Real Python

The Real Python Podcast – Episode #111: Questions for New Dependencies & Comparing Python Game Libraries

2022-05-27T12:00:00+00:00

What are the differences between the various Python game frameworks? Would it help to see a couple of game examples across several libraries to understand the distinctions? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Level Up Your Skills With the Real Python Slack Community

2022-05-25T14:00:00+00:00

The Real Python Community Slack is an English-medium Python community with members located worldwide. It’s a welcoming group in which you’re free to discuss any questions you may have, celebrate your progress, or hang out with the community at the virtual watercooler.

This guide is for you if you want to:

Get the most out of the Real Python Slack community
Navigate some of Slack’s most valuable features
Get your questions answered by other Real Python members
Learn the dos and don’ts to keep things on an even keel

Any recommendations or questions that you may have are welcome. You can share them with the administrators in Slack or in the comments below. On Slack, you can tell if someone is an administrator by the Real Python symbol next to their name.

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Enjoy Yourself, Be Curious, Ask Questions, and Respect Others

The people in the Real Python Slack community are friendly people who love to answer questions and hang out. It’s a fantastic resource for beginners and more advanced Pythonistas looking to expand their knowledge and build friendships.

There are no stupid questions—no such thing exists! Interestingly, beginners’ questions tend to lead to the most exciting and deep discussions about Python and programming. So please, ask away!

The Real Python Slack community isn’t an adversarial forum—everyone is here to learn, and people love to help. So listen and be excellent to each other. You’ll make some friends!

Of course, there are challenges to online communication that aren’t present when people meet face to face. If you have any issues about anything at all, then please feel free to get in touch with one of the administrators:

They’ll be happy to help you in any way they can.

If you’re wondering how to navigate, benefit from, and contribute to the community, then you can read on for some helpful tips and guidelines. In the rest of this guide, you’ll get to grips with some of the dos and don’ts to keep things flowing.

Find the Best Channel to Ask Your Question

There are several Slack channels in which you can ask questions. Most channels focus on a particular topic or specialty. When you join Slack, you’ll automatically get added to a few general channels. You’re also free—and encouraged—to join any additional channels that exist within the community Slack.

You can find the list of available channels by clicking the + icon next to the Channels menu. Select the channel that you wish to join, and click the Join button. For instance, you may want to join the #webdev, #data-science, and #careers channels if those subjects interest you:

Once you’ve joined a few channels, you can ask your question in the channel that’s most closely related to your problem. If your question doesn’t fit into a particular category, then #coding-questions is the channel for you.

Spend Some Time Composing Your Questions

When you run into a problem and are in a rush, it can be tempting to jump on Slack, paste all your code, and write IT’S NOT WORKING. Unfortunately, this is a surefire way to get people to ignore your question. Knowing how to ask questions is hard!

These days, everyone is busy and in a rush. Imagine you’ve interrupted a working colleague and asked them to help you with a problem. You don’t bring your laptop, set it in front of them, and state that it’s not working. You present the issue to them in a way that might elicit a helpful response:

Summarize the problem
Give some context to the question
Provide a minimal reproducible example
Mention what you’ve tried so far
Check your spelling, grammar, and formatting

The more complicated your question is to wrap your head around, the more likely that someone won’t know the answer off the top of their head and may dismiss the question as something they don’t know. Code is hard to read—it takes time and effort, no matter your skill level.

Spend some time collecting your thoughts and presenting the specific problem, along with code, in one post.

Summarize Your Problem

Read the full article at https://realpython.com/community-slack-guide/ »

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Exploring Scopes and Closures in Python

2022-05-24T14:00:00+00:00

Have you been wondering how scopes and closures work in Python? Maybe you’ve just heard about object.__closure__, and you’d like to figure out what exactly it does. In this Code Conversation video course, you’ll use the debugger Thonny to walk through some sample code and get a better understanding of scopes and closures in Python.

In this Code Conversation video course, you’ll:

Clarify code by refactoring it with descriptive names
Learn how functions access variables in local and nonlocal scopes
Understand how inner and outer function calls open and close their own scopes

You’ll also take a deep dive into the inner workings of Python by inspecting dunder objects to find out how Python handles and stores variables.

To get the most out of this Code Conversation, you should be familiar with scopes and variables in Python. You should also be comfortable defining your own functions and distinguishing between inner and outer functions.

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How to Publish an Open-Source Python Package to PyPI

2022-05-23T14:00:00+00:00

Python is famous for coming with batteries included, and many sophisticated capabilities are available in the standard library. However, to unlock the full potential of the language, you should also take advantage of the community contributions at PyPI: the Python Packaging Index.

PyPI, typically pronounced pie-pee-eye, is a repository containing several hundred thousand packages. These range from trivial Hello, World implementations to advanced deep learning libraries. In this tutorial, you’ll learn how to upload your own package to PyPI. Publishing your project is easier than it used to be. Yet, there are still a few steps involved.

In this tutorial, you’ll learn how to:

Prepare your Python package for publication
Handle versioning of your package
Build your package and upload it to PyPI
Understand and use different build systems

Throughout this tutorial, you’ll work with an example project: a reader package that can be used to read Real Python tutorials in your console. You’ll get a quick introduction to the project before going in depth about how to publish this package. Click the link below to access the GitHub repository containing the full source code of reader:

Get Source Code: Click here to get access to the source code for the Real Python Feed Reader that you’ll work with in this tutorial.

