Generators in Python

Learn how Python generators produce values one at a time using the yield keyword.

What is a Generator?

A generator is a special type of function that produces values one at a time instead of creating and returning all values at once.

Generators use the yield keyword to return a value and pause the function until the next value is requested.

Generator Flow

Basic Syntax

def generator_function():

    yield value

A function containing yield becomes a generator function.

Simple Generator Example

def numbers():

    yield 10
    yield 20
    yield 30


for number in numbers():

    print(number)

Output:

10
20
30

The generator produces each value one at a time.

Using yield

The yield keyword returns a value from a generator and pauses its execution.

def show_numbers():

    yield 1
    yield 2
    yield 3


generator = show_numbers()

print(next(generator))
print(next(generator))
print(next(generator))

Output:

1
2
3

Generator with next()

We can use next() to retrieve values from a generator one at a time.

def colors():

    yield "Red"
    yield "Green"
    yield "Blue"


generator = colors()

print(next(generator))
print(next(generator))
print(next(generator))

Output:

Red
Green
Blue

Generator with for Loop

A generator can be directly used with a for loop.

def courses():

    yield "Python"
    yield ".NET"
    yield "Java"


for course in courses():

    print(course)

Output:

Python
.NET
Java

Generator for Numbers

def count_numbers():

    for number in range(1, 6):

        yield number


for number in count_numbers():

    print(number)

Output:

1
2
3
4
5

Generator Expression

A generator expression provides a short way to create a generator.

numbers = (number * 2 for number in range(1, 6))

for number in numbers:

    print(number)

Output:

2
4
6
8
10

Generator vs List

numbers = [1, 2, 3, 4, 5]

numbers = (x for x in range(1, 6))

Memory Efficiency

Generators are useful when working with large amounts of data because values can be generated one at a time.

def large_numbers():

    number = 1

    while number <= 1000000:

        yield number

        number += 1


for number in large_numbers():

    if number == 5:

        print(number)

        break

The generator produces values as needed instead of creating all one million values at once.

Example 👀🌍

The following example generates student marks one at a time.

def student_marks():

    yield 75
    yield 82
    yield 90


for marks in student_marks():

    print("Marks:", marks)

Output:

Marks: 75
Marks: 82
Marks: 90

Advantages of Generators

Best Practices

Common Mistakes

• Confusing yield with return.
• Calling next() after the generator has no more values.
• Converting a generator to a list unnecessarily.
• Forgetting that a generator is consumed as values are requested.

Practice Programs

1. Create a generator that produces numbers from 1 to 10.
2. Create a generator that produces even numbers.
3. Create a generator for course names.
4. Use next() to retrieve generator values.
5. Create a generator that produces student marks.