To implement a custom iterator, you need to define the __iter__ and __next__ methods in the class. ① The __iter__ method returns the iterator object itself, usually self, to be compatible with iterative environments such as for loops; ② The __next__ method controls the value of each iteration, returns the next element in the sequence, and when there are no more items, StopIteration exception should be thrown; ③ The status must be tracked correctly and the termination conditions must be set to avoid infinite loops; ④ Complex logic such as file line filtering can be encapsulated, and resource cleaning and memory management can be paid attention to; ⑤ For simple logic, you can consider using the generator function yield instead, but you need to choose a suitable method based on the specific scenario.

To implement custom iterators in Python, you need to define both __iter__ and __next__ methods in your class. These two special methods allow your object to be iterable and control how the iteration behaves step by step.

Understanding __iter__ and __next__

The __iter__ method should return the iterator object itself — usually self . This is what makes your object compatible with for-loops and other iteration contexts.

The __next__ method defines what happens each time the next item is requested. It should return the next value in the sequence or raise StopIteration when there are no more items to return.

If you don't raise StopIteration at the end of your sequence, your iterator will keep running indefinitely, which can cause problems like infinite loops.

Creating a Simple Custom Iterator

Let's say you want to create an iterator that goes through a range of numbers but skips every second number.

 class SkipEvenIterator:
    def __init__(self, max_value):
        self.current = 0
        self.max_value = max_value

    def __iter__(self):
        Return self

    def __next__(self):
        if self.current > self.max_value:
            raise StopIteration
        result = self.current
        self.current = 2
        return result

Now you can use this in a loop:

 for num in SkipEvenIterator(10):
    print(num)

This would output: 0, 2, 4, 6, 8, 10.

A few things to remember:

• Your __next__ method must track state correctly.
• Always include a stopping condition to avoid infinite loops.
• You can store any kind of state inside your object — integers, strings, even other objects.

Handling More Complex Iteration Logic

Sometimes you might not just want to iterate over numbers. For example, imagine iterating over lines in a file that match a certain pattern.

In these cases, your __iter__ could open a file or prepare a data source, and __next__ processes it line by line or item by item.

Here's a simplified version:

 class GrepLikeIterator:
    def __init__(self, filename, keyword):
        self.filename = filename
        self.keyword = keyword
        self.file = None
        self.line = None

    def __iter__(self):
        self.file = open(self.filename, 'r')
        Return self

    def __next__(self):
        While True:
            line = self.file.readline()
            if not line:
                self.file.close()
                raise StopIteration
            if self.keyword in line:
                return line.strip()

This lets you do something like:

 for line in GrepLikeIterator('data.txt', 'error'):
    print(line)

Just make sure:

• You properly handle resource cleanup (like closing files).
• Avoid loading large datasets into memory all at once.
• Make sure your logic doesn't accidentally skip values ??or repeat them unintentionally.

When to Use Generators Instead

While implementing __iter__ and __next__ gives you full control, sometimes using a generator function with yield is simpler and cleaner. If your iteration logic isn't too complex, consider writing a generator instead.

For example:

 def skip_even_generator(max_value):
    current = 0
    While current <= max_value:
        yield current
        current = 2

You can still use this in a for-loop, and Python handles the state automatically.

But if you need to encapsulate state and behavior together — especially when combining with other object-oriented features — defining a custom iterator class is the right approach.

So yeah, implementing custom iterators in Python means writing classes with __iter__ and __next__ , handling state yourself, and making sure to stop cleanly. Not too hard once you get the hang of it, but definitely easy to mess up small details like forgetting to raise StopIteration .

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