Get to Know Python Packaging

Packaging in Python can seem complicated and confusing for both newcomers and seasoned veterans. You’ll find conflicting advice across the Internet, and what was once considered good practice may now be frowned upon.

The main reason for this situation is that Python is a fairly old programming language. Indeed, the first version of Python was released in 1991, before the World Wide Web became available to the general public. Naturally, a modern, web-based system for distribution of packages wasn’t included or even planned for in the earliest versions of Python.

Instead, Python’s packaging ecosystem has evolved organically over the decades as user needs became clear and technology offered new possibilities. The first packaging support came in the fall of 2000, with the distutils library being included in Python 1.6 and 2.0. The Python Packaging Index (PyPI) came online in 2003, originally as a pure index of existing packages, without any hosting capabilities.

Note: PyPI is often referred to as the Python Cheese Shop in reference to Monty Python’s famous Cheese Shop sketch. To this day, cheeseshop.python.org redirects to PyPI.

Over the last decade, many initiatives have improved the packaging landscape, bringing it from the Wild West and into a fairly modern and capable system. This is mainly done through Python Enhancement Proposals (PEPs) that are reviewed and implemented by the Python Packaging Authority (PyPA) working group.

The most important documents that define how Python packaging works are the following PEPs:

PEP 427 describes how wheels should be packaged.
PEP 440 describes how version numbers should be parsed.
PEP 508 describes how dependencies should be specified.
PEP 517 describes how a build backend should work.
PEP 518 describes how a build system should be specified.
PEP 621 describes how project metadata should be written.
PEP 660 describes how editable installs should be performed.

You don’t need to study these technical documents. In this tutorial, you’ll learn how all these specifications come together in practice as you go through the process of publishing your own package.

For a nice overview of the history of Python packaging, check out Thomas Kluyver’s presentation at PyCon UK 2019: Python packaging: How did we get here, and where are we going? You can also find more presentations at the PyPA website.

Create a Small Python Package

In this section, you’ll get to know a small Python package that you can use as an example that can be published to PyPI. If you already have your own package that you’re looking to publish, then feel free to skim this section and join up again at the next section.

The package that you’ll see here is called reader. It can be used both as a library for downloading Real Python tutorials in your own code and as an application for reading tutorials in your console.

Note: The source code as shown and explained in this section is a simplified—but fully functional—version of the Real Python feed reader. Compared to the version currently published on PyPI, this version lacks some error handling and extra options.

First, have a look at the directory structure of reader. The package lives completely inside a directory that can be named anything. In this case, it’s named realpython-reader/. The source code is wrapped inside an src/ directory. This isn’t strictly necessary, but it’s usually a good idea.

Note: The use of an extra src/ directory when structuring packages has been a point of discussion in the Python community for years. In general, a flat directory structure is slightly easier to get started with, but the src/-structure provides several advantages as your project grows.

The inner src/reader/ directory contains all your source code:

realpython-reader/
│
├── src/
│   └── reader/
│       ├── __init__.py
│       ├── __main__.py
│       ├── config.toml
│       ├── feed.py
│       └── viewer.py
│
├── tests/
│   ├── test_feed.py
│   └── test_viewer.py
│
├── LICENSE
├── MANIFEST.in
├── README.md
└── pyproject.toml

The source code of the package is in an src/ subdirectory together with a configuration file. There are a few tests in a separate tests/ subdirectory. The tests themselves won’t be covered in this tutorial, but you’ll learn how to treat test directories later. You can learn more about testing in general in Getting Started With Testing in Python and Effective Python Testing With Pytest.

Read the full article at https://realpython.com/pypi-publish-python-package/ »

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The Real Python Podcast – Episode #110: Advantages of Protobuf for Serialization in Python

2022-05-20T12:00:00+00:00

Would you like a way to send structured serialized data between different platforms and languages? What if the data was self-documenting, could automatically generate Python code, and would validate itself? This week on the show, Liran Haimovitch talks about protocol buffers and communicating with microservices through Remote Procedure Calls (RPC).

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Build a URL Shortener With FastAPI and Python

2022-05-18T14:00:00+00:00

In this tutorial, you’ll build a URL shortener with Python and FastAPI. URLs can be extremely long and not user-friendly. This is where a URL shortener can come in handy. A URL shortener reduces the number of characters in a URL, making it easier to read, remember, and share.

By following this step-by-step project, you’ll build a URL shortener with Python and FastAPI. At the end of this tutorial, you’ll have a fully functional API-driven web app that creates shortened URLs that forward to target URLs.

In this tutorial, you’ll learn how to:

Create a REST API with FastAPI
Run a development web server with Uvicorn
Model an SQLite database
Investigate the auto-generated API documentation
Interact with the database with CRUD actions
Optimize your app by refactoring your code

This URL shortener project is for intermediate Python programmers who want to try out FastAPI and learn about API design, CRUD, and interaction with a database. To follow along, it’ll help if you’re familiar with the basics of handling HTTP requests. If you need a refresher on FastAPI, Using FastAPI to Build Python Web APIs is a great introduction.

Get Source Code: Click here to get access to the source code that you’ll use to build your Python URL shortener with FastAPI.

Demo: Your Python URL Shortener

In this step-by-step project, you’ll build an API to create and manage shortened URLs. The main purpose of this API is to receive a full target URL and return a shortened URL. To try out your API endpoints, you’ll leverage the documentation that FastAPI automatically creates:

When you post a target URL to the URL shortener app, you get a shortened URL and a secret key back. The shortened URL contains a random key that forwards to the target URL. You can use the secret key to see the shortened URL’s statistics or delete the forwarding.

