Iterable (Python)

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Definition: In Python, an iterable is any object that can be traversed element by element. Technically, an iterable is an object capable of returning an iterator when the built-in function iter() is applied to it. This allows it to be used in for loops, list comprehensions, and many built-in functions that consume sequences.

Iterable vs Iterator

• Iterable: an object capable of providing an iterator (e.g. list, tuple, dict, set, str).
• Iterator: an object that knows how to return elements one by one using next() until the sequence is exhausted (raising StopIteration).

colors = {"red", "blue", "green"}   # a set is iterable
it = iter(colors)                   # obtain an iterator (set_iterator)
print(next(it))                     # returns one element from the set

Sets (set) as Iterables

Python set objects are iterable: they can be traversed with a for loop and converted into other collections. They are unordered and do not allow duplicates.

A = {"red", "blue", "green"}

for color in A:
    print(color)   # order is not guaranteed

lst = list(A)      # conversion possible, but order may vary

Consequences of Unordered Nature

• You cannot index a set (e.g. A[0] is invalid).
• If order is required, convert to a list and sort it: sorted(A).

Examples of Iteration

# List
nums = [2, 4, 6]
for n in nums:
    print(n)

# Dictionary (iterates over keys)
d = {"a": 1, "b": 2}
for k in d:            # equivalent to: for k in d.keys():
    print(k, d[k])

# Set
s = {1, 2, 3}
for x in s:
    print(x)

Useful Functions and Patterns with Iterables

• len(iterable), sum(numbers), max(iterable), min(iterable)
• list(iterable), tuple(iterable), set(iterable)
• Comprehensions: [f(x) for x in iterable], {f(x) for x in iterable}
• any(it) and all(it) for efficient logical checks

Common Errors and Best Practices

• Set indexing: not allowed; use sorted(set_) if you need positions.
• Iterator exhaustion: once an iterator is consumed, it cannot be reused; create a new one with iter(iterable) if needed.
• Large data: prefer generators for memory efficiency when working with big sequences.

See Also

• Iterator (Python): object returned by iter() and consumed by next().
• Set (set): iterable, unordered collection without duplicates.
• Comprehensions: concise syntax to build new collections from iterables.
• Generators and yield: create custom iterators that produce values on demand.

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