Generators in Python

A Generator is a function that returns an iterator and generates values one by one using the yield keyword.

Unlike normal functions (which use return), generators:

• Produce values lazily

• Save memory

• Handle large data efficiently

Normal Function vs Generator

Normal Function

def numbers():
    return [1, 2, 3, 4, 5]

This stores all numbers in memory.

Generator Function

def numbers():
    for i in range(1, 6):
        yield i

This generates one value at a time.

How to Use a Generator

gen = numbers()

print(next(gen))
print(next(gen))

Or:

for num in numbers():
    print(num)

Why Generators Are Powerful?

Imagine reading a large file (1GB file).

Using a normal list → High memory usage 
Using generator → Low memory usage 

Real Example: Reading a Large File

def read_file(filename):
    with open(filename) as file:
        for line in file:
            yield line

This reads file line by line without loading entire file into memory.

Generator Expressions

Similar to list comprehension, but use () instead of [].

squares = (x*x for x in range(5))

for num in squares:
    print(num)

This is memory efficient.

Decorators vs Generators (Quick Difference)

Feature	Decorators	Generators
Purpose	Modify function behavior	Generate values lazily
Keyword Used	@	yield
Memory Efficient	No	Yes
Used For	Logging, authentication	Large data processing

When Should You Use Them?

Use Decorators when:

• You want reusable functionality

• You don’t want to rewrite code

• You need logging or authentication

Use Generators when:

• Working with large data

• Reading files

• Processing streams

• Building pipelines