Project Overview

Your URL shortener Python project will provide API endpoints that are capable of receiving different HTTP request types. Each endpoint will perform an action that you’ll specify. Here’s a summary of your URL shortener’s API endpoints:

Endpoint	HTTP Verb	Request Body	Action
`/`	`GET`		Returns a `Hello, World!` string
`/url`	`POST`	Your target URL	Shows the created `url_key` with additional info, including a `secret_key`
`/{url_key}`	`GET`		Forwards to your target URL
`/admin/{secret_key}`	`GET`		Shows administrative info about your shortened URL
`/admin/{secret_key}`	`DELETE`	Your secret key	Deletes your shortened URL

The code you’ll write in this tutorial focuses on getting the app working first. However, having a working app doesn’t always mean that the code behind it is perfect. That’s why you’ll find a step in this tutorial where you’ll refactor parts of your app.

If you want to have a look at the final source code, go ahead and download it:

Get Source Code: Click here to get access to the source code that you’ll use to build your Python URL shortener with FastAPI.

This project is a great starting point to extend your API with more functionality. At the end of this tutorial, you’ll find ideas about what to build next.

Prerequisites

To get the most out of this tutorial, you should be comfortable with the following concepts:

The linked resources will help you better understand the code that you write in this tutorial. However, in this tutorial, you’ll build your app step by step. So even if you’re not familiar with the concepts above, then you’ll be able to follow along.

Step 1: Prepare Your Environment

In this step, you’ll prepare the development environment for your FastAPI app. First, you’ll create the folder structure for your app. Then, you’ll create a virtual environment and install all project dependencies that you need for your project. Finally, you’ll learn how to store environment variables outside of your code and how to load the variables into your app.

Create the Project’s Folder Structure

In this section, you’ll create your project structure. You can name the root folder of your project any way you like. For example, you could name it url_shortener_project/.

Read the full article at https://realpython.com/build-a-python-url-shortener-with-fastapi/ »

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Using Python Class Constructors

2022-05-17T14:00:00+00:00

Class constructors are a fundamental part of object-oriented programming in Python. They allow you to create and properly initialize objects of a given class, making those objects ready to use. Class constructors internally trigger Python’s instantiation process, which runs through two main steps: instance creation and instance initialization.

If you want to dive deeper into how Python internally constructs objects and learn how to customize the process, then this video course is for you.

In this video course, you’ll:

Understand Python’s internal instantiation process
Customize object initialization using .__init__()
Fine-tune object creation by overriding .__new__()

With this knowledge, you’ll be able to tweak the creation and initialization of objects in your custom Python classes, which will give you control over the instantiation process at a more advanced level.

To better understand the examples and concepts in this tutorial, you should be familiar with object-oriented programming and special methods in Python.

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Linear Regression in Python

2022-05-16T14:00:00+00:00

You’re living in an era of large amounts of data, powerful computers, and artificial intelligence. This is just the beginning. Data science and machine learning are driving image recognition, development of autonomous vehicles, decisions in the financial and energy sectors, advances in medicine, the rise of social networks, and more. Linear regression is an important part of this.

Linear regression is one of the fundamental statistical and machine learning techniques. Whether you want to do statistics, machine learning, or scientific computing, there’s a good chance that you’ll need it. It’s best to build a solid foundation first and then proceed toward more complex methods.

By the end of this article, you’ll have learned:

What linear regression is
What linear regression is used for
How linear regression works
How to implement linear regression in Python, step by step

Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills.

Take the Quiz: Test your knowledge with our interactive “Linear Regression in Python” quiz. Upon completion you will receive a score so you can track your learning progress over time:

Take the Quiz »

Regression

Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them.

What Is Regression?

Regression searches for relationships among variables. For example, you can observe several employees of some company and try to understand how their salaries depend on their features, such as experience, education level, role, city of employment, and so on.

This is a regression problem where data related to each employee represents one observation. The presumption is that the experience, education, role, and city are the independent features, while the salary depends on them.

Similarly, you can try to establish the mathematical dependence of housing prices on area, number of bedrooms, distance to the city center, and so on.

Generally, in regression analysis, you consider some phenomenon of interest and have a number of observations. Each observation has two or more features. Following the assumption that at least one of the features depends on the others, you try to establish a relation among them.

In other words, you need to find a function that maps some features or variables to others sufficiently well.

The dependent features are called the dependent variables, outputs, or responses. The independent features are called the independent variables, inputs, regressors, or predictors.

Regression problems usually have one continuous and unbounded dependent variable. The inputs, however, can be continuous, discrete, or even categorical data such as gender, nationality, or brand.

It’s a common practice to denote the outputs with 𝑦 and the inputs with 𝑥. If there are two or more independent variables, then they can be represented as the vector 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of inputs.

When Do You Need Regression?

Typically, you need regression to answer whether and how some phenomenon influences the other or how several variables are related. For example, you can use it to determine if and to what extent experience or gender impacts salaries.

Regression is also useful when you want to forecast a response using a new set of predictors. For example, you could try to predict electricity consumption of a household for the next hour given the outdoor temperature, time of day, and number of residents in that household.

Regression is used in many different fields, including economics, computer science, and the social sciences. Its importance rises every day with the availability of large amounts of data and increased awareness of the practical value of data.

Linear Regression

Linear regression is probably one of the most important and widely used regression techniques. It’s among the simplest regression methods. One of its main advantages is the ease of interpreting results.

