要實(shí)現(xiàn)自定義迭代器，需在類中定義__iter__和__next__方法。 ① __iter__方法返回迭代器對象自身，通常為self，以兼容for循環(huán)等迭代環(huán)境；② __next__方法控制每次迭代的值，返回序列中的下一個(gè)元素，當(dāng)無更多項(xiàng)時(shí)應(yīng)拋出StopIteration異常；③ 需正確跟蹤狀態(tài)並設(shè)置終止條件，避免無限循環(huán)；④ 可封裝複雜邏輯如文件行過濾，同時(shí)注意資源清理與內(nèi)存管理；⑤ 對簡單邏輯可考慮使用生成器函數(shù)yield替代，但需結(jié)合具體場景選擇合適方式。

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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