapper(*args, **kwargs): print(f'Calling {func.__name__}...') result = func(*args, **kwargs) print(f'{func.__name__} completed') return result return wrapper

@timing_decorator
def greet(name): return f'Hello, {name}!'

result = greet('Aditya')
# Output:
# Calling greet...
# greet completed

# Decorator with arguments
def repeat_decorator(times): def decorator(func): def wrapper(*args, **kwargs): result = None for _ in range(times): result = func(*args, **kwargs) return result return wrapper return decorator

@repeat_decorator(3)
def say_hello(): print('Hello!') say_hello()
# Output: Hello! Hello! Hello! 

Itertools: Functional Tools

The itertools module provides functional tools for working with iterables efficiently.

from itertools import chain, combinations, permutations, count, repeat

# Chain multiple iterables
list1 = [1, 2, 3]
list2 = [4, 5, 6]
result = list(chain(list1, list2))
print(result)  # [1, 2, 3, 4, 5, 6]

# Combinations (order doesn't matter)
items = ['a', 'b', 'c']
combos = list(combinations(items, 2))
print(combos)  # [('a', 'b'), ('a', 'c'), ('b', 'c')]

# Permutations (order matters)
perms = list(permutations(items, 2))
print(perms)  # [('a', 'b'), ('a', 'c'), ('b', 'a'), ...]

# Count: infinite counter
counter = count(start=10, step=5)
print([next(counter) for _ in range(5)])  # [10, 15, 20, 25, 30]

# Repeat: repeat values
repeated = list(repeat('A', 3))
print(repeated)  # ['A', 'A', 'A'] 

Practice Problems

1. Use map and lambda to convert a list of temperatures from Celsius to Fahrenheit
2. Use filter to find all numbers divisible by 3 from a list
3. Use reduce to find the product of all numbers in a list
4. Create a closure function that calculates compound interest
5. Create a decorator that measures and prints function execution time

Key Takeaways

• Lambda functions provide concise anonymous functions for simple operations
• Map transforms data by applying a function to each element
• Filter selects data based on a predicate function
• Reduce aggregates data into a single value using an accumulator function
• Closures allow functions to maintain state and access enclosing scope variables

From Concept to Reality: Functional Programming in Python

In the professional world, the difference between a good engineer and a great one often comes down to understanding fundamentals deeply. Anyone can copy code from Stack Overflow. But when that code breaks at 2 AM and your application is down — affecting millions of users — only someone who truly understands the underlying concepts can diagnose and fix the problem.

Functional Programming in Python is one of those fundamentals. Whether you end up working at Google, building your own startup, or applying CS to solve problems in agriculture, healthcare, or education, these concepts will be the foundation everything else is built on. Indian engineers are known globally for their strong fundamentals — this is why companies worldwide recruit from IITs, NITs, IIIT Hyderabad, and BITS Pilani. Let us make sure you have that same strong foundation.

Object-Oriented Programming: Modelling the Real World

OOP lets you model real-world entities as code "objects." Each object has properties (data) and methods (behaviour). Here is a practical example:

class BankAccount: """A simple bank account — like what SBI or HDFC uses internally""" def __init__(self, holder_name, initial_balance=0): self.holder = holder_name self.balance = initial_balance # Private in practice self.transactions = [] # History log def deposit(self, amount): if amount <= 0: raise ValueError("Deposit must be positive") self.balance += amount self.transactions.append(f"+₹{amount}") return self.balance def withdraw(self, amount): if amount > self.balance: raise ValueError("Insufficient funds!") self.balance -= amount self.transactions.append(f"-₹{amount}") return self.balance def statement(self): print(f"
--- Account Statement: {self.holder} ---") for t in self.transactions: print(f"  {t}") print(f"  Balance: ₹{self.balance}")

# Usage
acc = BankAccount("Rahul Sharma", 5000)
acc.deposit(15000) # Salary credited
acc.withdraw(2000) # UPI payment to Swiggy
acc.withdraw(500) # Metro card recharge
acc.statement()

This is encapsulation — bundling data and behaviour together. The user of BankAccount does not need to know HOW deposit works internally; they just call it. Inheritance lets you extend this: a SavingsAccount could inherit from BankAccount and add interest calculation. Polymorphism means different account types can respond to the same .withdraw() method differently (savings accounts might check minimum balance, current accounts might allow overdraft).

