Python | Generator Expressions (original) (raw)

Last Updated : 4 Dec, 2025

In Python, iterators can be created using both regular functions and generators. Generators are similar to normal functions, but instead of return, they use the yield keyword. This allows the function to pause, save its state, and resume later making generators efficient and memory-friendly.

Generators are especially useful when working with large sequences because they produce values one at a time, only when needed (lazy evaluation), instead of storing the entire sequence in memory.

**Example: This example only shows how to define a generator using yield.

Python `

def simple_gen(): yield "A" yield "B" yield "C"

**Explanation:

The function becomes a generator because it uses yield.
Each yield provides a value when the generator is iterated.
No output will appear yet this example only defines a generator.

Two built-in functions, next() and iter(), make generators easy to use and help them work seamlessly with Python’s iterator protocol.

**Example: This generator prints a message before yielding each value, showing how the function pauses and resumes.

Python `

def generator(): t = 1 print('First result is', t) yield t

t += 1
print('Second result is', t)
yield t

t += 1
print('Third result is', t)
yield t

call = generator() next(call) next(call) next(call)

Output

First result is 1 Second result is 2 Third result is 3

**Explanation:

The function starts with t = 1, prints it, and yields it.
On each next(call), execution continues from the last yield.
The printed lines show when control enters each part of the generator.

Difference between Generator function and Normal function

A generator pauses at each yield and resumes where it left off.
After the last yield, calling next() raises StopIteration automatically.
Local variables and state are saved between calls.
Generator functions contain one or more yield statements instead of return.
next() and iter() work automatically, making generators easy to loop through.

What Are Generator Expressions

A generator expression is a shorter and more compact way to create a generator without using a function or yield. It looks similar to a list comprehension, but uses parentheses () instead of brackets [].

**Key Characteristics

Produces items one at a time (lazy evaluation).
Does not store all values in memory like a list comprehension does.
Ideal when you only need to iterate through the values once.
Returns a generator object, not a list.
Faster and more memory-efficient for large data sets.

**Syntax

(expression for element in iterable if condition)

This provides the convenience of comprehensions with the efficiency of generators.

**Example: This generator expression computes squares of numbers from 0 to 9 one-by-one.

Python `

generator = (num ** 2 for num in range(10)) for num in generator: print(num)

Output

0 1 4 9 16 25 36 49 64 81

**Explanation:

(num ** 2 for num in range(10)) creates a generator.
Each loop iteration fetches the next square using next() internally.

Creating a List Using a Generator Expression

Even though generator expressions produce values lazily, you can convert them into a list.

Python `

string = 'geek' li = list(string[i] for i in range(len(string)-1, -1, -1)) print(li)

Output

['k', 'e', 'e', 'g']

**Explanation:

The generator expression extracts characters from the end of the string.
list() collects all generated values and stores them in a list.