Custom Iterator

Implement a class that can be iterated over using __iter__ and __next__.

IntermediateTopic: Object-Oriented Programs
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Python Custom Iterator Program

This program helps you to learn the fundamental structure and syntax of Python programming.

Try This Code
# Program to implement a custom iterator

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

    def __iter__(self):
        return self

    def __next__(self):
        if self.current <= 0:
            raise StopIteration
        value = self.current
        self.current -= 1
        return value


for num in Countdown(3):
    print(num)
Output
3
2
1

Understanding Custom Iterator

By defining __iter__ and __next__, Countdown becomes an iterator usable in for-loops.

Note: To write and run Python programs, you need to set up the local environment on your computer. Refer to the complete article Setting up Python Development Environment. If you do not want to set up the local environment on your computer, you can also use online IDE to write and run your Python programs.

Practical Learning Notes for Custom Iterator

This Python program is part of the "Object-Oriented Programs" topic and is designed to help you build real problem-solving confidence, not just memorize syntax. Start by understanding the goal of the program in plain language, then trace the logic line by line with a custom input of your own. Once you can predict the output before running the code, your understanding becomes much stronger.

A reliable practice pattern is to run the original version first, then modify only one condition or variable at a time. Observe how that single change affects control flow and output. This deliberate style helps you understand loops, conditions, and data movement much faster than copying full solutions repeatedly.

For interview preparation, explain this solution in three layers: the high-level approach, the step-by-step execution, and the time-space tradeoff. If you can teach these three layers clearly, you are ready to solve close variations of this problem under time pressure.

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