Real Python

The Real Python Podcast – Episode #119: Natural Language Processing and How ML Models Understand Text

2022-07-29T12:00:00+00:00

How do you process and classify text documents in Python? What are the fundamental techniques and building blocks for Natural Language Processing (NLP)? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, talks about how machine learning (ML) models understand text.

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Primer on Jinja Templating

2022-07-27T14:00:00+00:00

Templates are an essential ingredient in full-stack web development. With Jinja, you can build rich templates that power the front end of your Python web applications.

But you don’t need to use a web framework to experience the capabilities of Jinja. When you want to create text files with programmatic content, Jinja can help you out.

In this tutorial, you’ll learn how to:

Install the Jinja template engine
Create your first Jinja template
Render a Jinja template in Flask
Use for loops and conditional statements with Jinja
Nest Jinja templates
Modify variables in Jinja with filters
Use macros to add functionality to your front end

You’ll start by using Jinja on its own to cover the basics of Jinja templating. Later you’ll build a basic Flask web project with two pages and a navigation bar to leverage the full potential of Jinja.

Throughout the tutorial, you’ll build an example app that showcases some of Jinja’s wide range of features. To see what it’ll do, skip ahead to the final section.

You can also find the full source code of the web project by clicking on the link below:

Source Code: Click here to download the source code that you’ll use to explore Jinja’s capabilities.

This tutorial is for you if you want to learn more about the Jinja template language or if you’re getting started with Flask.

Get Started With Jinja

Jinja is not only a city in the Eastern Region of Uganda and a Japanese temple, but also a template engine. You commonly use template engines for web templates that receive dynamic content from the back end and render it as a static page in the front end.

But you can use Jinja without a web framework running in the background. That’s exactly what you’ll do in this section. Specifically, you’ll install Jinja and build your first templates.

Install Jinja

Before exploring any new package, it’s a good idea to create and activate a virtual environment. That way, you’re installing any project dependencies in your project’s virtual environment instead of system-wide.

Select your operating system below and use your platform-specific command to set up a virtual environment:

With the above commands, you create and activate a virtual environment named venv by using Python’s built-in venv module. The parentheses (()) surrounding venv in front of the prompt indicate that you’ve successfully activated the virtual environment.

After you’ve created and activated your virtual environment, it’s time to install Jinja with pip:

(venv) $ python -m pip install Jinja2

Don’t forget the 2 at the end of the package name. Otherwise, you’ll install an old version that isn’t compatible with Python 3.

It’s worth noting that although the current major version is actually greater than 2, the package that you’ll install is nevertheless called Jinja2. You can verify that you’ve installed a modern version of Jinja by running pip list:

(venv) $ python -m pip list
Package    Version
---------- -------
Jinja2     3.x
...

To make things even more confusing, after installing Jinja with an uppercase J, you have to import it with a lowercase j in Python. Try it out by opening the interactive Python interpreter and running the following commands:

>>>

>>> import Jinja2
Traceback (most recent call last):
  ...
ModuleNotFoundError: No module named 'Jinja2'

>>> import jinja2
>>> # No error

Read the full article at https://realpython.com/primer-on-jinja-templating/ »

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Managing Attributes With Python's property()

2022-07-26T14:00:00+00:00

With Python’s property(), you can create managed attributes in your classes. You can use managed attributes, also known as properties, when you need to modify their internal implementation without changing the public API of the class. Providing stable APIs can help you avoid breaking your users’ code when they rely on your classes and objects.

Properties are arguably the most popular way to create managed attributes quickly and in the purest Pythonic style.

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

Create managed attributes or properties in your classes
Perform lazy attribute evaluation and provide computed attributes
Avoid setter and getter methods to make your classes more Pythonic
Create read-only, read-write, and write-only properties
Create consistent and backwards-compatible APIs for your classes

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Your Python Coding Environment on Windows: Setup Guide

2022-07-25T14:00:00+00:00

Are you interested in writing Python code on a Windows machine? Maybe you’re a lifelong Windows user getting into coding with Python, or perhaps you’re just beginning to branch out from macOS or Linux. In this tutorial, you’ll walk through an easy-to-follow and flexible Python coding setup on Windows 10.

Note: Most of the steps here will work equally well on Windows 11.

To set up your Windows machine for Python coding, you’ll:

Clean and update a new Windows install
Use a package manager to bulk install key software
Use the built-in ssh-keygen to generate SSH keys and connect to your GitHub account
Set up a development environment, including PowerShell Core, pyenv for Windows, Python, and VS Code
Examine scripts and tools to automate the setup and installation process

Along the way, you’ll learn about various recommended tools for a complete, free, and mostly open-source Windows Python coding setup. You won’t get to know all the possible tools, but you’ll walk away with one setup that should be flexible enough for most situations.

If you’re a beginner to intermediate Pythonista, or you’re just looking to get set up on a Windows machine without having to think about all the different options out there, then this tutorial is for you.

Free Bonus: Click here to get our free Python Cheat Sheet that shows you the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Maybe you’re in a rush to get set up. If that’s the case, then you’ll find what you need in the section on fast-tracking your setup, where you’ll examine a PowerShell script to automate the process almost entirely. Jump over there if you need to get up and running quickly. That said, going over the first section will give you a good checklist to start with to make sure you won’t run into any errors.

