Python9 min read

Python Generators: Memory-Efficient Iteration

Learn Python generators and why they're so useful. They save memory, make your code cleaner, and are perfect for working with large datasets. This is a must-know concept for Python developers.

Alex Martinez

December 18, 2025

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Generators are one of Python's most elegant features. They let you create iterators without storing everything in memory. Perfect for large datasets, infinite sequences, or when you want cleaner code.

What Are Generators?

Generators are functions that use yield instead of return. When you call them, they don't run immediately - they return a generator object. You iterate over it, and it produces values one at a time.

Why Use Generators?

They're memory efficient - instead of creating a huge list, generators produce values on-demand. They're also lazy, meaning they only compute what you need. This makes them perfect for large files or infinite sequences.

Generator Expressions

Just like list comprehensions, but for generators. They use parentheses instead of square brackets and are super memory efficient. I'll show you when to use them.

Real-World Examples

I'll show you practical examples - reading large files, processing data streams, creating infinite sequences. These are patterns you'll use in real projects.

#Python#Generators#Iterators#Memory Efficiency

Common Questions & Answers

What is a generator in Python?

A generator is a function that uses `yield` instead of `return`. It returns a generator object that produces values one at a time when iterated. Generators are memory efficient because they don't store all values in memory at once.

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Run Explain

# Regular function (stores all in memory)
def squares_list(n):
    result = []
    for i in range(n):
        result.append(i * i)
    return result

# Generator (produces on-demand)
def squares_generator(n):
    for i in range(n):
        yield i * i

# Usage
for square in squares_generator(1000000):
    print(square)  # Memory efficient!

What's the difference between a generator and a list?

A list stores all values in memory at once. A generator produces values one at a time, only when needed. Generators are memory efficient for large datasets, but you can only iterate over them once. Lists can be accessed multiple times.