Welcome to your first lesson on iterators in Python! In this lesson, you will learn what an iterator is, why iterators are useful, and how to create and use them in Python. By the end, you'll understand how to use iterators to make your code efficient and easy to read.

An iterator is an object that allows you to traverse through a collection of elements, like a list or tuple. Think of an iterator like a sales clerk with an inventory list in a store, going item by item until every item is checked.

In Python, an iterator implements two methods: __iter__() and __next__(). The __iter__() method returns the iterator object itself, and the __next__() method returns the next element in the collection.

Iterators take responsibility for two main actions:

1. Returning the data from a stream or container one item at a time.
2. Keeping track of the current and visited items.

To create an iterator, you need a class that implements __iter__() and __next__(). Let’s look at a Counter class to understand this better:

Python
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1class Counter:
2    def __init__(self, low, high):
3        self.current = low
4        self.high = high
5
6    def __iter__(self):
7        return self

• __init__() initializes the counter with low and high.
  • self.current is initialized to low and will keep track of the current value.
  • self.high is set to the upper bound of the counter.
• __iter__() returns the iterator object itself, which allows the Counter instance to be used directly in a for loop or with other iterator contexts.

Continuing with the Counter class:

Python
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1class Counter:
2    def __init__(self, low, high):
3        self.current = low
4        self.high = high
5
6    def __iter__(self):
7        return self
8
9    def __next__(self):
10        if self.current > self.high:
11            raise StopIteration
12        else:
13            self.current += 1
14            return self.current - 1

• __next__() is responsible for returning the next value in the iteration:
  • It first checks if self.current has surpassed self.high. If so, it raises a StopIteration exception, signaling that the iteration is complete.
  • Otherwise, it increments self.current by 1 and then returns the previous value (i.e., self.current - 1).

Short Reminder on Exceptions: Exceptions in Python are events that can alter the flow of a program. They occur when an error arises, and they can be caught and handled using try...except blocks. The StopIteration exception is a specific type of exception used to signal the end of an iteration.

To use an iterator, you typically use a for loop. The loop calls __iter__() and __next__() behind the scenes. Here’s how to use our Counter class:

Python
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1class Counter:
2    def __init__(self, low, high):
3        self.current = low
4        self.high = high
5
6    def __iter__(self):
7        return self
8
9    def __next__(self):
10        if self.current > self.high:
11            raise StopIteration
12        else:
13            self.current += 1
14            return self.current - 1
15
16def main():
17    counter = Counter(1, 5)
18
19    for num in counter:
20        print(num)  # 1, 2, 3, 4, 5
21
22if __name__ == "__main__":
23    main()

The for loop calls __next__() until it catches the StopIteration exception.

Python provides several built-in iterators. Common examples include:

• Lists, Tuples, and Strings: When you use a for loop to traverse a list, tuple, or string, Python internally uses an iterator to iterate over the elements.
• Dictionaries: Iterating over a dictionary yields its keys, but you can also iterate over its values or items by using methods like .values() or .items().
• Files: When reading lines of a file using a for loop or methods like .readlines(), Python employs an iterator to go through each line sequentially.

In this lesson, you learned what an iterator is, and how to create and use one in Python. We used a Counter class to understand __iter__() and __next__(). By creating a custom iterator, you control the logic for traversing any collection of elements. We also touched upon built-in iterators and how to handle exceptions in Python.

Now that you have learned the basics of iterators, it's time to practice. In the exercises, you will create your own iterators and use them to traverse and process data collections. Let's get started!