Generators & `yield`

Advanced Iteration

Generators Make Data Streams

Generators produce values on demand, minimize memory usage, and support coroutine-style communication.

Write yield-based functionsCompose with yield from

Basics

def read_in_chunks(path, chunk_size=1024):
    with open(path, 'rb') as fh:
        while chunk := fh.read(chunk_size):
            yield chunk

• Use yield to pause a function and resume later.
• Generators automatically implement the iterator protocol.

yield from

def flatten(sequences):
    for seq in sequences:
        yield from seq

• Delegates to another iterable without manual loops.

Generator expressions

total = sum(price for price in prices if price > 0)

• Parentheses produce a generator expression.
• Perfect feed for sum, any, all, max, etc.

Next up in your learning path

Decorators & context managers

Pair generators with custom decorators for pipelines.

Pipeline

itertools

Compose streaming logic with standard helpers.

Companion

Async generators (future)

Prepare for asyncio by mastering sync generators now.

Preview