Generators in Python are a simple way to create iterators using functions and the yield keyword.
They allow you to generate values one at a time, instead of returning them all at once — making them memory-efficient and faster for large datasets.

FeatureDescription
DefinitionA function that returns an iterator object which generates values on the fly.
Keyword Usedyield instead of return.
Memory UsageLow — values are produced only when needed (lazy evaluation).
Execution StateRemembers its state between each yield call.
Use CaseHandling large data, streaming, infinite sequences, etc.

🧩 Example 1: Basic Generator

def my_generator():
    for i in range(3):
        yield i

# Using the generator
gen = my_generator()
print(next(gen))  # First value
print(next(gen))  # Second value
print(next(gen))  # Third value

Output:

0
1
2

(If you call next(gen) again, it raises StopIteration.)

🧩 Example 2: Using Generators with Loops

def countdown(n):
    while n > 0:
        yield n
        n -= 1

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

Output:

5
4
3
2
1

🧩 Example 3: Generator Expression (Compact Syntax)
Generators can also be created using parentheses () instead of square brackets [].

squares = (x**2 for x in range(5))
print(next(squares))
print(next(squares))
print(list(squares))  # Convert remaining items to list

Output:

0
1
[4, 9, 16]

🧩 Example 4: Infinite Generator
Generators can create infinite sequences because they produce values lazily.

def infinite_numbers():
    n = 1
    while True:
        yield n
        n += 1

gen = infinite_numbers()

for i in range(5):
    print(next(gen))

Output:

1
2
3
4
5

In Summary:

  • Generators are iterators built using functions and yield.
  • They save memory by producing one value at a time instead of generating all at once.
  • Ideal for streaming large data, pipelines, and infinite sequences.
  • yield pauses the function, and next() resumes it from where it left off.
Scroll to Top