Python Iterators – Iter() & Next() with Examples

Introduction

Python provides powerful tools for working with collections of data. Whether you’re looping through a list, tuple, string, dictionary, or file, Python uses a concept called iteration behind the scenes. The core component that makes iteration possible is the iterator.

An iterator is an object that allows you to traverse through elements one at a time without needing to know how the data is stored internally. Iterators are memory-efficient because they generate values on demand rather than loading everything into memory at once.

For example, when you use a for loop:

numbers = [10, 20, 30]
for num in numbers:
    print(num)

Note: Python automatically converts the list into an iterator and retrieves each element one by one.

What are Python Iterators?

An iterator is an object that allows sequential access to elements in a collection.

It retrieves one item at a time and remembers its current position.

Example:

numbers = [1, 2, 3, 4]
iterator = iter(numbers)
print(next(iterator))

Output:

Explanation:

• iter() creates an iterator object.
• next() retrieves the next element.

Iterable vs Iterator

Many beginners confuse iterables and iterators.

Iterable Example:

numbers = [1, 2, 3]

A list is an iterable.

Iterator Example:

iterator = iter(numbers)

Now it becomes an iterator.

How Iterators Work

Python iterators use two special methods:

iter()

Returns the iterator object.

next()

Returns the next value.

When no values remain, Python raises StopIteration.

Creating an Iterator

Example:

fruits = ["Apple", "Banana", "Mango"]
iterator = iter(fruits)
print(next(iterator))
print(next(iterator))
print(next(iterator))

Output:

Each call to next() returns the next item.

What Happens When Items End?

Example:

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

Output:

The iterator raises a StopIteration exception when all elements are exhausted.

Using Iterators with for Loops

Most of the time, iterators are used automatically.

Example:

numbers = [10, 20, 30]
for num in numbers:
    print(num)

Output:

Internally Python performs:

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

Iterating Through Strings

Strings are iterable objects.

Example:

text = "Python"
iterator = iter(text)
print(next(iterator))
print(next(iterator))

Output:

Iterating Through Tuples

Example:

colors = ("Red", "Green", "Blue")
iterator = iter(colors)
print(next(iterator))

Output:

Iterating Through Dictionaries

Example:

student = {
    "name": "John",
    "age": 20
}
iterator = iter(student)
print(next(iterator))

Output:

By default, dictionary iteration returns keys.

Creating a Custom Iterator

Python allows you to create your own iterator classes.

Example:

class Counter:
    def __init__(self):
        self.num = 1
    def __iter__(self):
        return self
    def __next__(self):
        if self.num <= 5:
            value = self.num
            self.num += 1
            return value
        raise StopIteration

Usage:

counter = Counter()
for value in counter:
    print(value)

Output:

Understanding iter()

The __iter__() method returns the iterator object.

Example:

def __iter__(self):
    return self

This tells Python that the object itself acts as an iterator.

Understanding next()

The __next__() method returns the next item.

Example:

def __next__(self):
    return value

If no more items exist, raise StopIteration must be used.

Infinite Iterators

Iterators can generate endless values.

Example:

class InfiniteNumbers:
    def __init__(self):
        self.num = 1
    def __iter__(self):
        return self
    def __next__(self):
        value = self.num
        self.num += 1
        return value

Usage:

numbers = InfiniteNumbers()
print(next(numbers))
print(next(numbers))
print(next(numbers))

Output:

Be careful because such iterators never stop automatically.

Real-Life Examples:

1. File Reading

Files are iterators in Python.

Example:

file = open("data.txt")
for line in file:
    print(line)

Benefits:

• Reads one line at a time
• Saves memory
• Efficient for large files

2. Employee ID Generator

class EmployeeID:
    def __init__(self):
        self.id = 1000
    def __iter__(self):
        return self
    def __next__(self):
        self.id += 1
        return self.id

Usage:

employee = EmployeeID()
print(next(employee))
print(next(employee))

Output:

Useful for generating unique IDs.

3. Countdown System

class Countdown:
    def __init__(self, start):
        self.start = start
    def __iter__(self):
        return self
    def __next__(self):
        if self.start <= 0:
            raise StopIteration
        current = self.start
        self.start -= 1
        return current

Usage:

count = Countdown(5)
for num in count:
    print(num)

Output:

Built-in Functions Related to Iterators

iter()

Creates an iterator.

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

next()

Returns the next item.

next(iterator)

enumerate()

Creates an iterator with index values.

names = ["A", "B"]
for index, name in enumerate(names):
    print(index, name)

Output:

Iterator Protocol

To create a valid iterator, a class must implement:

This is known as the Iterator Protocol.

Advantages of Iterators

Disadvantages of Iterators

Common Mistakes

1. Forgetting StopIteration

Incorrect:

def __next__(self):
    return self.num

Without raise StopIteration the loop may never end.

2. Calling next() Too Many Times

Incorrect:

iterator = iter([1])
next(iterator)
next(iterator)

Output:

Always handle exhaustion properly.

3. Confusing Iterable and Iterator

Incorrect:

numbers = [1, 2, 3]
next(numbers)

Output:

Correct:

iterator = iter(numbers)
next(iterator)

4. Missing iter()

A custom iterator must implement:

__iter__()

Otherwise, it won’t work correctly with loops.

5. Returning Wrong Values

Always return the next item from __next__().

Conclusion

Python Iterators are a fundamental concept that enables efficient data traversal and lazy evaluation. They allow elements to be processed one at a time, making them ideal for large datasets, file handling, data streams, and custom data structures.

Python’s iterator protocol, built around the __iter__() and __next__() methods, provides a flexible way to create custom iteration behavior.

Python Iterators – Interview Questions

Python Iterator – Objective Questions (MCQs)

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