Real-World System Design: Swiggy's Architecture

When you order food on Swiggy, here is what happens behind the scenes in about 2 seconds: your location is geocoded (algorithms), nearby restaurants are queried from a spatial index (data structures), menu prices are pulled from a database (SQL), delivery time is estimated using ML models trained on historical data (AI), the order is placed in a distributed message queue (Kafka), a delivery partner is assigned using a matching algorithm (optimization), and real-time tracking begins using WebSocket connections (networking). EVERY concept in your CS curriculum is being used simultaneously to deliver your biryani.

How the Web Request Cycle Works

Every time you visit a website, a precise sequence of events occurs. Here is the flow:

 You (Browser) DNS Server Web Server | | | |---[1] bharath.ai --->| | | | | |<--[2] IP: 76.76.21.9| | | | | |---[3] GET /index.html ----------------->  | | | | | | [4] Server finds file, | | runs server code, | | prepares response | | | |<---[5] HTTP 200 OK + HTML + CSS + JS --- | | | | [6] Browser parses HTML | Loads CSS (styling) | Executes JS (interactivity) | Renders final page |

Step 1-2 is DNS resolution — converting a human-readable domain name to a machine-readable IP address. Step 3 is the HTTP request. Step 4 is server-side processing (this is where frameworks like Node.js, Django, or Flask operate). Step 5 is the HTTP response. Step 6 is client-side rendering (this is where React, Angular, or Vue operate).

In a real-world scenario, this cycle also involves CDNs (Content Delivery Networks), load balancers, caching layers, and potentially microservices. Indian companies like Jio use this exact architecture to serve 400+ million subscribers.

Production Engineering: Functional Programming in Python at Scale

Understanding functional programming in python at an academic level is necessary but not sufficient. Let us examine how these concepts manifest in production environments where failure has real consequences.

Consider India's UPI system processing 10+ billion transactions monthly. The architecture must guarantee: atomicity (a transfer either completes fully or not at all — no half-transfers), consistency (balances always add up correctly across all banks), isolation (concurrent transactions on the same account do not interfere), and durability (once confirmed, a transaction survives any failure). These are the ACID properties, and violating any one of them in a payment system would cause financial chaos for millions of people.

At scale, you also face the thundering herd problem: what happens when a million users check their exam results at the same time? (CBSE result day, anyone?) Without rate limiting, connection pooling, caching, and graceful degradation, the system crashes. Good engineering means designing for the worst case while optimising for the common case. Companies like NPCI (the organisation behind UPI) invest heavily in load testing — simulating peak traffic to identify bottlenecks before they affect real users.

Monitoring and observability become critical at scale. You need metrics (how many requests per second? what is the 99th percentile latency?), logs (what happened when something went wrong?), and traces (how did a single request flow through 15 different microservices?). Tools like Prometheus, Grafana, ELK Stack, and Jaeger are standard in Indian tech companies. When Hotstar streams IPL to 50 million concurrent users, their engineering team watches these dashboards in real-time, ready to intervene if any metric goes anomalous.

The career implications are clear: engineers who understand both the theory (from chapters like this one) AND the practice (from building real systems) command the highest salaries and most interesting roles. India's top engineering talent earns ₹50-100+ LPA at companies like Google, Microsoft, and Goldman Sachs, or builds their own startups. The foundation starts here.

Key Vocabulary

Here are important terms from this chapter that you should know:

💡 Interview-Style Problem

Here is a problem that frequently appears in technical interviews at companies like Google, Amazon, and Flipkart: "Design a URL shortener like bit.ly. How would you generate unique short codes? How would you handle millions of redirects per second? What database would you use and why? How would you track click analytics?"

Think about: hash functions for generating short codes, read-heavy workload (99% redirects, 1% creates) suggesting caching, database choice (Redis for cache, PostgreSQL for persistence), and horizontal scaling with consistent hashing. Try sketching the system architecture on paper before looking up solutions. The ability to think through system design problems is the single most valuable skill for senior engineering roles.

Where This Takes You

The knowledge you have gained about functional programming in python is directly applicable to: competitive programming (Codeforces, CodeChef — India has the 2nd largest competitive programming community globally), open-source contribution (India is the 2nd largest contributor on GitHub), placement preparation (these concepts form 60% of technical interview questions), and building real products (every startup needs engineers who understand these fundamentals).

India's tech ecosystem offers incredible opportunities. Freshers at top companies earn ₹15-50 LPA; experienced engineers at FAANG companies in India earn ₹50-1 Cr+. But more importantly, the problems being solved in India — digital payments for 1.4 billion people, healthcare AI for rural areas, agricultural tech for 150 million farmers — are some of the most impactful engineering challenges in the world. The fundamentals you are building will be the tools you use to tackle them.

Crafted for Class 7–9 • Python Mastery • Aligned with NEP 2020 & CBSE Curriculum