🔁 Python Iterators — Behind the Scenes of Loops

Introduction 🌟

An iterator in Python is an object that allows you to traverse through elements of a collection (like lists, tuples, strings) **one item at a time**. Iterators power loops like for and make lazy evaluation possible.

Note

✔ Every iterator must implement two methods:
__iter__() → returns the iterator object itself
__next__() → returns the next value

1. What Is an Iterable? 🧩

An iterable is any object in Python that can return its elements one by one. Examples: list, tuple, set, string, dictionary, generator, file object.

iterable_examples.py

nums = [1, 2, 3]
text = "hello"

print(iter(nums))   # iterable
print(iter(text))

2. Getting an Iterator from an Iterable 🔄

get_iterator.py

nums = [10, 20, 30]

it = iter(nums)

print(next(it))   # 10
print(next(it))   # 20
print(next(it))   # 30
# next(it) → StopIteration

Note

✔ A for-loop internally uses iter() and next().

3. How a for-loop Actually Works 🧠

for_loop_internal.py

nums = [1, 2, 3]

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

✔ This is how Python internally executes a for loop.

4. Creating Your Own Iterator Class 🏗️

custom_iterator.py

class Counter:
    def __init__(self, limit):
        self.limit = limit
        self.current = 1

    def __iter__(self):
        return self   # iterator object

    def __next__(self):
        if self.current <= self.limit:
            value = self.current
            self.current += 1
            return value
        else:
            raise StopIteration

c = Counter(5)
for i in c:
    print(i)

Note

✔ Custom iterators give full control over iteration behavior.

5. Iterator vs Iterable ⚔️

IterableIterator
Has __iter__()Has __iter__() & __next__()
Returns iteratorReturns next item
Examples: list, tupleExamples: file, generator

6. Iterators with Strings 🔤

string_iterator.py

text = "ABC"
it = iter(text)

print(next(it))  # A
print(next(it))  # B
print(next(it))  # C

7. Iterators with Dictionaries 🔑

dict_iterator.py

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

for key in person:
    print(key, person[key])

8. Infinite Iterators (itertools) ♾️

infinite_iterator.py

import itertools

counter = itertools.count(1)

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

Note

⚠️ Infinite iterators must be controlled carefully.

9. Building a Fibonacci Iterator 🌀

fibonacci_iterator.py

class Fibonacci:
    def __init__(self, max_limit):
        self.max = max_limit
        self.a = 0
        self.b = 1

    def __iter__(self):
        return self

    def __next__(self):
        if self.a > self.max:
            raise StopIteration
        value = self.a
        self.a, self.b = self.b, self.a + self.b
        return value

for num in Fibonacci(50):
    print(num)

10. Iterator for Reversing a List 🔁

reverse_iterator.py

class Reverse:
    def __init__(self, items):
        self.items = items
        self.index = len(items)

    def __iter__(self):
        return self

    def __next__(self):
        if self.index == 0:
            raise StopIteration
        self.index -= 1
        return self.items[self.index]

for x in Reverse([1, 2, 3, 4]):
    print(x)

11. Generators vs Iterators ⚡

GeneratorsIterators
Created with yieldMust define __next__()
Simpler syntaxMore control
Automatically memory-efficientDepends on implementation

12. Real-World Uses 🌍

Reading large files efficiently

file_iterator.py

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

Processing database results

db_iterator.py

# pseudo-code
cursor = db.execute("SELECT * FROM users")

for row in cursor:  # cursor is an iterator
    print(row)

Streaming API data

api_iterator.py

# pseudo-code
def api_stream():
    while True:
        yield fetch_data()

Conclusion 🎉

>>“Iterators are the engine behind Python loops — powerful, efficient, and elegant.” ✨

You now fully understand Iterators in Python! Want the next topic? Try Decorators, Modules, OOP, or Error Handling. Just tell me! 😊

🔧 Python __iter__ & __next__ — Building Custom Iterators

Introduction 🌟

In Python, iterators are objects that allow iteration over data one element at a time. To create a custom iterator, you must implement two special methods:

  • __iter__(self) → returns the iterator object
  • __next__(self) → returns the next value (or raises StopIteration)

Note

💡 These two methods allow your objects to work with for loops, next(), generators, and all iteration tools.

