iter()

iter() is a built-in Python function that returns an iterator object from an iterable — enabling manual iteration with next(), custom loops, or integration into generator expressions and comprehensions. In 2026, iter() remains a foundational tool in data science (chunked processing in pandas/Polars/Dask), software engineering (custom iterators, sentinel-based loops), and functional programming — supporting the iterator protocol (__iter__, __next__), sentinel values for termination, and seamless use with next(), for loops, and async iterators. It’s the bridge between iterables (lists, generators, files) and explicit iteration control.

Here’s a complete, practical guide to using iter() in Python: basic iterator creation, sentinel mode, real-world patterns (earthquake chunked reading, log tailing, custom generators), and modern best practices with type hints, error handling, performance, and integration with Dask/Polars/pandas/NumPy/asyncio.

Basic iter() — create iterator from any iterable, step with next().


# From list
lst = [1, 2, 3, 4, 5]
it = iter(lst)

print(next(it))  # 1
print(next(it))  # 2
print(list(it))  # [3, 4, 5] (consumes remaining)

# From string
s = "hello"
it_str = iter(s)
print(''.join(next(it_str) for _ in range(3)))  # hel

# From file (line-by-line)
with open('quakes.txt') as f:
    it_file = iter(f)
    first_line = next(it_file)
    print(first_line.strip())

Sentinel mode — iter(callable, sentinel) — calls callable until sentinel returned.


# Read lines until EOF (sentinel '')
with open('large_log.txt') as f:
    for line in iter(f.readline, ''):
        print(line.strip())

# Simulate producer until None
def get_next():
    return 42  # or real data source

for value in iter(get_next, None):
    print(value)  # infinite loop unless get_next returns None

Real-world pattern: chunked reading & processing large earthquake data — manual iteration control.


import dask.dataframe as dd

# Dask lazy read (internal iter)
ddf = dd.read_csv('earthquakes/*.csv', blocksize='64MB')

# Manual chunk iteration (for custom processing)
chunks = iter(ddf.to_delayed())
first_chunk = next(chunks).compute()
print(first_chunk.head())

# Sentinel-based reading from generator
def read_quake_stream():
    for chunk in ddf.to_delayed():
        yield chunk.compute()

for chunk in iter(read_quake_stream, None):
    strong = chunk[chunk['mag'] >= 7.0]
    if strong.empty:
        break
    print(strong[['time', 'mag', 'place']].head())

Best practices for iter() in Python & data workflows. Prefer for item in iterable — when consuming entire sequence; use iter() + next() for manual control. Modern tip: use Polars pl.scan_csv(...).collect(streaming=True) — streaming iteration; Dask for parallel chunks. Use sentinel mode — iter(callable, sentinel) for producer patterns (queues, files, streams). Add type hints — from typing import Iterator; def get_iterator() -> Iterator[int]. Use next(it, default) — safe next with fallback. Handle StopIteration — in manual loops. Use itertools.islice(iterable, n) — take first n items. Use itertools.tee() — duplicate iterator. Use itertools.chain() — combine iterables. Use itertools.cycle() — infinite cycling. Use itertools.count() — infinite counter. Use itertools.repeat() — infinite repeat. Use itertools.accumulate() — cumulative sums. Use itertools.groupby() — group consecutive items. Use itertools.starmap() — unpack tuples. Use itertools.compress() — selective filtering. Use itertools.dropwhile()/takewhile() — conditional dropping/taking. Use itertools.filterfalse() — inverse filter. Use itertools.zip_longest() — zip with fillvalue. Use itertools.product() — cartesian product. Use itertools.permutations()/combinations() — permutations/combinations. Use itertools.batched() — chunking (Python 3.12+). Use itertools.pairwise() — consecutive pairs (Python 3.10+).

iter(obj[, sentinel]) creates an iterator from any iterable — or calls a callable until sentinel value, enabling manual control, sentinel termination, and integration with next(), for, and itertools. In 2026, use sentinel mode for streams, itertools for advanced iteration, and integrate with Dask/Polars for chunked/streaming data. Master iter(), and you’ll control iteration precisely and efficiently in any Python workflow.

Next time you need to iterate manually or handle streams — use iter(). It’s Python’s cleanest way to say: “Give me an iterator — from any sequence or callable.”

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