Can Generator Be Used to Create Iterators ?
by Radu Raicea
How — and why — you lot should use Python Generators
Generators accept been an of import part of Python ever since they were introduced with PEP 255.
Generator functions let you lot to declare a role that behaves like an iterator.
They allow programmers to make an iterator in a fast, easy, and make clean fashion.
What's an iterator, you may inquire?
An iterator is an object that can be iterated (looped) upon. It is used to abstract a container of data to arrive behave similar an iterable object. Y'all probably already utilize a few iterable objects every mean solar day: strings, lists, and dictionaries to proper name a few.
An iterator is defined by a class that implements the Iterator Protocol. This protocol looks for 2 methods within the class: __iter__ and __next__.
Whoa, pace dorsum. Why would you even want to brand iterators?
Saving memory infinite
Iterators don't compute the value of each item when instantiated. They just compute it when yous ask for it. This is known every bit lazy evaluation.
Lazy evaluation is useful when you take a very large data prepare to compute. It allows yous to start using the information immediately, while the whole information fix is being computed.
Permit's say we want to get all the prime number numbers that are smaller than a maximum number.
We first ascertain the function that checks if a number is prime:
def check_prime(number): for divisor in range(two, int(number ** 0.5) + 1): if number % divisor == 0: return False render True And then, nosotros ascertain the iterator class that will include the __iter__ and __next__ methods:
grade Primes: def __init__(self, max): self.max = max self.number = 1 def __iter__(self): render self def __next__(cocky): self.number += 1 if self.number >= self.max: heighten StopIteration elif check_prime(self.number): return self.number else: render self.__next__() Primes is instantiated with a maximum value. If the next prime is greater or equal than the max, the iterator will raise a StopIteration exception, which ends the iterator.
When we asking the next element in the iterator, information technology will increment number by 1 and check if it's a prime number. If it'south non, information technology will phone call __next__ again until number is prime number. Once it is, the iterator returns the number.
By using an iterator, nosotros're not creating a list of prime numbers in our memory. Instead, we're generating the next prime number every time we request for it.
Allow'due south endeavour it out:
primes = Primes(100000000000) print(primes) for x in primes: print(x) --------- <__main__.Primes object at 0x1021834a8>235711... Every iteration of the Primes object calls __next__ to generate the next prime number.
Iterators tin can only be iterated over once. If you try to iterate over primes once again, no value will be returned. Information technology will bear like an empty list.
Now that nosotros know what iterators are and how to make one, we'll move on to generators.
Generators
Retrieve that generator functions allow us to create iterators in a more elementary way.
Generators introduce the yield statement to Python. Information technology works a scrap like render considering information technology returns a value.
The difference is that it saves the state of the function. The next time the office is called, execution continues from where it left off, with the same variable values it had earlier yielding.
If we transform our Primes iterator into a generator, it'll await like this:
def Primes(max): number = 1 while number < max: number += one if check_prime(number): yield number primes = Primes(100000000000) print(primes) for 10 in primes: print(x) --------- <generator object Primes at 0x10214de08>235711... Now that'south pretty pythonic! Tin we do amend?
Yep! Nosotros can employ Generator Expressions, introduced with PEP 289.
This is the list comprehension equivalent of generators. It works exactly in the aforementioned way as a listing comprehension, simply the expression is surrounded with () as opposed to [].
The following expression can replace our generator part in a higher place:
primes = (i for i in range(ii, 100000000000) if check_prime(i)) impress(primes) for 10 in primes: print(ten) --------- <generator object <genexpr> at 0x101868e08>235711... This is the beauty of generators in Python.
In summary…
- Generators allow y'all to create iterators in a very pythonic style.
- Iterators allow lazy evaluation, only generating the next chemical element of an iterable object when requested. This is useful for very large data sets.
- Iterators and generators can only be iterated over once.
- Generator Functions are amend than Iterators.
- Generator Expressions are better than Iterators (for simple cases only).
You can also cheque out my explanation of how I used Python to find interesting people to follow on Medium.
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Source: https://www.freecodecamp.org/news/how-and-why-you-should-use-python-generators-f6fb56650888/
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