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Python Iterators and Iterables

Video link: https://youtu.be/C_rhipZonok

In this video, we learned about iterables and iterators in Python with the help of examples.

Programs in the Video

Python Iterables

Anything that you can loop over in Python is called an iterable. For example, a list is an iterable. For an object to be considered an iterable, it must have the __iter()__ method.

numbers = [1, 4, 9]
print(dir(numbers))

Output

['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__',
'__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__',
'__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__',
'__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend',
'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

We can see that there is a special __iter__() method among all these methods. Let's call this method.

numbers = [1, 4, 9]

value = numbers.__iter__()
print(value)

Output

<list_iterator object at 0x7fa223878d00>

The iter method returns an iterator object.

Python Iterators

Iterator in Python is simply an object that can return data one at a time while iterating over it.

For an object to be an iterator, it must implement two methods:

  • __iter__()
  • __next__()

These are collectively called the iterator protocol.

The next() method

The __next__() method returns the next value in the iteration.

numbers = [1, 4, 9]
value = numbers.__iter__()

item1 = value.__next__()
print(item1)

Output

1

Now, if we run the next method again, it should return the next item which is 4. It's because the next method also updates the state of the iterator.

numbers = [1, 4, 9]
value = numbers.__iter__()

item1 = value.__next__()
print(item1)

item2 = value.__next__()
print(item2)

item3 = value.__next__()
print(item3)

Output

1
4
9

Note: Instead of calling these special methods with an underscore, Python has an elegant way to call __iter__() simply with the iter() function and __next__() with the next() function.

Here, we have already reached the end of our list. Now, let's see what happens if we further try to get the next value.

numbers = [1, 4, 9]
value = iter(numbers)

item1 = next(value)
print(item1)

item2 = next(value)
print(item2)

item3 = next(value)
print(item3)

item4 = next(value)
print(item4)

Output

1
4
9
Traceback (most recent call last):
  File "<string>", line 13, in <module>
StopIteration

Since our list had only 3 elements, the call to the fourth next() method raised the StopIteration exception.

Working of for loops

Did you know that for loops internally use the while loop to iterate through sequences?

num_list = [1, 4, 9]

iter_obj = iter(num_list)

while True:
    try:
        element = next(iter_obj)
        print(element)
    except StopIteration:
        break

Output

1
4
9

Here's how this code works:

  • First, we have created an iterator object from a list using iter_obj = iter(num_list)
  • Then, we have created an infinite while loop.
  • Inside the loop, we have used the next method to return the next element in the sequence element = next(iter_obj)) and printed it. We have put this code inside the try block.
  • When all the items of the iterator are iterated, the try block raises the StopIteration exception, and the except block catches it and breaks the loop.

In fact, this is exactly how for loops work behind the scene. A for loop internally creates an iterator object, and iterates over it calling the next method until a StopIteration exception is encountered.

The above code is equivalent to:

num_list = [1, 4, 9]

for element in num_list:
    print(element)

Creating Custom Iterators

Let's try to make our own iterator object to generate a sequence of even numbers such as 2, 4, 6, 8 and so on.

class Even:
    def __init__(self, max):
        self.n = 2
        self.max = max

    def __iter__(self):
        return self

    def __next__(self):
        if self.n <= self.max:
            result = self.n
            self.n += 2
            return result
        else:
            raise StopIteration

numbers = Even(10)

print(next(numbers))
print(next(numbers))
print(next(numbers))

Output

2
4
6

Iterators are powerful tools when dealing with a large stream of data. If we used regular lists to store these values, our computer would run out of memory. With iterators, however, we can save resources as they return only one element at a time. So, in theory, we can deal with infinite data in finite memory.