Setting Up Your Basic Python Coding Environment in Windows

Say you’re sitting in front of a completely fresh Windows installation. In this section, you’ll go through the first steps that you should perform in that case. You’ll get an initial command line environment set up, install software via package managers, and configure essential Windows settings for a Python coding setup.

Note: If you’re going to be following along, you’ll be restarting your computer various times during this whole process. It’s worthwhile to have another device close by, not only for keeping this tutorial open, but for general queries, troubleshooting, and the ability to carry on reading other Real Python tutorials while waiting for updates or installations.

In addition to installing what you need, you’ll also learn how to get around some of Microsoft’s built-in programs that might get in the way. Your first step is to ensure that your Windows installation is up-to-date.

Updating Your Windows Installation

First, if your machine is new or you’ve disabled updates, then you’ll want to make sure your Windows installation is up-to-date. Updating is especially relevant if you want to use Windows Subsystem for Linux (WSL) and have Windows 10. It’s also a safeguard against the dreaded Windows auto-reboot.

Installing Windows updates can take an hour or two, but it’s relatively mindless. So if you’re starting here, line up some other tasks while waiting for the updates to complete.

To start the update process, go to the Start menu and search for Windows Update. Once there, prompt Windows to search for, download, and install any updates it can find:

Once all the updates have been installed, you’ll probably be prompted to reboot the computer. Do this as many times as it asks you to.

You’ll probably have to repeat the update process two or three times. Once the first batch of updates have been installed and you’ve rebooted, if you go back to the update screen, then it’ll probably say that the system is up-to-date. However, if you wait a while and then check for updates again, it’ll likely find some more to install.

Once you have all the updates installed, then you’re ready to do the initial setup for the command line environment in your Windows Python coding setup.

Discovering the Windows Terminal

If you’ve always used Windows, then you may be used to using the Command Prompt and PowerShell through their own apps. Opening them this way may make you think that they’re self-contained programs. However, when you run these programs from the Start menu, two processes are running. One process is what performs the commands, and the other is a process to send your input and display the output.

The window that you see when you open the Command Prompt is the displaying process. The core of the Command Prompt is in the background awaiting your input that’s sent by the window. In this way, the window acts as a sort of host to the terminal.

For more information about the differences between command interpreters and interfaces, take a look at the introduction to the shell section in the article about the subprocess module.

Windows has created a new, open-source Windows Terminal to be a universal console host. It acts as an interface to multiple shells, allowing you to start the Command Prompt, PowerShell, and any other shell that you might have available as different tabs in the same host:

Read the full article at https://realpython.com/python-coding-setup-windows/ »

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The Real Python Podcast – Episode #118: Creating Documentation With MkDocs & When to Use a Python dict

2022-07-22T12:00:00+00:00

How do you start building your project documentation? What if you had a tool that could do the heavy lifting and automatically write large portions directly from your code? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Custom Python Dictionaries: Inheriting From dict vs UserDict

2022-07-20T14:00:00+00:00

Creating dictionary-like classes may be a requirement in your Python career. Specifically, you may be interested in making custom dictionaries with modified behavior, new functionalities, or both. In Python, you can do this by inheriting from an abstract base class, by subclassing the built-in dict class directly, or by inheriting from UserDict.

In this tutorial, you’ll learn how to:

Create dictionary-like classes by inheriting from the built-in dict class
Identify common pitfalls that can happen when inheriting from dict
Build dictionary-like classes by subclassing UserDict from the collections module

Additionally, you’ll code a few examples that’ll help you understand the pros and cons of using dict vs UserDict to create your custom dictionary classes.

To get the most out of this tutorial, you should be familiar with Python’s built-in dict class and its standard functionality and features. You’ll also need to know the basics of object-oriented programming and understand how inheritance works in Python.

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Creating Dictionary-Like Classes in Python

The built-in dict class provides a valuable and versatile collection data type, the Python dictionary. Dictionaries are everywhere, including in your code and the code of Python itself.

Sometimes, the standard functionality of Python dictionaries isn’t enough for certain use cases. In these situations, you’ll probably have to create a custom dictionary-like class. In other words, you need a class that behaves like a regular dictionary but with modified or new functionality.

You’ll typically find at least two reasons for creating custom dictionary-like classes:

Extending the regular dictionary by adding new functionality
Modifying the standard dictionary’s functionality

Note that you could also face situations in which you need to both extend and modify the dictionary’s standard functionality.

Depending on your specific needs and skill level, you can choose from a few strategies for creating custom dictionaries. You can:

Inherit from an appropriate abstract base class, such as MutableMapping
Inherit from the Python built-in dict class directly
Subclass UserDict from collections

There are a few key considerations when you’re selecting the appropriate strategy to implement. Keep reading for more details.

Building a Dictionary-Like Class From an Abstract Base Class

This strategy for creating dictionary-like classes requires that you inherit from an abstract base class (ABC), like MutableMapping. This class provides concrete generic implementations of all the dictionary methods except for .__getitem__(), .__setitem__(), .__delitem__(), .__iter__(), and .__len__(), which you’ll have to implement by yourself.

