Python Iterators Tutorial – Complete Guide with Examples

Python - Iterators 

When working with loops in Python, you often use for loops to go through lists, tuples, or other collections. But have you ever wondered how Python actually goes through these items one by one?

The answer is Iterators.

Iterators are a powerful concept that allows you to traverse through data efficiently.

In this tutorial, you will learn what iterators are, how they work, and how to create your own iterators in Python.

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What is an Iterator?

An iterator is:

An object that allows you to access elements of a collection one at a time.

It follows the iterator protocol in Python.

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Iterator Protocol

An object is an iterator if it implements:

• __iter__() → returns the iterator object itself
• __next__() → returns the next value

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Iterable vs Iterator

Iterable	Iterator
Object that can be looped	Object that produces values one at a time
Example: list, tuple	Example: result of iter()
Cannot directly use next()	Supports next()

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Creating an Iterator

Python provides the iter() function to create an iterator.

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Example

numbers = [1, 2, 3]

it = iter(numbers)

print(next(it))
print(next(it))
print(next(it))

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Output

1
2
3

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How Iterators Work

• iter() converts iterable into iterator
• next() fetches values one by one
• When no values remain → StopIteration error

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Example with StopIteration

numbers = [1, 2]

it = iter(numbers)

print(next(it))
print(next(it))
print(next(it))  # Error here

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Using Iterator in For Loop

Python automatically uses iterators in loops.

numbers = [1, 2, 3]

for n in numbers:
    print(n)

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Behind the Scene

The loop does this internally:

it = iter(numbers)

while True:
    try:
        value = next(it)
        print(value)
    except StopIteration:
        break

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Creating Custom Iterator

You can create your own iterator class.

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Example

class MyNumbers:
    def __init__(self):
        self.num = 1

    def __iter__(self):
        return self

    def __next__(self):
        if self.num <= 5:
            val = self.num
            self.num += 1
            return val
        else:
            raise StopIteration

obj = MyNumbers()
it = iter(obj)

for i in it:
    print(i)

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Output

1
2
3
4
5

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Why Use Iterators?

Iterators are useful because they:

• Save memory
• Process data lazily
• Improve performance
• Handle large datasets efficiently

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Iterator vs List

Feature	List	Iterator
Memory	High	Low
Speed	Fast access	Lazy access
Storage	All data stored	One item at a time

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Real-World Example: Streaming Data

def stream_data():
    yield "data1"
    yield "data2"
    yield "data3"

for data in stream_data():
    print(data)

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Note

Generators (using yield) are a special type of iterator.

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Infinite Iterator Example

class Count:
    def __init__(self):
        self.num = 1

    def __iter__(self):
        return self

    def __next__(self):
        val = self.num
        self.num += 1
        return val

counter = Count()

print(next(counter))
print(next(counter))

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Caution

Infinite iterators must be handled carefully to avoid endless loops.

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When to Use Iterators

✔ Large data processing
✔ File reading
✔ Streaming APIs
✔ Infinite sequences
✔ Memory-efficient loops

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Advantages of Iterators

• Memory efficient
• Faster processing for large data
• Lazy evaluation
• Clean code structure

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Common Mistakes

1. Forgetting StopIteration

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2. Reusing exhausted iterator

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3. Confusing iterable and iterator

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Best Practices

1. Use iter() when needed

it = iter(data)

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2. Prefer for-loops for simplicity

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3. Use generators for large data

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Summary

Iterators in Python allow you to traverse through data one element at a time efficiently. They are the backbone of Python loops and help manage memory effectively.

Key Takeaways

• Iterator returns items one by one
• Uses __iter__() and __next__()
• Built into Python for loops
• Saves memory
• Useful for large datasets and streaming

Understanding iterators is essential for mastering Python’s data handling and performance optimization.