Problem Formulation

When implementing linear regression of some dependent variable 𝑦 on the set of independent variables 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of predictors, you assume a linear relationship between 𝑦 and 𝐱: 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀. This equation is the regression equation. 𝛽₀, 𝛽₁, …, 𝛽ᵣ are the regression coefficients, and 𝜀 is the random error.

Read the full article at https://realpython.com/linear-regression-in-python/ »

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The Real Python Podcast – Episode #109: Start Testing Your Python with doctest & Pagination in Django

2022-05-13T12:00:00+00:00

Did you know you can add testing to your Python code while simultaneously documenting it? Using docstrings, you can create examples of how your functions should interact in a Python REPL and test them with the built-in doctest module. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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pandas GroupBy: Your Guide to Grouping Data in Python

2022-05-11T14:00:00+00:00

Whether you’ve just started working with pandas and want to master one of its core capabilities, or you’re looking to fill in some gaps in your understanding about .groupby(), this tutorial will help you to break down and visualize a pandas GroupBy operation from start to finish.

This tutorial is meant to complement the official pandas documentation and the pandas Cookbook, where you’ll see self-contained, bite-sized examples. Here, however, you’ll focus on three more involved walkthroughs that use real-world datasets.

In this tutorial, you’ll cover:

How to use pandas GroupBy operations on real-world data
How the split-apply-combine chain of operations works
How to decompose the split-apply-combine chain into steps
How to categorize methods of a pandas GroupBy object based on their intent and result

This tutorial assumes that you have some experience with pandas itself, including how to read CSV files into memory as pandas objects with read_csv(). If you need a refresher, then check out Reading CSVs With pandas and pandas: How to Read and Write Files.

You can download the source code for all the examples in this tutorial by clicking on the link below:

Download Datasets: Click here to download the datasets that you’ll use to learn about pandas’ GroupBy in this tutorial.

Prerequisites

Before you proceed, make sure that you have the latest version of pandas available within a new virtual environment:

In this tutorial, you’ll focus on three datasets:

The U.S. Congress dataset contains public information on historical members of Congress and illustrates several fundamental capabilities of .groupby().
The air quality dataset contains periodic gas sensor readings. This will allow you to work with floats and time series data.
The news aggregator dataset holds metadata on several hundred thousand news articles. You’ll be working with strings and doing text munging with .groupby().

You can download the source code for all the examples in this tutorial by clicking on the link below:

Download Datasets: Click here to download the datasets that you’ll use to learn about pandas’ GroupBy in this tutorial.

Once you’ve downloaded the .zip file, unzip the file to a folder called groupby-data/ in your current directory. Before you read on, ensure that your directory tree looks like this:

./
│
└── groupby-data/
    │
    ├── legislators-historical.csv
    ├── airqual.csv
    └── news.csv

With pandas installed, your virtual environment activated, and the datasets downloaded, you’re ready to jump in!

Example 1: U.S. Congress Dataset

You’ll jump right into things by dissecting a dataset of historical members of Congress. You can read the CSV file into a pandas DataFrame with read_csv():

# pandas_legislators.py

import pandas as pd

dtypes = {
    "first_name": "category",
    "gender": "category",
    "type": "category",
    "state": "category",
    "party": "category",
}
df = pd.read_csv(
    "groupby-data/legislators-historical.csv",
    dtype=dtypes,
    usecols=list(dtypes) + ["birthday", "last_name"],
    parse_dates=["birthday"]
)

The dataset contains members’ first and last names, birthday, gender, type ("rep" for House of Representatives or "sen" for Senate), U.S. state, and political party. You can use df.tail() to view the last few rows of the dataset:

>>>

>>> from pandas_legislators import df
>>> df.tail()
      last_name first_name   birthday gender type state       party
11970   Garrett     Thomas 1972-03-27      M  rep    VA  Republican
11971    Handel      Karen 1962-04-18      F  rep    GA  Republican
11972     Jones     Brenda 1959-10-24      F  rep    MI    Democrat
11973    Marino        Tom 1952-08-15      M  rep    PA  Republican
11974     Jones     Walter 1943-02-10      M  rep    NC  Republican

The DataFrame uses categorical dtypes for space efficiency:

>>>

>>> df.dtypes
last_name             object
first_name          category
birthday      datetime64[ns]
gender              category
type                category
state               category
party               category
dtype: object

Read the full article at https://realpython.com/pandas-groupby/ »

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Deploying a Flask Application Using Heroku

2022-05-10T14:00:00+00:00

In this video course, you’ll create a Python Flask example application and deploy it using Heroku, making it publicly available on the web. Heroku removes much of the infrastructure burden related to building and running web applications, allowing you to focus on creating an awesome app.

Besides deploying the app, you’ll use Git to track changes to the code, and you’ll also configure a deployment workflow with different environments for staging and production. Using this setup, you’ll be able to test and preview the app before releasing it.

In this video course, you’ll learn how to:

Create a Python Flask example web application
Deploy the web application using Heroku
Implement a deployment workflow using Heroku pipelines
Manage configuration and secrets for different environments in a secure way

This course assumes that you understand the basics of how web applications work and that you have some experience using Git. To get up to speed on these topics, check out Python Web Applications with Flask (Tutorial Series) and Introduction to Git and GitHub for Python Developers.

As you’ll learn through this video course, by combining Flask and Heroku, you can minimize the effort required to create and run web applications.

