Generators

Generators are functions that behave like iterators: they pause, return a value with yield, and resume later. This lets us iterate large or infinite sequences without allocating memory for the entire collection.

Why Use Generators​

• Memory Efficient: Produce values on demand instead of building large lists.
• Composable: Can be chained with other iterators and generator expressions.
• Natural Fit For Streams: Reading large files, network streams, or infinite sequences.

Basic Generator Example​

from typing import Generator

def count_num_fast(num: int) -> Generator[int]:
    for i in range(1, num + 1):
        yield i

counts = count_num_fast(4)

print(next(counts))  # 1
print(next(counts))  # 2
print(next(counts))  # 3
print(next(counts))  # 4
print(next(counts))  # will raise StopIteration

Exact Output (uv run):

1
2
3
4
Traceback (most recent call last):
  File "/home/soymadip/Projects/soymadip.gitlab.io/static/code/python/7-generators-and-decoretors/52-generators.py", line 52, in <module>
    print(next(counts))  # will give error
          ~~~~^^^^^^^^
StopIteration

Infinite Generators​

Generators can produce potentially infinite sequences. Use them with caution and always consume a finite slice when needed.

def infinite_chai():
    count = 1
    while True:
        yield count
        count += 1

refill = infinite_chai()
for _ in range(10):
    print(next(refill))

Exact Output (uv run):

1
2
3
4
5
6
7
8
9
10