iterator

• iterators are objects that allow you to iterate through a collection of elements one at a time.
• They provide a way to efficiently access elements in a sequence without loading the entire collection into memory at once.
• This is particularly useful for working with large datasets or when you only need to process elements one by one.
• Iterable vs. Iterator
  • An iterable is an object that can be used to create an iterator.
  • It defines a protocol for iterating, but it's not the iterator itself.
  • Common iterables in Python include lists, tuples, strings, dictionaries (for keys), and sets.
  • An iterator, on the other hand, is an object that implements the iterator protocol.
  • It keeps track of the current position within the sequence and provides a way to access the next element.
• The Iterator Protocol
  • An iterator must implement two special methods
    • __iter__(self): This method returns the iterator object itself. It's called when you use the iter() function on an iterable.
    • __next__(self): This method raises a StopIteration exception when there are no more elements to iterate over. Otherwise, it returns the next element in the sequence.
• You can obtain an iterator from an iterable using the built-in iter() function

numbers = [1, 2, 3, 4, 5]
number_iterator = iter(numbers)

numbers = [1, 2, 3, 4, 5] number_iterator = iter(numbers)

• Using Iterators in for Loops
  • The for loop in Python automatically uses the iterator protocol behind the scenes.
  • When you use a for loop with an iterable, Python internally creates an iterator object, calls __next__ repeatedly to get the next element, and assigns it to the loop variable until StopIteration is raised.

for number in numbers:
    print(number)  # This will print each number on a new line

for number in numbers: print(number) # This will print each number on a new line

• Custom Iterators
  • You can create your own custom iterators by defining a class that implements the iterator protocol (i.e., defines the __iter__ and __next__ methods).
  • This allows you to create iterators for custom data structures or generate sequences on demand.
• Benefits of Using Iterators
  • Memory Efficiency: Iterators process elements one at a time, reducing memory usage compared to loading the entire collection at once. This is especially beneficial for large datasets.
  • Lazy Evaluation: Some iterators, like those created with generator expressions, can be even more memory-efficient as they generate elements only when needed.
  • Flexibility: Iterators provide a generic way to work with different data structures that support iteration.
• Common Use Cases
  • Iterating over elements in lists, tuples, and strings.
  • Processing elements in files line by line.
  • Creating custom sequences using generator expressions or generator functions.