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Python Iterators (Iterator Usage)

In Python, an iterator is an object that allows you to traverse through all elements of a collection (like lists, tuples, or dictionaries) one by one, without exposing the underlying structure. Iterators are implemented using the __iter__() and __next__() methods.

Creating and Using an Iterator

# A simple list
my_list = [10, 20, 30]

# Get an iterator from the list
it = iter(my_list)

# Access elements using next()
print(next(it))  # Output: 10
print(next(it))  # Output: 20
print(next(it))  # Output: 30
# next(it) would raise StopIteration if called again
  

Explanation:

• iter(my_list): Returns an iterator object for the list.
• next(it): Returns the next element from the iterator.
• StopIteration: Raised when there are no more elements to iterate.

Using Iterators in a Loop

my_list = [1, 2, 3, 4, 5]

# For loop automatically handles the iterator
for item in my_list:
    print(item)
# Output: 1 2 3 4 5
  

Explanation:

• A for loop internally creates an iterator and calls next() until StopIteration is raised.
• This is the most common way to use iterators in Python.

Creating a Custom Iterator

# Custom iterator class
class MyNumbers:
    def __init__(self, max):
        self.max = max
        self.num = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.num < self.max:
            current = self.num
            self.num += 1
            return current
        else:
            raise StopIteration

# Using the custom iterator
numbers = MyNumbers(5)
for n in numbers:
    print(n)
# Output: 0 1 2 3 4
  

Key Points

• Iterator: An object with __iter__() and __next__() methods.
• iter(): Used to get an iterator from an iterable.
• next(): Used to get the next element from an iterator.
• StopIteration: Signals that iteration is complete.
• Custom iterators allow fine-grained control over how elements are generated or returned.

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Using iterators in Python enables efficient traversal of data collections and supports lazy evaluation, which is particularly useful for large datasets.