Additionally, suppose you need to customize the functionality of any other standard dictionary method. In that case, you’ll have to override the method at hand and provide a suitable implementation that fulfills your needs.

This process implies a fair amount of work. It’s also error-prone and requires advanced knowledge of Python and its data model. It can also imply performance issues because you’ll be writing the class in pure Python.

The main advantage of this strategy is that the parent ABC will alert you if you miss any method in your custom implementation.

For these reasons, you should embrace this strategy only if you need a dictionary-like class that’s fundamentally different from the built-in dictionary.

In this tutorial, you’ll focus on creating dictionary-like classes by inheriting from the built-in dict class and the UserDict class, which seem to be the quickest and most practical strategies.

Inheriting From the Python Built-in `dict` Class

For a long time, it was impossible to subclass Python types implemented in C. Python 2.2 fixed this issue. Now you can directly subclass built-in types, including dict. This change brings several technical advantages to the subclasses because now they:

Will work in every place that requires the original built-in type
Can define new instance, static, and class methods
Can store their instance attributes in a .__slots__ class attribute, which essentially replaces the .__dict__ attribute

Read the full article at https://realpython.com/inherit-python-dict/ »

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Using the Python not Operator

2022-07-19T14:00:00+00:00

Python’s not operator allows you to invert the truth value of Boolean expressions and objects. You can use this operator in Boolean contexts, such as if statements and while loops. It also works in non-Boolean contexts, which allows you to invert the truth value of your variables.

Using the not operator effectively will help you write accurate negative Boolean expressions to control the flow of execution in your programs.

In this course, you’ll learn:

How Python’s not operator works
How to use the not operator in Boolean and non-Boolean contexts
How to use the operator.not_() function to perform logical negation
How and when to avoid unnecessary negative logic in your code

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Build a Flashcards App With Django

2022-07-18T14:00:00+00:00

Flashcards are a great tool when you want to memorize a new topic or learn a new language. You write a question on the front of the card and the answer on the back of the card. Then you can test your memory by going through the flashcards. The more often you show a card to yourself, the better your chances of memorizing its content. With Django, you can build your own flashcards app.

By following this tutorial, you’ll build a Django flashcards app that replicates a spaced repetition system, which can boost your learning potential.

In this step-by-step project, you’ll learn how to:

Set up a Django project
Work with a SQLite database and the Django shell
Create models and class-based views
Structure and nest templates
Create custom template tags

Along the way, you’ll get step-by-step guidance to create your final flashcards app. That means you’ll get all the code you need in separate, bite-size pieces. You can also find the full source code of the application by clicking on the link below:

Source Code: Click here to download the source code that you’ll use to build your Django flashcards app.

If you want to learn about a new topic while sharpening your Django and Python skills, then this project is the perfect match for you!

Demo: Your Django Flashcards App

In this tutorial, you’ll build a flashcards app. On the front page of your web app, you’ll see all your existing cards and be able to create new ones. On each flashcard, you can add a question and an answer, which you can later edit. When you want to test your knowledge, you can navigate to a box to review the cards that the box contains:

Once you’ve learned the answer to the card’s question, the card moves to the next box. If a flashcard moves to the next box, that doesn’t mean you’re done with it. You’ll still review it periodically to refresh your memory, and it’ll continue progressing through the boxes. Basically, the higher the box number, the more likely that you’ve mastered those concepts. If you don’t know the answer to a card’s question, then the card moves back to the first box.

Project Overview

Your work in this tutorial is divided into multiple steps. That way, you can take breaks and continue at your own pace. You’ll build a full-stack web app with a database connection that replicates the Leitner system:

In [the Leitner system], flashcards are sorted into groups according to how well the learner knows each one in Leitner’s learning box. The learners try to recall the solution written on a flashcard. If they succeed, they send the card to the next group. If they fail, they send it back to the first group. (Source)

By using spaced repetition, you’ll test your knowledge of the new or challenging topics in the first box more frequently, while you’ll check the cards from the other boxes in larger time intervals:

You have five boxes that can contain flashcards.
When you create a flashcard, you put it into the first box.
To test your knowledge, you choose a box, pick a random flashcard, and check if you know the answer to the card’s question.
If you know the answer, then you move the card to the next higher box.
If you don’t know the answer, then you move the card back to the first box.

The higher the box number, the less frequently you check the flashcards in that box to test your knowledge.

Note: Flashcards are an excellent tool for learning a new language. In the examples throughout this tutorial, you’ll find translations of English and Spanish words. But the examples are kept minimal on purpose. That way, you can conveniently customize the questions and answers on your cards.

This project is a great starting point, and then you can enhance your Django project with more features. At the end of this tutorial, you’ll find ideas about what to build next.

Prerequisites

You don’t need any previous knowledge of Django or databases to complete this project. If you want to learn more about the topics that you encounter in this tutorial, you’ll find links to resources along the way.

However, you should be comfortable using the command line and have a basic knowledge of Python and classes. Although it helps to know about virtual environments and pip, you’ll learn how to set everything up as you work through the tutorial.

