Summary

Generators in Python are a powerful tool for creating iterators efficiently, providing a convenient way to produce items without the need to store them all in memory, thus saving time and space.

Abstract

The article "Generators in Python" delves into the concept of generators as a means to produce iterators in Python. It emphasizes the simplicity of creating generators compared to manually implementing iterators using __iter__() and __next__() methods. Generators are described as a subclass of iterators, which can be easily created using generator expressions or by defining a function with the yield statement. The article highlights the benefits of using generators for handling large datasets or infinite data streams, as they produce items on-demand rather than all at once, leading to significant reductions in memory usage and execution time. The author also illustrates how multiple generators can be combined to form pipelines, enhancing their versatility and applicability in various scenarios.

Opinions

The author believes that understanding iterators and iterables is crucial for grasping the concept of generators, suggesting readers to familiarize themselves with these concepts through a linked article.
Generators are portrayed as superior to traditional methods of creating iterators due to their ease of use and efficiency.
The author points out that the yield statement in a function is a defining feature of generator functions, which allows them to pause and resume execution, unlike normal functions.
It is noted that generators are particularly useful for handling large datasets or representing infinite data streams, as they can produce items one at a time without exhausting memory resources.
The article suggests that the ability to chain or combine generators into pipelines is a powerful feature that adds to the flexibility of using generators in Python.
The author encourages readers to explore more Python tutorials and topics by following their publication, indicating a commitment to providing educational content in programming and technology.

Generators in Python

Introduction

The generator is a convenient and powerful tool in Python to produce Iterators. This post will explain and dive into generators in Python by some examples.

If you haven’t totally understood Itreators yet, no worries, please read this post:

Iterable and Iterator in Python

Dive into these two important concepts by examples

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Why Python Has Generator?

We can get an Iterator by implementing the __iter__() and __next__() special methods in a Python class. However, this way is a little complex though it’s helpful to understand how the Iterators really work. Creating Iterators by generators is a much better and convenient way. In fact, the Generator is a subclass of the Iterator. The relationships of Iterable, Iterator and Generator are as following:

Relationships of Python Iterable, Iterator and Generator

As above figure shown, Iterator is a subclass of Iterable and Generator is a subclass of Iterator.

An Generator, having the same ambition with an Iterator, is used to save a method which knows how to produce required elements. Operating a large list in Python is very time consuming. If we just need to get one element every time, generator is a very good choice to reduce both time and space costs.

How to Get a Generator?

1. Generator Expression

The generator expression is the simplest way to get a generator. It’s very similar with list comprehensions. We just need to change the brackets to parentheses.

Since a generator saves item producing method rather than items, we use next() function to get items one by one, which is the same as an Iterator. When all items were produced, the next() function will raise a StopIteration error information. Of course, we can also use a for loop to get items in a generator.

2. Use yield to Define a Generator

If a function includes the yield statement, it can produce generators.

The yield means “produce”. When the programme meets a yield statement, it “produces” an item and the next() function pauses there.

The key difference between normal functions and functions including yield is the execution flow:

A normal function executes sequentially, and return results when encountering the return statement or arriving the final line.
A function including yield is executed when next() is called and returns when the yield statement is encountered. When next() is called again, it continues from the yield statement paused last time.

There is a example:

Note: A function including yield statement is not itself a generator. It is still a function but it can return a generator every time it is called. A generator is a class rather than a function. (As we said before, the generator is a subclass of the Iterator.)

The next() can only be used for a generator. It cannot be used to a function.

More Generator Application Examples

So far, we knew generators can help us save an algorithm to produce items and generate items when needed. Comparing with a huge list containing all items, a generator reduce the time and memory costs.

In fact, a generator can even represent an infinite stream of data. For example:

The fib is an infinite generator, we can use it based on our requirements.

Another interesting application of generators is that we can combine a series of generators to get a new generator, which is technically called “pipeline”.

As above example shown, we can use the two existing generators to define a new generator. Isn’t this wonderful?

Conclusion

Generator is a very useful mechanism in Python to reduce time and memory costs. It saves an item producing algorithm rather than items. We can also use generators to produce infinite data stream and pipelines.

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