The difference is that it saves the state of the function. It works a bit like return because it returns a value. Generators introduce the yield statement to Python. Recall that generator functions allow us to create iterators in a more simple fashion.

Now that we know what iterators are and how to make one, we’ll move on to generators. If you try to iterate over primes again, no value will be returned. Iterators can only be iterated over once. Let’s try it out: primes = Primes(100000000000) print(primes) for x in primes: print(x) - 235711.Įvery iteration of the Primes object calls _next_ to generate the next prime number. Instead, we’re generating the next prime number every time we request for it. Once it is, the iterator returns the number.īy using an iterator, we’re not creating a list of prime numbers in our memory. If it’s not, it will call _next_ again until number is prime. When we request the next element in the iterator, it will increment number by 1 and check if it’s a prime number. If the next prime is greater or equal than the max, the iterator will raise a StopIteration exception, which ends the iterator. Primes is instantiated with a maximum value. Then, we define the iterator class that will include the _iter_ and _next_ methods: class Primes: def _init_(self, max): self.max = max self.number = 1 def _iter_(self): return self def _next_(self): self.number += 1 if self.number >= self.max: raise StopIteration elif check_prime(self.number): return self.number else: return self._next_() We first define the function that checks if a number is prime: def check_prime(number): for divisor in range(2, int(number ** 0.5) + 1): if number % divisor = 0: return False return True Let’s say we want to get all the prime numbers that are smaller than a maximum number. It allows you to start using the data immediately, while the whole data set is being computed. Lazy evaluation is useful when you have a very large data set to compute. They only compute it when you ask for it. Iterators don’t compute the value of each item when instantiated. Why would you even want to make iterators? Saving memory space This protocol looks for two methods within the class: _iter_ and _next_. You probably already use a few iterable objects every day: strings, lists, and dictionaries to name a few.Īn iterator is defined by a class that implements the Iterator Protocol.

It is used to abstract a container of data to make it behave like an iterable object. They allow programmers to make an iterator in a fast, easy, and clean way.Īn iterator is an object that can be iterated (looped) upon. Generator functions allow you to declare a function that behaves like an iterator. Generators have been an important part of Python ever since they were introduced with PEP 255. By Radu Raicea How - and why - you should use Python Generators Image Credit: Beat Health Recruitment

Python generator example