Step 1: Prepare Your Django Project

In this step, you’ll prepare the development environment for your Django flashcards project. First, you’ll create a virtual environment and install all dependencies that you need for your project. At the end of this step, you’ll create the Django project and verify that your project is set up correctly in the browser.

Create a Virtual Environment

Read the full article at https://realpython.com/django-flashcards-app/ »

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The Real Python Podcast – Episode #117: Measuring Python Code Quality, Simplicity, and Maintainability

2022-07-15T12:00:00+00:00

How maintainable is your Python code? Is it possible to hold the code for your functions in your head? When is it appropriate to use measurements in a code review? This week on the show, Reka Horvath and Ben Martineau from Sourcery are here to discuss their recent PyCon talk, "Actionable insights vs ranking: How to use and how NOT to use code quality metrics."

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NumPy's max() and maximum(): Find Extreme Values in Arrays

2022-07-13T14:00:00+00:00

The NumPy library supports expressive, efficient numerical programming in Python. Finding extreme values is a very common requirement in data analysis. The NumPy max() and maximum() functions are two examples of how NumPy lets you combine the coding comfort offered by Python with the runtime efficiency you’d expect from C.

In this tutorial, you’ll learn how to:

Use the NumPy max() function
Use the NumPy maximum() function and understand why it’s different from max()
Solve practical problems with these functions
Handle missing values in your data
Apply the same concepts to finding minimum values

This tutorial includes a very short introduction to NumPy, so even if you’ve never used NumPy before, you should be able to jump right in. With the background provided here, you’ll be ready to continue exploring the wealth of functionality to be found in the NumPy library.

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.

NumPy: Numerical Python

NumPy is short for Numerical Python. It’s an open source Python library that enables a wide range of applications in the fields of science, statistics, and data analytics through its support of fast, parallelized computations on multidimensional arrays of numbers. Many of the most popular numerical packages use NumPy as their base library.

Introducing NumPy

The NumPy library is built around a class named np.ndarray and a set of methods and functions that leverage Python syntax for defining and manipulating arrays of any shape or size.

NumPy’s core code for array manipulation is written in C. You can use functions and methods directly on an ndarray as NumPy’s C-based code efficiently loops over all the array elements in the background. NumPy’s high-level syntax means that you can simply and elegantly express complex programs and execute them at high speeds.

You can use a regular Python list to represent an array. However, NumPy arrays are far more efficient than lists, and they’re supported by a huge library of methods and functions. These include mathematical and logical operations, sorting, Fourier transforms, linear algebra, array reshaping, and much more.

Today, NumPy is in widespread use in fields as diverse as astronomy, quantum computing, bioinformatics, and all kinds of engineering.

NumPy is used under the hood as the numerical engine for many other libraries, such as pandas and SciPy. It also integrates easily with visualization libraries like Matplotlib and seaborn.

NumPy is easy to install with your package manager, for example pip or conda. For detailed instructions plus a more extensive introduction to NumPy and its capabilities, take a look at NumPy Tutorial: Your First Steps Into Data Science in Python or the NumPy Absolute Beginner’s Guide.

In this tutorial, you’ll learn how to take your very first steps in using NumPy. You’ll then explore NumPy’s max() and maximum() commands.

Creating and Using NumPy Arrays

You’ll start your investigation with a quick overview of NumPy arrays, the flexible data structure that gives NumPy its versatility and power.

The fundamental building block for any NumPy program is the ndarray. An ndarray is a Python object wrapping an array of numbers. It may, in principle, have any number of dimensions of any size. You can declare an array in several ways. The most straightforward method starts from a regular Python list or tuple:

>>>

>>> import numpy as np
>>> A = np.array([3, 7, 2, 4, 5])
>>> A
array([3, 7, 2, 4, 5])

>>> B = np.array(((1, 4), (1, 5), (9, 2)))
>>> B
array([[1, 4],
       [1, 5],
       [9, 2]])

You’ve imported numpy under the alias np. This is a standard, widespread convention, so you’ll see it in most tutorials and programs. In this example, A is a one-dimensional array of numbers, while B is two-dimensional.

Notice that the np.array() factory function expects a Python list or tuple as its first parameter, so the list or tuple must therefore be wrapped in its own set of brackets or parentheses, respectively. Just throwing in an unwrapped bunch of numbers won’t work:

>>>

>>> np.array(3, 7, 2, 4, 5)
Traceback (most recent call last):
...
TypeError: array() takes from 1 to 2 positional arguments but 5 were given

With this syntax, the interpreter sees five separate positional arguments, so it’s confused.

In your constructor for array B, the nested tuple argument needs an extra pair of parentheses to identify it, in its entirety, as the first parameter of np.array().

Addressing the array elements is straightforward. NumPy’s indices start at zero, like all Python sequences. By convention, a two-dimensional array is displayed so that the first index refers to the row, and the second index refers to the column. So A[0] is the first element of the one-dimensional array A, and B[2, 1] is the second element in the third row of the two-dimensional array B:

Read the full article at https://realpython.com/numpy-max-maximum/ »

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SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files

2022-07-12T14:00:00+00:00

All programs process data in one form or another, and many need to be able to save and retrieve that data from one invocation to the next. Python, SQLite, and SQLAlchemy give your programs database functionality, allowing you to store data in a single file without the need for a database server.