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Python News: What's New From April 2022

2022-05-09T14:00:00+00:00

April 2022 saw the return of the PyCon US conference in person in Salt Lake City. During the conference, Python developers met for the annual Language Summit, and Anaconda announced PyScript, a way to write Python directly inside HTML. Earlier in the month, the Python Software Foundation (PSF) welcomed its new executive director.

Read on to dive into the biggest Python news from the past month!

PyScript: Python in Your Browser

During his keynote at PyCon US, Anaconda CEO Peter Wang unveiled the PyScript project. PyScript allows you to write Python directly inside HTML and run it in your browser. Consider the following example:

<html>
<head>
    <link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
    <script defer src="https://pyscript.net/alpha/pyscript.js"></script>
</head>

<body>
    <py-script> print('Hello, World!') </py-script>
</body>
</html>

Note the <py-script> tag, which can contain any valid Python code. In this case, this is the traditional Hello, World! greeting.

This is functioning code. You can copy the above code block into a file, such as hello.html, and save that file to your computer. Then you can open it in your browser, for example by using Ctrl+O or Cmd+O and selecting hello.html. Alternatively, you can test a similar example immediately on the PyScript demos page.

PyScript provides custom HTML tags, including <py-script>, which you saw above. There are several other tags as well, with many still in development. However, here are a couple that are immediately useful:

<py-env> lists packages that should be made available in the environment.
<py-repl> creates a working Python REPL to interact with the environment.

Read the full article at https://realpython.com/python-news-april-2022/ »

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The Real Python Podcast – Episode #108: Run Python in a Browser With Pyodide & The Power of f-Strings

2022-05-06T12:00:00+00:00

Have you heard about the projects working toward getting Python to run in the browser? Maybe you would like to try it out for yourself, by building an interactive Python REPL with Pyodide and WebAssembly (WASM). This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Top Python Game Engines

2022-05-04T14:00:00+00:00

Like many people, maybe you wanted to write video games when you first learned to code. But were those games like the games you played? Maybe there was no Python when you started, no Python games available for you to study, and no game engines to speak of. With no real guidance or framework to assist you, the advanced graphics and sound that you experienced in other games may have remained out of reach.

Now, there’s Python, and a host of great Python game engines available. This powerful combination makes crafting great computer games much easier than in the past. In this tutorial, you’ll explore several of these game engines, learning what you need to start crafting your own Python video games!

By the end of this article, you’ll:

Understand the pros and cons of several popular Python game engines
See these game engines in action
Understand how they compare to stand-alone game engines
Learn about other Python game engines available

To get the most out of this tutorial, you should be well-versed in Python programming, including object-oriented programming. An understanding of basic game concepts is helpful, but not necessary.

Ready to dive in? Click the link below to download the source code for all the games that you’ll be creating:

Get Source Code: Click here to get the source code you’ll use to try out Python game engines.

Python Game Engines Overview

Game engines for Python most often take the form of Python libraries, which can be installed in a variety of ways. Most are available on PyPI and can be installed with pip. However, a few are available only on GitHub, GitLab, or other code sharing locations, and they may require other installation steps. This article will cover installation methods for all the engines discussed.

Python is a general purpose programming language, and it’s used for a variety of tasks other than writing computer games. In contrast, there are many different stand-alone game engines that are tailored specifically to writing games. Some of these include:

These stand-alone game engines differ from Python game engines in several key aspects:

Language support: Languages like C++, C#, and JavaScript are popular for games written in stand-alone game engines, as the engines themselves are often written in these languages. Very few stand-alone engines support Python.
Proprietary scripting support: In addition, many stand-alone game engines maintain and support their own scripting languages, which may not resemble Python. For example, Unity uses C# natively, while Unreal works best with C++.
Platform support: Many modern stand-alone game engines can produce games for a variety of platforms, including mobile and dedicated game systems, with very little effort. In contrast, porting a Python game across various platforms, especially mobile platforms, can be a major undertaking.
Licensing options: Games written using a stand-alone game engine may have different licensing options and restrictions, based on the engine used.

So why use Python to write games at all? In a word, Python. Using a stand-alone game engine often requires you to learn a new programming or scripting language. Python game engines leverage your existing knowledge of Python, reducing the learning curve and getting you moving forward quickly.

There are many game engines available for the Python environment. The engines that you’ll learn about here all share the following criteria:

They’re relatively popular engines, or they cover aspects of gaming that aren’t usually covered.
They’re currently maintained.
They have good documentation available.

For each engine, you’ll learn about:

Installation methods
Basic concepts, as well as assumptions that the engine makes
Major features and capabilities
Two game implementations, to allow for comparison

Where appropriate, you should install these game engines in a virtual environment. Full source code for the games in this tutorial is available for download at the link below and will be referenced throughout the article:

Get Source Code: Click here to get the source code you’ll use to try out Python game engines.

With the source code downloaded, you’re ready to begin.

Pygame

When people think of Python game engines, the first thought many have is Pygame. In fact, there’s already a great primer on Pygame available at Real Python.

Written as a replacement for the stalled PySDL library, Pygame wraps and extends the SDL library, which stands for Simple DirectMedia Layer. SDL provides cross-platform access to your system’s underlying multimedia hardware components, such as sound, video, mouse, keyboard, and joystick. The cross-platform nature of both SDL and Pygame means that you can write games and rich multimedia Python programs for every platform that supports them!