1. What is __iter__()? 🔄

The __iter__() method returns the iterator object itself. It is called automatically when iteration begins.

iter_method.py

def __iter__(self):
    return self

2. What is __next__()? ▶️

The __next__() method returns the next value in the sequence. When the sequence is finished, it must raise StopIteration.

next_method.py

def __next__(self):
    if no_more_items:
        raise StopIteration
    return next_item

Note

✔️ A for loop will keep calling __next__() until StopIteration is raised.

3. Creating a Simple Custom Iterator 🧱

simple_iterator.py

class Counter:
    def __init__(self, limit):
        self.limit = limit
        self.current = 1

    def __iter__(self):
        return self

    def __next__(self):
        if self.current <= self.limit:
            value = self.current
            self.current += 1
            return value
        else:
            raise StopIteration

counter = Counter(5)
for num in counter:
    print(num)

✔️ Prints: 1 2 3 4 5

4. Manually Using iter() and next() 🎮

manual_iter_next.py

numbers = Counter(3)

it = iter(numbers)
print(next(it))  # 1
print(next(it))  # 2
print(next(it))  # 3
# next(it) → StopIteration

5. Iterator for a Custom Range Function 🔢

range_iterator.py

class MyRange:
    def __init__(self, start, end):
        self.current = start
        self.end = end

    def __iter__(self):
        return self

    def __next__(self):
        if self.current <= self.end:
            value = self.current
            self.current += 1
            return value
        else:
            raise StopIteration

for x in MyRange(1, 5):
    print(x)

6. Reverse Iterator 🔁

reverse_iterator.py

class Reverse:
    def __init__(self, items):
        self.items = items
        self.index = len(items)

    def __iter__(self):
        return self

    def __next__(self):
        if self.index == 0:
            raise StopIteration
        self.index -= 1
        return self.items[self.index]

for item in Reverse([10, 20, 30]):
    print(item)

7. Iterator for Fibonacci Sequence 🌀

fibonacci_iterator.py

class Fibonacci:
    def __init__(self, max_limit):
        self.a = 0
        self.b = 1
        self.max = max_limit

    def __iter__(self):
        return self

    def __next__(self):
        if self.a > self.max:
            raise StopIteration
        value = self.a
        self.a, self.b = self.b, self.a + self.b
        return value

for n in Fibonacci(50):
    print(n)

8. Iterator Inside a Class (Multiple Iterators) 🧩

multiple_iterators.py

class MyList:
    def __init__(self, data):
        self.data = data

    def __iter__(self):
        return iter(self.data)  # delegates to built-in iterator

nums = MyList([1, 2, 3, 4])
for n in nums:
    print(n)

Note

✔ This approach avoids writing __next__() manually.

9. Important Rules for Iterators ⚠️

  • __iter__() must return the iterator object.
  • __next__() must return the next value or raise StopIteration.
  • Iterators maintain internal state.
  • Iterators are exhausted once completed (you must create a new one).

10. Real-World Use Cases 🌍

Iterating over database rows

db_iterator.py

# Cursor returned from DB is an iterator
for row in cursor:
    print(row)

Reading large file line-by-line

file_iterator.py

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

Streaming API data

stream_iterator.py

def stream():
    while True:
        yield fetch_data()  # generator → iterator

Conclusion 🎉

>>“__iter__ and __next__ give you total control over iteration — the foundation of loops, generators, and lazy evaluation.” ✨

You now fully understand __iter__ and __next__ in Python! Want the next topic? Try Decorators, Modules, Classes & Objects (OOP), or Error Handling. Just tell me! 😊