Iterators and Iterable Objects Explained

An iterable is something you can loop over. An iterator is the object that gives you values one at a time during that loop.

This difference is important because it helps you understand:

• how for loops work
• why next() works on some objects but not others
• why some objects get "used up" after you loop through them

A good short way to remember it is:

• iterable = can be looped over
• iterator = produces the next value

Quick example

numbers = [10, 20, 30]
iterator = iter(numbers)

print(next(iterator))  # 10
print(next(iterator))  # 20
print(next(iterator))  # 30

Use iter() to get an iterator from an iterable like a list. Use next() to read one item at a time.

What this page covers

• What an iterable is
• What an iterator is
• How iter() and next() work
• How for loops use iterators behind the scenes
• What StopIteration means

What is an iterable?

An iterable is an object you can loop over with a for loop.

Common iterable objects include:

• lists
• tuples
• strings
• dictionaries
• sets
• range() objects
• files opened for reading

Example:

colors = ["red", "green", "blue"]

for color in colors:
    print(color)

Output:

red
green
blue

Here, colors is iterable because Python can go through its values one by one.

You can also pass an iterable to iter() to get an iterator:

colors = ["red", "green", "blue"]
iterator = iter(colors)

print(iterator)

The exact printed result may look different, but iterator is now an iterator object.

If you want a fuller beginner definition, see what is an iterable in Python.

What is an iterator?

An iterator is an object that gives values one at a time.

It keeps track of where it is. Each time you call next(), it returns the next item.

Example:

numbers = [10, 20, 30]
iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
print(next(iterator))

Output:

10
20
30

After the last value, there is nothing left to return. At that point, Python raises StopIteration.

numbers = [10, 20, 30]
iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
print(next(iterator))
print(next(iterator))  # no more items

This causes:

StopIteration

If you want a short definition page, see what is an iterator in Python.

Iterable vs iterator

These two terms are related, but they are not the same.

• An iterable is the object you can loop over
• An iterator is the object that produces items during the loop

For example:

numbers = [1, 2, 3]

print(hasattr(numbers, "__iter__"))
print(hasattr(numbers, "__next__"))

Output:

True
False

A list is iterable, but it is not usually an iterator.

Now look at an iterator created from that list:

numbers = [1, 2, 3]
iterator = iter(numbers)

print(hasattr(iterator, "__iter__"))
print(hasattr(iterator, "__next__"))

Output:

True
True

This shows that the iterator can both be iterated over and provide the next value.

Also, calling iter() on an iterator usually returns the same iterator:

numbers = [1, 2, 3]
iterator = iter(numbers)

print(iter(iterator) is iterator)

Output:

True

How iter() and next() work

These two functions are the core of Python's iteration system.

iter(object)

iter(object) asks Python for an iterator for that object.

Example:

text = "abc"
iterator = iter(text)

print(next(iterator))
print(next(iterator))
print(next(iterator))

Output:

a
b
c

next(iterator)

next(iterator) gets the next item from the iterator.

If there are no items left, Python raises StopIteration.

text = "ab"
iterator = iter(text)

print(next(iterator))
print(next(iterator))
print(next(iterator))

This raises StopIteration on the third call.

You can handle that yourself with try and except:

text = "ab"
iterator = iter(text)

try:
    while True:
        print(next(iterator))
except StopIteration:
    print("Iterator is finished")

Output:

a
b
Iterator is finished

If you want to learn more about this exception, see StopIteration exception in Python explained.

How for loops use iterators

A for loop does this behind the scenes:

1. Calls iter() on the object
2. Repeatedly calls next()
3. Stops when StopIteration is raised

This means code like this:

numbers = [10, 20, 30]

for number in numbers:
    print(number)

works roughly like this:

numbers = [10, 20, 30]
iterator = iter(numbers)

while True:
    try:
        number = next(iterator)
        print(number)
    except StopIteration:
        break

Output for both:

10
20
30

You do not need to write loops this way in normal code. But understanding it makes for loops much easier to understand.

If you want a full beginner guide, see Python for loops explained.

Examples of iterable objects

Here are some common iterable objects in Python.

Lists and tuples

items = [1, 2, 3]
for item in items:
    print(item)

values = (4, 5, 6)
for value in values:
    print(value)

Strings

for letter in "cat":
    print(letter)

Output:

c
a
t

Dictionaries and sets

A dictionary loops over its keys by default:

person = {"name": "Ava", "age": 25}

for key in person:
    print(key)

A set is also iterable:

numbers = {1, 2, 3}

for number in numbers:
    print(number)

range() objects

range() is iterable, which is why it works well in loops.

for number in range(3):
    print(number)

Output:

0
1
2

You can learn more on the Python range() function explained.

Files opened for reading

Files are iterable too. Python can read them one line at a time.

with open("example.txt", "r") as file:
    for line in file:
        print(line.strip())

This is useful because it reads values one at a time instead of loading everything at once.

When iterators are useful

Iterators are helpful when you want to work with data step by step.

Common uses:

• Reading values one at a time
• Working with large data without loading everything at once
• Building custom classes that support loops
• Understanding generators more easily

A very common real-world example is a generator. Generators are a special kind of iterator, so learning this topic makes generators in Python explained much easier to understand.

Beginner mistakes to avoid

Here are some common problems beginners run into.

Using next() on a list instead of on an iterator

This is wrong:

numbers = [10, 20, 30]
print(next(numbers))

A list is iterable, but it is not an iterator. You need iter() first:

numbers = [10, 20, 30]
iterator = iter(numbers)

print(next(iterator))

Thinking every iterable is already an iterator

Objects like lists, strings, and tuples are iterable, but they do not usually have __next__() directly.

Check it:

text = "hello"
print(hasattr(text, "__next__"))

Output:

False

Forgetting that iterators get used up

Once you consume an iterator, it does not automatically restart.

numbers = [1, 2, 3]
iterator = iter(numbers)

for item in iterator:
    print(item)

for item in iterator:
    print(item)  # nothing prints

If you want to loop again, create a new iterator:

numbers = [1, 2, 3]

for item in iter(numbers):
    print(item)

for item in iter(numbers):
    print(item)

Not handling StopIteration when calling next() manually

If you call next() yourself, you may need to handle the end of the iterator.

numbers = [1, 2]
iterator = iter(numbers)

try:
    print(next(iterator))
    print(next(iterator))
    print(next(iterator))
except StopIteration:
    print("No more items")

Output:

1
2
No more items

Useful checks when debugging

If you are not sure whether an object is iterable or an iterator, these checks can help:

obj = [1, 2, 3]

print(type(obj))
print(hasattr(obj, "__iter__"))
print(hasattr(obj, "__next__"))

You can also try creating an iterator:

obj = [1, 2, 3]
iterator = iter(obj)

print(type(iterator))
print(next(iterator))

Useful commands:

• type(obj)
• iter(obj)
• next(iterator)
• hasattr(obj, '__iter__')
• hasattr(obj, '__next__')

FAQ

Is every iterable also an iterator?

No. Many objects like lists and strings are iterable, but you must call iter() to get an iterator from them.

What happens when an iterator runs out of values?

Python raises StopIteration. A for loop handles this automatically.

Can I loop over the same iterator twice?

Usually no. Once an iterator is exhausted, it does not start over unless you create a new one.

Why should beginners learn this?

It helps you understand for loops, generators, files, and many common Python tools.

See also

• Python for loops explained
• Generators in Python explained
• Python range() function explained
• StopIteration exception in Python explained
• What is an iterable in Python
• What is an iterator in Python

If this topic makes sense now, the best next step is to learn generators. They are one of the most common real-world examples of iterators in Python.