Pygame Installation

Read the full article at https://realpython.com/top-python-game-engines/ »

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Testing Your Code With pytest

2022-05-03T14:00:00+00:00

Testing your code brings a wide variety of benefits. It increases your confidence that the code behaves as you expect and ensures that changes to your code won’t cause regressions. Writing and maintaining tests is hard work, so you should leverage all the tools at your disposal to make it as painless as possible. pytest is one of the best tools you can use to boost your testing productivity.

In this video course, you’ll learn:

What benefits pytest offers
How to ensure your tests are stateless
How to make repetitious tests more comprehensible
How to run subsets of tests by name or custom groups
How to create and maintain reusable testing utilities

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Python's min() and max(): Find Smallest and Largest Values

2022-05-02T14:00:00+00:00

Python’s built-in min() and max() functions come in handy when you need to find the smallest and largest values in an iterable or in a series of regular arguments. Even though these might seem like fairly basic computations, they turn out to have many interesting use cases in real-world programing. You’ll try out some of those use cases here.

In this tutorial, you’ll learn how to:

Use Python’s min() and max() to find smallest and largest values in your data
Call min() and max() with a single iterable or with any number of regular arguments
Use min() and max() with strings and dictionaries
Tweak the behavior of min() and max() with the key and default arguments
Use comprehensions and generator expressions as arguments to min() and max()

Once you have this knowledge under your belt, then you’ll be prepared to write a bunch of practical examples that will showcase the usefulness of min() and max(). Finally, you’ll code your own versions of min() and max() in pure Python, which can help you understand how these functions work internally.

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To get the most out of this tutorial, you should have some previous knowledge of Python programming, including topics like for loops, functions, list comprehensions, and generator expressions.

Getting Started With Python’s `min()` and `max()` Functions

Python includes several built-in functions that make your life more pleasant and productive because they mean you don’t need to reinvent the wheel. Two examples of these functions are min() and max(). They mostly apply to iterables, but you can use them with multiple regular arguments as well. What’s their job? They take care of finding the smallest and largest values in their input data.

Whether you’re using Python’s min() or max(), you can use the function to achieve two slightly different behaviors. The standard behavior for each is to return the minimum or maximum value through straightforward comparison of the input data as it stands. The alternative behavior is to use a single-argument function to modify the comparison criteria before finding the smallest and largest values.

To explore the standard behavior of min() and max(), you can start by calling each function with either a single iterable as an argument or with two or more regular arguments. That’s what you’ll do right away.

Calling `min()` and `max()` With a Single Iterable Argument

The built-in min() and max() have two different signatures that allow you to call them either with an iterable as their first argument or with two or more regular arguments. The signature that accepts a single iterable argument looks something like this:

min(iterable, *[, default, key]) -> minimum_value

max(iterable, *[, default, key]) -> maximum_value

Both functions take a required argument called iterable and return the minimum and maximum values respectively. They also take two optional keyword-only arguments: default and key.

Note: In the above signatures, the asterisk (*) means that the following arguments are keyword-only arguments, while the square brackets ([]) denote that the enclosed content is optional.

Here’s a summary of what the arguments to min() and max() do:

Argument	Description	Required
`iterable`	Takes an iterable object, like a list, tuple, dictionary, or string	Yes
`default`	Holds a value to return if the input iterable is empty	No
`key`	Accepts a single-argument function to customize the comparison criteria	No

Later in this tutorial, you’ll learn more about the optional default and key arguments. For now, just focus on the iterable argument, which is a required argument that leverages the standard behavior of min() and max() in Python:

>>>

>>> min([3, 5, 9, 1, -5])
-5

>>> min([])
Traceback (most recent call last):
    ...
ValueError: min() arg is an empty sequence

>>> max([3, 5, 9, 1, -5])
9

>>> max([])
Traceback (most recent call last):
    ...
ValueError: max() arg is an empty sequence

In these examples, you call min() and max() with a list of integer numbers and then with an empty list. The first call to min() returns the smallest number in the input list, -5. In contrast, the first call to max() returns the largest number in the list, or 9. If you pass an empty iterator to min() or max(), then you get a ValueError because there’s nothing to do on an empty iterable.

An important detail to note about min() and max() is that all the values in the input iterable must be comparable. Otherwise, you get an error. For example, numeric values work okay:

>>>

>>> min([3, 5.0, 9, 1.0, -5])
-5

>>> max([3, 5.0, 9, 1.0, -5])
9

These examples combine int and float numbers in the calls to min() and max(). You get the expected result in both cases because these data types are comparable.

However, what would happen if you mixed strings and numbers? Check out the following examples:

>>>

>>> min([3, "5.0", 9, 1.0, "-5"])
Traceback (most recent call last):
    ...
TypeError: '<' not supported between instances of 'str' and 'int'

>>> max([3, "5.0", 9, 1.0, "-5"])
Traceback (most recent call last):
    ...
TypeError: '>' not supported between instances of 'str' and 'int'

Read the full article at https://realpython.com/python-min-and-max/ »

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Real Python at PyCon US 2022

2022-04-29T17:15:00+00:00

PyCon US is back as an in-person conference. PyCon US 2022 is happening in Salt Lake City April 29 to May 1, and Real Python is there as well. Come join us at our booth and at the open space on Saturday.

In this article, you’ll learn where you can find Real Python at PyCon in Salt Lake City, and get to know what some of our team members are excited about at the conference.

Meet Real Python at PyCon US 2022

The PyCon US conference has been an annual meeting place for the Python community since 2003. Because of the COVID pandemic, the conference went virtual in 2020 and 2021. At Real Python, we’re excited about being able to meet in person this year. Come say hello if you’re in Salt Lake City!

