COMP1531

🔨 10.1 - Python - Iterators & Generators

In this lecture

Why?​

  • Understand the concepts of iterators and iterables
  • Create iterator classes
  • Write simple generator functions
  • Understand iterator invalidation
  • Understand how python abstractions are implemented at a (slightly) lower level

 

How does a for loop actually work?

shopping_list = ['apple', 'banana', 'pineapple', 'orange']

for item in shopping_list:
    print(item)

First attempt: C-style

shopping_list = ['apple', 'banana', 'pineapple', 'orange']

for i in range(len(shopping_list)):
    print(shopping_list[i])

What if we don't know the length?

from itertools import cycle

my_cycle = cycle([1, 2, 3])

for i in my_cycle:
    print(i)
  • An iterator is an object that enables a programmer to traverse a container
  • Allows us to access the contents of a data structure while abstracting away its underlying representation
  • In python, for loops are an abstraction of iterators
  • Iterators can tell us:
    • Do we have any elements left?
    • What is the next element?

Iterators

Let's rewrite our for-loop using an iterator

Iterators vs Iterables

  • An iterable is an object that can be iterated over
  • All iterators are iterable, but not all iterables are iterators
  • For loops only need to be given something iterable
  • Concretely:
    • ​An iterator has an __iter__() and __next__() methods
    • An iterable has an __iter__() method
  • The __iter__() method
    • ​​Returns an object of type iterator
  • The __next__() method
    • ​Returns the next element in iteration
    • Raises a StopIteration if there are no elements left

A Custom Iterator: Square Numbers

Generators

  • A functional way of writing iterators
  • Defined via generator functions instead of classes
  • Example generator
def shopping_list():
    print(1)
    yield 'apple'
    print(2)
    yield 'orange'
    print(3)
    yield 'banana'
    print(4)
    yield 'pineapple'

for item in shopping_list():
    print(item)

Generators

  • Intuitively, you can think of a generator as a suspendable computation
  • Calling next() on a generator executes it until it reaches a yield, at which point it is suspended (frozen) until the subsequent call to next()

Generators

  • More useful examples
def squares():
    i = 0
    while True:
        i += 1
        yield i * i

Implementing cycle

https://docs.python.org/3/library/itertools.html#itertools.cycle

Generator Syntactic Sugar

  • yield from
  • Generator comprehensions
  • Wrapping up a generator

Iterator Invalidation

  • What happens when we modify something we're iterating over?
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]

for number in numbers:
    if number == 3 or number == 4:
        numbers.remove(number)

print(numbers)

Iterator Use Cases

  • Most data structures provide in-built iterators
  • Traversing non-linear data structures
  • Example: BFS and DFS traversal of a graph

More interesting python topics

  • https://python-course.eu

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COMP1531 21T3 - 10.1 - Python - Iterators & Generators

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COMP1531 21T3 - 10.1 - Python - Iterators & Generators

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