You can achieve similar results using flat files in any number of formats, including CSV, JSON, XML, and even custom formats. Flat files are often human-readable text files—though they can also be binary data—with a structure that can be parsed by a computer program. You’ll explore using SQL databases and flat files for data storage and manipulation and learn how to decide which approach is right for your program.

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

Flat files for data storage
SQL to improve access to persistent data
SQLite for data storage
SQLAlchemy to work with data as Python objects

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Python and TOML: New Best Friends

2022-07-11T14:00:00+00:00

TOML—Tom’s Obvious Minimal Language—is a reasonably new configuration file format that the Python community has embraced over the last couple of years. TOML plays an essential part in the Python ecosystem. Many of your favorite tools rely on TOML for configuration, and you’ll use pyproject.toml when you build and distribute your own packages.

In this tutorial, you’ll learn more about TOML and how you can use it. In particular, you’ll:

Learn and understand the syntax of TOML
Use tomli and tomllib to parse TOML documents
Use tomli_w to write data structures as TOML
Use tomlkit when you need more control over your TOML files

A new module for TOML parsing is being added to Python’s standard library in Python 3.11. Later in this tutorial, you’ll learn how to use this new module. If you want to know more about why tomllib was added to Python, then have a look at the companion tutorial, Python 3.11 Preview: TOML and tomllib.

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Use TOML as a Configuration Format

TOML is short for Tom’s Obvious Minimal Language and is humbly named after its creator, Tom Preston-Werner. It was designed expressly to be a configuration file format that should be “easy to parse into data structures in a wide variety of languages” (Source).

In this section, you’ll start thinking about configuration files and look at what TOML brings to the table.

Configurations and Configuration Files

A configuration is an important part of almost any application or system. It’ll allow you to change settings or behavior without changing the source code. Sometimes you’ll use a configuration to specify information needed to connect to another service like a database or cloud storage. Other times you’ll use configuration settings to allow your users to customize their experience with your project.

Using a configuration file for your project is a good way to separate your code from its settings. It also encourages you to be conscious about which parts of your system are genuinely configurable, giving you a tool to name magic values in your source code. For now, consider this configuration file for a hypothetical tic-tac-toe game:

player_x_color = blue
player_o_color = green
board_size     = 3
server_url     = https://tictactoe.example.com/

You could potentially code this directly in your source code. However, by moving the settings into a separate file, you achieve a few things:

You give explicit names to values.
You provide these values more visibility.
You make it simpler to change the values.

Look more closely at your hypothetical configuration file. Those values are conceptually different. The colors are values that your framework probably supports changing. In other words, if you replaced blue with red, that would be honored without any special handling in your code. You could even consider if it’s worth exposing this configuration to your end users through your front end.

However, the board size may or may not be configurable. A tic-tac-toe game is played on a three-by-three grid. It’s not certain that your logic would still work for other board sizes. It may still make sense to keep the value in your configuration file, both to give a name to the value and to make it visible.

Finally, the project URL is usually essential when deploying your application. It’s not something that a typical user will change, but a power user may want to redeploy your game to a different server.

To be more explicit about these different use cases, you may want to add some organization to your configuration. One popular option is to separate your configuration into additional files, each dealing with a different concern. Another option is to group your configuration values somehow. For example, you can organize your hypothetical configuration file as follows:

[user]
player_x_color = blue
player_o_color = green

[constant]
board_size = 3

[server]
url = https://tictactoe.example.com

The organization of the file makes the role of each configuration item clearer. You can also add comments to the configuration file with instructions to anyone thinking about making changes to it.

Note: The actual format of your configuration file isn’t important for this discussion. The above principles hold independently of how you specify your configuration values. As it happens, the examples that you’ve seen so far can be parsed by Python’s ConfigParser class.

There are many ways for you to specify a configuration. Windows has traditionally used INI files, which resemble your configuration file from above. Unix systems have also relied on plain-text, human-readable configuration files, although the actual format varies between different services.

Over time, more and more applications have come to use well-defined formats like XML, JSON, or YAML for their configuration needs. These formats were designed as data interchange or serialization formats, usually meant for computer communication.

On the other hand, configuration files are often written or edited by humans. Many developers have gotten frustrated with JSON’s strict comma rules when updating their Visual Studio Code settings or with YAML’s nested indentations when setting up a cloud service. Despite their ubiquity, these file formats aren’t the easiest to write by hand.

TOML: Tom’s Obvious Minimal Language

Read the full article at https://realpython.com/python-toml/ »

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Python News: What's New From June 2022

2022-07-06T14:00:00+00:00

June 2022 brought a flurry of exciting news for the Python community! The PSF received funding for a new role focused on security, and held elections for four seats on the board of directors. Results from two important developer surveys were published, and new versions of both Python and some popular packages saw the light of day.

PEP 691 got accepted, extending the Simple API for Python packaging indexes. With the Python 3.12 change page live, you can now start hitting refresh to be the first to know about upcoming features and deprecations in next year’s major Python release.

With summer in full swing, there have also been more in-person meetups in the Python community, and others are still to come.

Let’s dive into the biggest Python news from the past month!