Visit the Real Python Booth

The exhibit hall is a lively place at any PyCon conference. Here, you can stroll around and chat with other attendees while exploring what sponsors and exhibitors have brought to the table. It’s a great place to hang out and make new friends!

Real Python has a booth at this year’s conference. We’re excited about having our own place to hang out and show our content to everyone. You can find us at booth 228, which is just opposite Microsoft and AWS. Look for our logo and friendly faces—we’ll be smiling with our eyes!

Stop by the booth to hear about all the content that we offer, or have a chat about your favorite packages, square roots, or the latest developments in Python.

Join Our Open Space

The open spaces are a unique staple of PyCon. These are self-organized one-hour meetup-like events that are happening throughout the conference. Check out the Open Space board to see if there’s anything that you’d like to join!

Read the full article at https://realpython.com/real-python-pycon-us-2022/ »

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Why Is It Important to Close Files in Python?

2022-04-27T14:00:00+00:00

At some point in your Python coding journey, you learn that you should use a context manager to open files. Python context managers make it easy to close your files once you’re done with them:

with open("hello.txt", mode="w") as file:
    file.write("Hello, World!")

The with statement initiates a context manager. In this example, the context manager opens the file hello.txt and manages the file resource as long as the context is active. In general, all the code in the indented block depends on the file object being open. Once the indented block either ends or raises an exception, then the file will close.

If you’re not using a context manager or you’re working in a different language, then you might explicitly close files with the try … finally approach:

try:
    file = open("hello.txt", mode="w")
    file.write("Hello, World!")
finally:
    file.close()

The finally block that closes the file runs unconditionally, whether the try block succeeds or fails. While this syntax effectively closes the file, the Python context manager offers less verbose and more intuitive syntax. Additionally, it’s a bit more flexible than simply wrapping your code with try … finally.

You probably use context managers to manage files already, but have you ever wondered why most tutorials and four out of five dentists recommend doing this? In short, why is it important to close files in Python?

In this tutorial, you’ll dive into that very question. First, you’ll learn about how file handles are a limited resource. Then you’ll experiment with the consequences of not closing your files.

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In Short: Files Are Resources Limited by the Operating System

Python delegates file operations to the operating system. The operating system is the mediator between processes, such as Python, and all the system resources, such as the hard drive, RAM, and CPU time.

When you open a file with open(), you make a system call to the operating system to locate that file on the hard drive and prepare it for reading or writing. The operating system will then return an unsigned integer called a file handle on Windows and a file descriptor on UNIX-like systems, including Linux and macOS:

Once you have the number associated with the file, you’re ready to do read or write operations. Whenever Python wants to read, write, or close the file, it’ll make another system call, providing the file handle number. The Python file object has a .fileno() method that you can use to find the file handle:

>>>

>>> with open("test_file.txt", mode="w") as file:
...     file.fileno()
...
4

The .fileno() method on the opened file object will return the integer used by the operating system as a file descriptor. Just like how you might use an ID field to get a record from a database, Python provides this number to the operating system every time it reads or writes from a file.

Read the full article at https://realpython.com/why-close-file-python/ »

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The Real Python Podcast – Episode #107: Type-Safe ORM With Prisma Client & Real Python at PyCon US 2022

2022-04-22T12:00:00+00:00

Are you using an Object-Relational Mapper (ORM) for your Python projects? What if it could work with SQL or No-SQL databases and be fully type-safe? This week on the show, Robert Craigie talks about Prisma Client Python.

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Building a Django User Management System

2022-04-19T14:00:00+00:00

If you’re building your own Django applications, you might decide to extend them with user accounts. In this video course, you’ll learn how to work with Django user management and add it to your program.

By the end of this video course, you’ll be able to:

Create an application where users can register, log in, and reset and change passwords on their own
Edit the default Django templates responsible for user management
Send password reset emails to actual email addresses
Authenticate using an external service

You may find it useful to complete Get Started With Django: Build a Portfolio App before beginning this course. However, you can also apply Django user management to any Django apps that you’ve created.

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The Real Python Podcast – Episode #106: Class Constructors & Pythonic Image Processing

2022-04-15T12:00:00+00:00

Do you know the difference between creating a class instance and initializing it? Would you like an interactive tour of the Python Pillow library? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Exploring Keywords in Python

2022-04-12T14:00:00+00:00

Every programming language has special reserved words, or keywords, that have specific meanings and restrictions around how they should be used. Python is no different. Python keywords are the fundamental building blocks of any Python program.

In this video course, you’ll find a basic introduction to all Python keywords along with other resources that will be helpful for learning more about each keyword.

By the end of this video course, you’ll be able to:

Identify Python keywords
Understand what each keyword is used for
Work with keywords programmatically using the keyword module

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #105: Creating Better Error Messages for Python 3.10 & 3.11

2022-04-08T12:00:00+00:00

What goes into creating those enhanced error messages in the latest versions of Python? How does the new PEG parser help to pinpoint where errors have occurred? This week on the show, Pablo Galindo Salgado talks about the work that goes into creating these improvements.

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Python REST APIs With FastAPI

2022-04-05T14:00:00+00:00

Creating APIs, or application programming interfaces, is an important part of making your software accessible to a broad range of users. In this video course, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default.

By the end of it, you’ll be able to start creating production-ready web APIs, and you’ll have the understanding needed to go deeper and learn more for your specific use cases.

