🚀 Day 99/150 – Generator Examples in Python

 

🚀 Day 99/150 – Generator Example in Python

A generator is a special type of function that produces values one at a time instead of returning all values at once. It uses the yield keyword instead of return, making it memory-efficient, especially when working with large datasets.

In this post, we'll explore four common examples of generators in Python.

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Method 1 – Basic Generator

Create a simple generator that yields numbers one by one.

def numbers():
    yield 1
    yield 2
    yield 3

gen = numbers()

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










Output

1
2
3
 
Explanation

• yield returns a value and pauses the function.
• next() resumes the generator from where it stopped.
• Each call to next() produces the next value.

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Method 2 – Generator with a Loop

Generate numbers from 1 to n.

def count(n):
    for i in range(1, n + 1):
        yield i

for num in count(5):
    print(num)






Output

1 2 3 4 5

Explanation

• The for loop generates numbers one by one.
• yield returns each number individually.
• The generator stops automatically after the last value.

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Method 3 – Generator Expression

Python also provides generator expressions, which are similar to list comprehensions.

squares = (x ** 2 for x in range(1, 6))

for square in squares:
    print(square)

Output

1 4 9 16 25

Explanation

• (x ** 2 for x in range(1, 6)) creates a generator expression.
• Unlike a list comprehension, it doesn't store all values in memory.
• Values are generated only when needed.

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Method 4 – Taking User Input

Generate numbers from 1 to the number entered by the user.

def generate_numbers(n):
    for i in range(1, n + 1):
        yield i

num = int(input("Enter a number: "))

for value in generate_numbers(num):
    print(value)











Sample Input

5

Output

1 2 3 4 5

Explanation

• The user enters a number.
• The generator produces numbers from 1 to that number.
• Each value is generated only when the loop requests it.

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Comparison of Methods

Method	Best For
Basic Generator	Understanding yield
Generator with Loop	Generating sequences
Generator Expression	Memory-efficient computations
User Input	Interactive programs

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🔥 Key Takeaways

• A generator is a function that uses the yield keyword.
• yield returns one value at a time and pauses the function.
• Generators are more memory-efficient than lists because they don't store all values at once.
• Use next() to retrieve values manually from a generator.
• Generator expressions provide a concise way to create generators.
• Generators are useful when working with large datasets or continuous data streams.