🐍 Lesson 18: Python Iterators & Generators – Efficient Loops

Welcome to Lesson 18! Today we’ll learn about iterators and generators in Python. These tools help loop through data efficiently and save memory, especially for large datasets.

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⭐ What You Will Learn in This Lesson

• Understand what iterators and generators are
• Learn how to create and use iterators
• Work with generators for efficient memory usage
• Use generator expressions for cleaner code
• Improve performance when handling large datasets

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👥 Who Is This Lesson For?

• Beginners and intermediate Python developers looking to optimize loops
• Anyone working with large datasets or streaming data
• Developers aiming to improve performance and memory efficiency in their Python programs

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📦 What Is an Iterator?

An iterator is an object that represents a stream of data. It can be traversed using the next() function.

# Create a list
numbers = [1, 2, 3]

# Get iterator
it = iter(numbers)

print(next(it))  # 1
print(next(it))  # 2
print(next(it))  # 3
# print(next(it))  # Raises StopIteration

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🧩 1. Using Iterators in Loops

numbers = [10, 20, 30]
for num in numbers:   # implicitly uses iterator
    print(num)

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📦 What Is a Generator?

A generator is a special type of iterator that generates values on the fly using yield instead of storing everything in memory.

def my_generator():
    for i in range(5):
        yield i

gen = my_generator()
print(next(gen))  # 0
print(next(gen))  # 1

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🧩 2. Using Generators in Loops

def squares(n):
    for i in range(n):
        yield i**2

for val in squares(5):
    print(val)

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🧩 3. Generator Expressions

Generators can also be created in a single line:

squares = (x**2 for x in range(5))
for val in squares:
    print(val)

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🧩 4. Why Iterators & Generators Matter

• Efficient memory usage for large datasets
• Useful for streaming data, logs, or files
• Can create infinite sequences without crashing
• Improve performance in loops and data pipelines

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🧪 Practice

1. Create a generator that yields even numbers up to 20.
2. Create a generator for Fibonacci numbers up to n terms.
3. Use a generator expression to generate squares of numbers from 1 to 10.
4. Iterate through a list using an iterator and print all elements.
5. Combine iterators and generators to process large datasets efficiently.

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❌ Common Mistakes

• Trying to access elements beyond the iterator’s range without handling the StopIteration exception
• Misunderstanding the difference between regular functions and generator functions
• Creating generators that yield too many items, consuming too much memory

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❓ Frequently Asked Questions (FAQ)

1. What’s the difference between iterators and generators?

Iterators traverse through data, while generators create data on the fly with yield, making them more memory-efficient.

2. Can I use a generator for large datasets?

Yes! Generators are ideal for working with large datasets as they don’t store all values in memory at once.

3. How do I create infinite sequences using generators?

You can create infinite generators using yield in a loop that never ends, like generating Fibonacci numbers.

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🚀 What’s Next?

In the next lesson, you’ll learn about:

• Classes and Object-Oriented Programming (OOP) in Python
• How to design and implement classes
• Encapsulation, inheritance, and polymorphism in Python

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➡ Next Lesson

Go to Lesson 19 →