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The Python Software Foundation Has News

The Python Software Foundation (PSF) is the organization behind Python. It aims to “promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers” (Source).

If you haven’t heard of the PSF before, make sure to check out the PSF blog, which you’ll see linked a few times in this news article.

PSF Elects New Board Directors

Elections to fill four seats on the PSF board of directors took place during the second half of June. A term for a seat on the PSF board of directors lasts for three years, and directors are eligible for reelection. To vote in this election, you had to be a registered PSF member with voting rights.

This year, there were a lot of nominees, and the seats were filled with the four nominees who received the most votes:

If you’re curious what it means to serve on the PSF board and what a director’s duties are, then you can watch a video in which three board members describe Life as a Python Software Foundation Director.

Congratulations to the new and returning board directors, and big thanks to all the other nominees for their continued engagement in the Python community.

PSF Releases 2021 Annual Report

This month, the PSF also released its 2021 annual report. Last year marked the twentieth anniversary of the Python Software Foundation, and the thirtieth anniversary for Python itself:

The report highlights how four team members joined the PSF in new roles during 2021. Python packaging and CPython itself received strong support through the dedicated roles of Packaging Project Manager and Developer in Residence. Funding for event grants started up again, with a focus on online events. The PSF also organized PyCon US 2021 as a fully online event.

If you’re curious to learn more, then you can read the full report online.

OpenSSF Funds A Security Role for the Python Ecosystem

Sadly not for the first time, there was another set of malicious packages identified on PyPI. Because Python is used by such a large community worldwide, such attacks can have dangerous and wide-reaching consequences. In the news article linked above, you’ll see that these packages aimed to expose programmers’ secrets.

Therefore, it’s great news that the Open Source Security Foundation (OpenSSF) has decided to fund a new role in the PSF.

The person filling the role will have a chance to positively impact the current state of security in the wider Python ecosytem as they “provide security expertise for Python, the Python Package Index (PyPI), and the rest of the Python ecosystem” (Source).

The grant money will also be used to complete a security audit on critical open source infrastructure in the Python ecosystem, such as PyPI and the CPython source code itself. Having a dedicated role and financing will hopefully go a long way in boosting security for Python’s large global community.

Survey Results Are In!

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

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Using the Python and Operator

2022-07-05T14:00:00+00:00

Python has three Boolean operators, or logical operators: and, or, and not. You can use them to check if certain conditions are met before deciding the execution path your programs will follow. In this video course, you’ll learn about the and operator and how to use it in your code.

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

Understand the logic behind Python’s and operator
Build and understand Boolean and non-Boolean expressions that use the and operator
Use the and operator in Boolean contexts to decide the course of action of your programs
Use the and operator in non-Boolean contexts to make your code more concise

You’ll also code a few practical examples that will help you understand how to use the and operator to approach different problems in a Pythonic way. Even if you don’t use all the features of and, learning about them will allow you to write better and more accurate code.

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Python 3.11 Preview: TOML and tomllib

2022-07-04T14:00:00+00:00

Python 3.11 is getting closer to its final release, which will happen in October 2022. The new version is currently going through beta testing, and you can install it yourself to preview and test some of the new features, including support for reading TOML with the new tomllib module.

TOML is a configuration file format that’s getting more and more popular in the Python ecosystem. This is driven by the adoption of pyproject.toml as the central configuration file in Python packaging. Other important tools, like Black, mypy, and pytest, also use TOML for their configuration.

In this tutorial, you’ll:

Install Python 3.11 beta on your computer, next to your current Python installations
Get familiar with the basics of the TOML format
Read TOML files with the new tomllib module
Write TOML with third-party libraries and learn why this functionality is not included in tomllib
Explore Python 3.11’s new typing features, including the Self and LiteralString types as well as variadic generics

There are many other new features and improvements coming in Python 3.11. Check out what’s new in the changelog for an up-to-date list, and read other Python 3.11 previews on Real Python to learn about other features.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level.

Python 3.11 Beta

A new version of Python is released in October each year. The code is developed and tested over a seventeen-month period before the release date. New features are implemented during the alpha phase. For Python 3.11, seven alpha releases were made between October 2021 and April 2022.

The first beta release of Python 3.11 happened in the early hours of May 8, 2022. Each such pre-release is coordinated by a release manager—currently Pablo Galindo Salgado—and ties together hundreds of commits from Python’s core developers and other volunteers.

This release also marked the feature freeze for the new version. In other words, no new features will be added to Python 3.11 that aren’t already present in Python 3.11.0b1. Instead, the time between the feature freeze and the release date—October 3, 2022—is used to test and solidify the code.

About once a month during the beta phase, Python’s core developers release a new beta version to continue showing off the new features, testing them, and getting early feedback. Currently, the latest beta version of Python 3.11 is 3.11.0b3, released on June 1, 2022.

Note: This tutorial uses the third beta version of Python 3.11. You might experience small differences if you use a later version. However, tomllib builds on a mature library, and you can expect that what you learn in this tutorial will stay the same through the beta phase and in the final release of Python 3.11.

If you’re maintaining your own Python package, then the beta phase is an important period when you should start testing your package with the new version. Together with the community, the core developers want to find and fix as many bugs as possible before the final release.

