B.Tech • Chapter 5

Functions, Generators, & Decorators

B.Tech level module on Functions, Generators, & Decorators.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate time complexity of related algorithms.Understand memory management and background architecture.Analyze code snippets for bugs or inefficiencies.Explain theoretical concepts with real-world technical examples.

Functions in Python are First-Class Objects, meaning they can be passed as arguments, returned from other functions, and assigned to variables. This enables functional programming paradigms and metaprogramming through Decorators.

Advanced Engineering Concepts

Generators use the 'yield' keyword to return an iterator that computes one value at a time. This is lazy evaluation, which is extremely memory efficient for large datasets (e.g., reading gigabytes of log files) because it avoids loading everything into RAM at once. Decorators are wrappers that modify the behavior of a function dynamically, heavily used in web frameworks like Flask and Django.

1Understand the Big O time and space complexities involved.
2Ensure memory leaks and race conditions are mitigated.
3Adhere to PEP 8 standards for clean, readable code.
4Utilize unit testing to verify algorithmic correctness.

Code Implementation

# Generator for large sequences
def fibonacci_gen(limit):
    a, b = 0, 1
    while a < limit:
        yield a
        a, b = b, a + b

# Decorator for timing execution
import time
def timer_decorator(func):
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        print(f"Execution time: {time.time() - start}s")
        return result
    return wrapper

@timer_decorator
def heavy_computation():
    sum(i*i for i in range(1000000))

heavy_computation()

📝 Quick Revision Points

1Review the CPython implementation details.
2Practice the code snippets until syntax is committed to memory.
3Test edge cases and exception handling.
4Understand trade-offs between different data structures.

← PreviousHashing: Tuples, Sets, & Dictionaries Next →File I/O and Serialization

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# Generator for large sequences def fibonacci_gen(limit): a, b = 0, 1 while a < limit: yield a a, b = b, a + b # Decorator for timing execution import time def timer_decorator(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) print(f"Execution time: {time.time() - start}s") return result return wrapper @timer_decorator def heavy_computation(): sum(i*i for i in range(1000000)) heavy_computation()