Generator in Python? What is Generator?

Python Generators – Complete Beginner to Advanced Guide

A Generator in Python is a special type of function that allows you to create your own iterator. It returns values one-by-one using the yield keyword instead of returning all values at once.

Generators are extremely powerful when working with large data, streams, loops, or memory-sensitive applications.

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What Is a Generator?

A generator is a function that behaves like an iterator. Instead of returning a single value and ending execution, a generator:

• Produces a sequence of values
• Pauses after each value using yield
• Resumes execution from the same point when next() is called

In simple words: Generator = Function + Iterator + Yield

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Why Use Generators?

• They save memory (lazy evaluation)
• They generate values on-demand
• Faster performance for large datasets
• Replaces complex iterator classes
• Easy to read and write

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Difference Between yield and return

yield	return
Pauses function execution	Ends function execution
Returns value without losing state	Returns final value and loses state
Used to create generators	Used to return a final result

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Basic Generator Example

def fun():
    print(10)
    yield
    
    # return 1   # return stops execution permanently
    
    print(20)
    yield

# Calling generator
x = fun()
next(x)
next(x)

Explanation:

• The first next(x) prints 10 and pauses at yield.
• The second next(x) resumes execution, prints 20, and pauses again.

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Generator for Looping (Iterator Example)

def loop(x):
    for i in range(x):
        yield i

l = loop(5)

while True:
    try:
        print(next(l))
    except StopIteration:
        break

This generator behaves exactly like range(), generating values one by one.

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Extra Generator Examples (Added for Better Learning)

1. Generator to Produce Even Numbers

def even_numbers(n):
    for i in range(0, n+1, 2):
        yield i

for num in even_numbers(10):
    print(num)

2. Generator to Read Large Files Efficiently

def read_file_line_by_line(filename):
    with open(filename, "r") as f:
        for line in f:
            yield line

for line in read_file_line_by_line("data.txt"):
    print(line)

Use Case: You can read GB-size files without loading them fully into memory.

3. Infinite Generator (Useful for Streaming)

def infinite_counter():
    num = 1
    while True:
        yield num
        num += 1

counter = infinite_counter()

print(next(counter))
print(next(counter))
print(next(counter))

4. Generator Expression (Short Syntax)

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

for n in squares:
    print(n)

Similar to list comprehension but memory efficient.

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When Should You Use Generators?

• Processing large datasets
• Streaming data applications
• Building custom iterators
• Reading big files line-by-line
• Creating pipelines (just like Unix)

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YouTube Video (Reference)

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Assignments for Practice

1. Create a generator that produces Fibonacci series.
2. Create a generator that yields prime numbers up to 100.
3. Create a generator that yields characters of a string one by one.
4. Create a generator for multiplication table of a number.
5. Create an infinite generator for random OTP generation.