Cool New Features

Some of the highlights of Python 3.11 include:

Enhanced error messages, which help you more effectively debug your code
Task and exception groups, which streamline the use of asynchronous code and allow programs to raise and handle multiple exceptions at the same time
TOML support, which allows you to parse TOML documents using the standard library
Static typing improvements, which let you annotate your code more precisely
Optimizations, which promise to make Python 3.11 significantly faster than previous versions

There’s a lot to look forward to in Python 3.11! You can already read about the enhanced error messages and task and exception groups in earlier Python 3.11 preview articles.

In this tutorial, you’ll focus on how you can use the new tomllib library to read and parse TOML files. You’ll also get a short peek at some of the typing improvements that’ll be shipping with Python 3.11.

Installation

To play with the code examples in this tutorial, you’ll need to install a version of Python 3.11 onto your system. In this subsection, you’ll learn about a few different ways to do this: using Docker, using pyenv, or installing from source. Pick the one that works best for you and your system.

Note: Beta versions are previews of upcoming features. While most features will work well, you shouldn’t depend on any Python 3.11 beta version in production or anywhere else where potential bugs will have serious consequences.

If you have access to Docker on your system, then you can download the latest version of Python 3.11 by pulling and running the python:3.11-rc-slim Docker image:

$ docker pull python:3.11-rc-slim
3.11-rc-slim: Pulling from library/python
[...]
docker.io/library/python:3.11-rc-slim

$ docker run -it --rm python:3.11-rc-slim

This drops you into a Python 3.11 REPL. Check out Run Python Versions in Docker for more information about working with Python through Docker, including how to run scripts.

The pyenv tool is great for managing different versions of Python on your system, and you can use it to install Python 3.11 beta if you like. It comes with two different versions, one for Windows and one for Linux and macOS. Choose your platform with the switcher below:

Read the full article at https://realpython.com/python311-tomllib/ »

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The Real Python Podcast – Episode #116: Exploring Functional Programming in Python With Bruce Eckel

2022-07-01T12:00:00+00:00

Would you like to explore the functional programming side of Python? What are the advantages of this approach, and what tools are built into the language? This week on the show, author Bruce Eckel talks about functional programming in Python.

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Python Stacks, Queues, and Priority Queues in Practice

2022-06-29T14:00:00+00:00

Queues are the backbone of numerous algorithms found in games, artificial intelligence, satellite navigation, and task scheduling. They’re among the top abstract data types that computer science students learn early in their education. At the same time, software engineers often leverage higher-level message queues to achieve better scalability of a microservice architecture. Plus, using queues in Python is simply fun!

Python provides a few built-in flavors of queues that you’ll see in action in this tutorial. You’re also going to get a quick primer on the theory of queues and their types. Finally, you’ll take a look at some external libraries for connecting to popular message brokers available on major cloud platform providers.

In this tutorial, you’ll learn how to:

Differentiate between various types of queues
Implement the queue data type in Python
Solve practical problems by applying the right queue
Use Python’s thread-safe, asynchronous, and interprocess queues
Integrate Python with distributed message queue brokers through libraries

To get the most out of this tutorial, you should be familiar with Python’s sequence types, such as lists and tuples, and the higher-level collections in the standard library.

You can download the complete source code for this tutorial with the associated sample data by clicking the link in the box below:

Get Source Code: Click here to get access to the source code and sample data that you’ll use to explore queues in Python.

Learning About the Types of Queues

A queue is an abstract data type that represents a sequence of elements arranged according to a set of rules. In this section, you’ll learn about the most common types of queues and their corresponding element arrangement rules. At the very least, every queue provides operations for adding and removing elements in constant time or O(1) using the Big O notation. That means both operations should be instantaneous regardless of the queue’s size.

Some queues may support other, more specific operations. It’s time to learn more about them!

Queue: First-In, First-Out (FIFO)

The word queue can have different meanings depending on the context. However, when people refer to a queue without using any qualifiers, they usually mean a FIFO queue, which resembles a line that you might find at a grocery checkout or tourist attraction:

Note that unlike the line in the photo, where people are clustering side by side, a queue in a strict sense will be single file, with people admitted one at a time.

FIFO is short for first-in, first-out, which describes the flow of elements through the queue. Elements in such a queue will be processed on a first-come, first-served basis, which is how most real-life queues work. To better visualize the element movement in a FIFO queue, have a look at the following animation:

Unbounded FIFO Queue

Notice that, at any given time, a new element is only allowed to join the queue on one end called the tail—which is on the right in this example—while the oldest element must leave the queue from the opposite end. When an element leaves the queue, then all of its followers shift by exactly one position towards the head of the queue. These few rules ensure that elements are processed in the order of their arrival.

Note: You can think of elements in a FIFO queue as cars stopping at a traffic light.

Adding an element to the FIFO queue is commonly referred to as an enqueue operation, while retrieving one from it is known as a dequeue operation. Don’t confuse a dequeue operation with the deque (double-ended queue) data type that you’ll learn about later!

