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This article to share with you is about how to use the generator in Python, Xiaobian feel quite practical, so share to everyone to learn, I hope you can read this article after some harvest, not much to say, follow Xiaobian to see it.

Generators have been an important part of Python since the introduction of PEP 255.

Generator functions allow you to declare a function that behaves like an iterator.

They allow programmers to create iterators in a quick, simple, and clean way.

You might ask, what is an iterator?

An iterator is an object that iterates (loops). It is used to abstract data containers and make them behave like iterable objects. You probably already use iterable objects on a daily basis: strings, lists, dictionaries, and so on.

Iterators are defined by classes that implement the iterator protocol. The protocol looks for two methods in the class: __iter__and__next__.

Why do you want to make iterators?

Save memory space

Iterators do not compute the value of each item when instantiated. They only calculate it when you ask. This is called inertia assessment.

Latency evaluation is useful when you have a very large data set to compute. It allows you to start working with data immediately as you compute an entire dataset.

Suppose we want to get all prime numbers less than the maximum.

We first define a function that checks whether a number is prime:

Then, we define iterator classes that will contain__iter__and__next__methods:

Primes are instantiated at maximum. If the next prime number is greater than or equal to max, the iterator throws a StopIteration exception, ending the iterator.

When we request the next element in the iterator, it increments the number by 1 and checks if it is a prime. If not, it calls__next__again until the number is prime. Once it is, the iterator returns a number.

By using iterators, we do not create a list of primes in memory. Instead, we generate the next prime number every time we request it.

Let's try it:

Each iteration of the Primes object calls__next__to generate the next prime.

An iterator can only iterate once. If you try to traverse the prime number again, nothing is returned. It will behave like an empty list.

Now that we know what iterators are and how to make them, we'll move on to generators.

generator

Recall that generator functions allow us to create iterators in a simpler way.

Generators introduce yield statements into Python. It's kind of like return because it returns a value.

The difference is that it saves the state of the function. The next time the function is called, execution continues from where it left off, with the same variable values as before generation.

If we convert the Primes iterator to a generator, it will look like this:

Now this is pretty pythonic! Can we do better?

Yes! We can use Generator Expressions introduced in PEP 289.

This is the list understanding equivalent of the generator. It works exactly the same way as list derivation, but the expression is surrounded by () instead of [].

The following expression can replace the generator function above:

This is the beauty of generators in Python.

To sum up...

Generators allow you to create iterators in a very pythonic way.

Iterators allow delayed evaluation, generating the next element of an iterable object only on request. This is useful for very large data sets.

Iterators and generators can only iterate once.

Generator functions are better than iterators.

The above is how to use generators in Python, Xiaobian believes that some of the knowledge points may be what we see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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