Iterators and Generators Intro

Iterator Mental Model

An iterator produces values one at a time. This enables streaming processing and predictable memory usage.

Iterator Basics

it = iter([1, 2, 3])
print(next(it))
print(next(it))

Generator Function

def read_lines(path: str):
    with open(path, "r", encoding="utf-8") as f:
        for line in f:
            yield line.rstrip("
")

Pipeline Style

def only_errors(lines):
    for line in lines:
        if "ERROR" in line:
            yield line

errors = only_errors(read_lines("app.log"))
for e in errors:
    print(e)

Operational Checklist

• Prefer generators for large inputs (logs, files, streams).
• Keep generators side-effect aware (resource management, ordering).
• Document whether a function returns a list or an iterator.

Failure Modes

• One-shot iterators: iterators are consumed; reusing requires re-creation.
• Hidden I/O: generator that reads files can fail late; handle errors intentionally.