In Python, iterators are objects that allow looping through collections by implementing __iter__() and __next__(). 1) Iterators work via the iterator protocol, using __iter__() to return the iterator and __next__() to retrieve the next item until StopIteration is raised. 2) An iterable (like a list or dict) provides an iterator via __iter__(), while the iterator manages iteration state. 3) Custom iterators can be built in classes, useful for memory-efficient or infinite sequences like generating even numbers without storing them all. 4) Common pitfalls include exhausted iterators not resetting and improper reuse across loops or functions like sum().
Python iterators are objects that let you loop through collections like lists, tuples, or dictionaries one item at a time. They’re the behind-the-scenes mechanism that makes loops like for work in Python.
How do iterators work in Python?
At the core of Python’s iteration system is the iterator protocol, which requires two methods:
-
__iter__()– returns the iterator object itself -
__next__()– returns the next value from the collection
When you use a for loop on a list or dictionary, Python automatically creates an iterator and calls next() until there are no items left. When there are no more items, __next__() raises a StopIteration exception to signal the end of iteration.
For example:
my_list = [1, 2, 3] it = iter(my_list) print(next(it)) # prints 1 print(next(it)) # prints 2
You can also build your own custom iterator by defining a class with these two methods.
What’s the difference between iterable and iterator?
This is a common point of confusion for beginners.
- An iterable is any object that can return an iterator. Examples include lists, strings, dictionaries, and files.
- An iterator is the object that actually does the iterating — it keeps track of the current state and returns the next item.
So when you call iter(some_list), you get an iterator back. The iterable gives you access to the data, while the iterator handles how you go through it.
Here’s a quick way to remember:
- Iterable: has
__iter__() - Iterator: has both
__iter__()and__next__()
When would you use a custom iterator?
Custom iterators are useful when you want to define your own way of traversing a sequence — maybe you're dealing with infinite sequences, or you want to control memory usage by generating values on the fly.
For instance, imagine you want to generate even numbers without storing them all in memory:
class EvenNumbers:
def __init__(self, limit):
self.limit = limit
self.value = 0
def __iter__(self):
return self
def __next__(self):
if self.value >= self.limit:
raise StopIteration
result = self.value
self.value = 2
return resultThen you can use it like this:
for num in EvenNumbers(10):
print(num)This will print even numbers from 0 to 8.
Common pitfalls when working with iterators
There are a few things to watch out for:
- Once you’ve exhausted an iterator, calling
next()again will raiseStopIteration. - Iterators don’t reset themselves — once done, they stay done.
- If you reuse an iterator in multiple places, you might find nothing left after the first loop.
To avoid issues:
- Don't reuse the same iterator multiple times; create a new one each time.
- Use built-in functions like
list()ortuple()if you need to store results for later use. - Be careful mixing iterators with functions that consume them fully, like
sum()ormax().
That's basically how iterators work in Python — not too flashy, but essential for handling loops efficiently.
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