08 🎭 Functions and Modules

Functions and modules are essential building blocks in Python, promoting code reuse, modularity, and maintainability. Understanding them is crucial for data science, machine learning, and AI, where reusable code improves efficiency and readability.

This chapter covers defining functions, argument handling, lambda functions, recursion, and working with modules to write efficient, modular, and scalable Python code.

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08.1 🎯 Functions: The Building Blocks of Python

A function is a reusable block of code that performs a specific task.

✅ Defining a Function

def greet(name):
    return f"Hello, {name}!"

print(greet("Alice"))  # Output: Hello, Alice!

✅ Use Case: Encapsulating repetitive code, making programs more readable.

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08.2 🎭 Function Arguments and Parameters

Functions in Python support different types of arguments:

🔹 Positional Arguments

def add(a, b):
    return a + b

print(add(5, 3))  # Output: 8

🔹 Default Arguments

def power(base, exponent=2):
    return base  exponent

print(power(3))     # Output: 9 (3²)
print(power(3, 3))  # Output: 27 (3³)

🔹 Keyword Arguments

print(power(exponent=3, base=2))  # Output: 8

🔹 Variable-Length Arguments (*args, **kwargs)

def sum_all(*numbers):
    return sum(numbers)

print(sum_all(1, 2, 3, 4))  # Output: 10

def display_info(info):
    print(info)

display_info(name="Alice", age=25)

✅ Use Case: Handling dynamic data inputs in ML models, APIs, and automation scripts.

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08.3 ⚡ Lambda (Anonymous) Functions

Lambda functions are short, one-line functions often used in data processing.

✅ Lambda Syntax

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

🔹 Lambda with map(), filter(), reduce()

numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x2, numbers))
print(squared)  # Output: [1, 4, 9, 16, 25]

✅ Use Case: Data transformations in pandas, NumPy, and machine learning preprocessing.

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08.4 🔁 Recursion: Functions Calling Themselves

Recursion is used when a problem can be broken down into smaller subproblems.

✅ Factorial Calculation

def factorial(n):
    if n == 1:
        return 1
    return n * factorial(n - 1)

print(factorial(5))  # Output: 120

✅ Use Case: Tree-based algorithms, graph traversal (DFS), Fibonacci sequence.

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08.5 📦 Python Modules (Importing and Organizing Code)

Modules allow code organization by grouping related functions and variables.

✅ Importing a Module

import math
print(math.sqrt(25))  # Output: 5.0

🔹 Importing Specific Functions

from math import sqrt
print(sqrt(16))  # Output: 4.0

🔹 Creating a Custom Module

📌 Create a file mymodule.py

def greet(name):
    return f"Hello, {name}!"

📌 Import and Use the Module

import mymodule
print(mymodule.greet("Alice"))

✅ Use Case: Reusing functions in large AI projects, ML models, and APIs.

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08.6 📂 Working with Built-in and Third-Party Modules

🔹 Useful Built-in Modules

Module	Purpose
math	Mathematical functions
random	Random number generation
datetime	Date and time operations
os	File and system operations
sys	System-related functions
re	Regular expressions

🔹 Installing & Using Third-Party Modules

pip install numpy pandas

import numpy as np
import pandas as pd

✅ Use Case: Data science, AI model training, automation.