In this video course, you’ll learn how to:

Use path parameters to get a unique URL path per item
Receive JSON data in your requests using pydantic
Use API best practices, including validation, serialization, and documentation
Continue learning about FastAPI for your use cases

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The Real Python Podcast – Episode #104: Building a Hash Table in Python and Thoughtful REST API Design

2022-04-01T12:00:00+00:00

Do you understand how a hash table works? What if you could learn about building one while practicing test-driven development? What are best practices when designing a REST API? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Using Python's datetime Module

2022-03-29T14:00:00+00:00

Python has several different modules to deal with dates and times. This course concentrates on the primary one, datetime. Dates and times are messy things! Shifts due to daylight savings time and time zones complicate any computing with dates and times.

In this course, you’ll tackle that messiness and learn:

How to use the datetime module
What the zoneinfo module does and how to use it
How to calculate the difference between two datetime objects

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The Real Python Podcast – Episode #103: Becoming More Effective at Manipulating Data With Pandas

2022-03-25T12:00:00+00:00

Do you wonder if you're taking the right approach when shaping data in pandas? Is your Jupyter workflow getting out of hand? This week on the show, Matt Harrison talks about his new book, "Effective Pandas: Patterns for Data Manipulation."

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Python Basics: Code Your First Python Program

2022-03-22T14:00:00+00:00

In the previous Python Basics video course, you set up Python on your computer. With Python installed, you’re ready ready to start coding!

In this video course, you’ll:

Write your first Python program
Learn what happens when you run a program with an error
Learn how to declare a variable and inspect its value
Learn how to write comments

This course can be enjoyed alone or as an accompaniment to Python Basics: A Practical Introduction to Python 3.

Ready to begin your Python journey? Let’s go!

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The Real Python Podcast – Episode #102: Making Your Notebook Interactive and Using Python's Assert

2022-03-18T12:00:00+00:00

Would you like to build visualizations that allow your audience to play with data? How do you effectively use Python's assert statement during development? This week on the show, Christopher Trudeau is here, and he's brought another batch of PyCoder's Weekly articles and projects.

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Sorting Data in Python With Pandas

2022-03-15T14:00:00+00:00

Learning pandas sort methods is a great way to start with or practice doing basic data analysis using Python. Most commonly, data analysis is done with spreadsheets, SQL, or pandas. One of the great things about using pandas is that it can handle a large amount of data and offers highly performant data manipulation capabilities.

In this video course, you’ll learn how to use .sort_values() and .sort_index(), which will enable you to sort data efficiently in a DataFrame.

By the end of this video course, you’ll know how to:

Sort a pandas DataFrame by the values of one or more columns
Use the ascending parameter to change the sort order
Sort a DataFrame by its index using .sort_index()
Organize missing data while sorting values
Sort a DataFrame in-place using inplace set to True

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Real Python

The Real Python Podcast – Episode #111: Questions for New Dependencies & Comparing Python Game Libraries

Level Up Your Skills With the Real Python Slack Community

Enjoy Yourself, Be Curious, Ask Questions, and Respect Others

Find the Best Channel to Ask Your Question

Spend Some Time Composing Your Questions

Summarize Your Problem

Exploring Scopes and Closures in Python

How to Publish an Open-Source Python Package to PyPI

Get to Know Python Packaging

Create a Small Python Package

The Real Python Podcast – Episode #110: Advantages of Protobuf for Serialization in Python

Build a URL Shortener With FastAPI and Python

Demo: Your Python URL Shortener

Project Overview

Prerequisites

Step 1: Prepare Your Environment

Create the Project’s Folder Structure

Using Python Class Constructors

Linear Regression in Python

Regression

What Is Regression?

When Do You Need Regression?

Linear Regression

Problem Formulation

The Real Python Podcast – Episode #109: Start Testing Your Python with doctest & Pagination in Django

pandas GroupBy: Your Guide to Grouping Data in Python

Prerequisites

Example 1: U.S. Congress Dataset

Deploying a Flask Application Using Heroku

Python News: What's New From April 2022

PyScript: Python in Your Browser

The Real Python Podcast – Episode #108: Run Python in a Browser With Pyodide & The Power of f-Strings

Top Python Game Engines

Python Game Engines Overview

Pygame

Pygame Installation

Testing Your Code With pytest

Python's min() and max(): Find Smallest and Largest Values

Getting Started With Python’s min() and max() Functions

Calling min() and max() With a Single Iterable Argument

Real Python at PyCon US 2022

Meet Real Python at PyCon US 2022

Visit the Real Python Booth

Join Our Open Space

Why Is It Important to Close Files in Python?

In Short: Files Are Resources Limited by the Operating System

The Real Python Podcast – Episode #107: Type-Safe ORM With Prisma Client & Real Python at PyCon US 2022

Building a Django User Management System

The Real Python Podcast – Episode #106: Class Constructors & Pythonic Image Processing

Exploring Keywords in Python

The Real Python Podcast – Episode #105: Creating Better Error Messages for Python 3.10 & 3.11

Python REST APIs With FastAPI

The Real Python Podcast – Episode #104: Building a Hash Table in Python and Thoughtful REST API Design

Using Python's datetime Module

The Real Python Podcast – Episode #103: Becoming More Effective at Manipulating Data With Pandas

Python Basics: Code Your First Python Program

The Real Python Podcast – Episode #102: Making Your Notebook Interactive and Using Python's Assert

Sorting Data in Python With Pandas

Getting Started With Python’s `min()` and `max()` Functions

Calling `min()` and `max()` With a Single Iterable Argument