Enqueuing and dequeuing are two independent operations that may be taking place at different speeds. This fact makes FIFO queues the perfect tool for buffering data in streaming scenarios and for scheduling tasks that need to wait until some shared resource becomes available. For example, a web server flooded with HTTP requests might place them in a queue instead of immediately rejecting them with an error.

Note: In programs that leverage concurrency, a FIFO queue often becomes the shared resource itself to facilitate two-way communication between asynchronous workers. By temporarily locking the read or write access to its elements, a blocking queue can elegantly coordinate a pool of producers and a pool of consumers. You’ll find more information about this use case in later sections about queues in multithreading and multiprocessing.

Another point worth noting about the queue depicted above is that it can grow without bounds as new elements arrive. Picture a checkout line stretching to the back of the store during a busy shopping season! In some situations, however, you might prefer to work with a bounded queue that has a fixed capacity known upfront. A bounded queue can help to keep scarce resources under control in two ways:

By irreversibly rejecting elements that don’t fit
By overwriting the oldest element in the queue

Under the first strategy, once a FIFO queue becomes saturated, it won’t take any more elements until others leave the queue to make some space. You can see an animated example of how this works below:

Bounded FIFO Queue (Bounce)

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

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Building a Site Connectivity Checker

2022-06-28T14:00:00+00:00

Building a site connectivity checker in Python is an interesting project to level up your skills. With this project, you’ll integrate knowledge related to handling HTTP requests, creating command-line interfaces (CLI), and organizing your application’s code using common Python project layout practices.

By building this project, you’ll learn how Python’s asynchronous features can help you deal with multiple HTTP requests efficiently.

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

Create command-line interfaces (CLI) using Python’s argparse
Check if a website is online using Python’s http.client from the standard library
Implement synchronous checks for multiple websites
Check if a website is online using the aiohttp third-party library
Implement asynchronous checks for multiple websites

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Build a Tic-Tac-Toe Game With Python and Tkinter

2022-06-27T14:00:00+00:00

Playing computer games is a great way to unwind or challenge yourself. Some people even do it professionally. It’s also fun and educational to build your own computer games. In this tutorial, you’ll build a classic tic-tac-toe game using Python and Tkinter.

With this project, you’ll go through the thought processes required for creating your own game. You’ll also learn how to integrate your diverse programming skills and knowledge to develop a functional and fun computer game.

In this tutorial, you’ll learn how to:

Program the classic tic-tac-toe game’s logic using Python
Create the game’s graphical user interface (GUI) using the Tkinter tool kit
Integrate the game’s logic and GUI into a fully functional computer game

As mentioned, you’ll be using the Tkinter GUI framework from the Python standard library to create your game’s interface. You’ll also use the model-view-controller pattern and an object-oriented approach to organize your code. For more on these concepts, check out the links in the prerequisites.

To download the entire source code for this project, click the link in the box below:

Get Source Code: Click here to get access to the source code that you’ll use to build your tic-tac-toe game.

Demo: A Tic-Tac-Toe Game in Python

In this step-by-step project, you’ll build a tic-tac-toe game in Python. You’ll use the Tkinter tool kit from the Python standard library to create the game’s GUI. In the following demo video, you’ll get a sense of how your game will work once you’ve completed this tutorial:

Your tic-tac-toe game will have an interface that reproduces the classic three-by-three game board. The players will take turns making their moves on a shared device. The game display at the top of the window will show the name of the player who gets to go next.

If a player wins, then the game display will show a winning message with the player’s name or mark (X or O). At the same time, the winning combination of cells will be highlighted on the board.

Finally, the game’s File menu will have options to reset the game if you want to play again or to exit the game when you’re done playing.

If this sounds like a fun project to you, then read on to get started!

Project Overview

Your goal with this project is to create a tic-tac-toe game in Python. For the game interface, you’ll use the Tkinter GUI tool kit, which comes in the standard Python installation as an included battery.

The tic-tac-toe game is for two players. One player plays X and the other plays O. The players take turns placing their marks on a grid of three-by-three cells. If a given player gets three marks in a row horizontally, vertically, or diagonally, then that player wins the game. The game will be tied if no one gets three in a row by the time all the cells are marked.

With these rules in mind, you’ll need to put together the following game components:

The game’s board, which you’ll build with a class called TicTacToeBoard
The game’s logic, which you’ll manage using a class called TicTacToeGame

The game board will work as a mix between view and controller in a model-view-controller design. To build the board, you’ll use a Tkinter window, which you can create by instantiating the tkinter.Tk class. This window will have two main components:

Top display: Shows information about the game’s status
Grid of cells: Represents previous moves and available spaces or cells

You’ll create the game display using a tkinter.Label widget, which allows you to display text and images.

For the grid of cells, you’ll use a series of tkinter.Button widgets arranged in a grid. When a player clicks one of these buttons, the game logic will run to process the player’s move and determine whether there’s a winner. In this case, the game logic will work as the model, which will manage the data, logic, and rules of your game.

Now that you have a general idea of how to build your tic-tac-toe game, you should check out a few knowledge prerequisites that’ll allow you to get the most out of this tutorial.

Prerequisites

Read the full article at https://realpython.com/tic-tac-toe-python/ »

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The Real Python Podcast – Episode #115: Digging Into PyScript & Preventing or Handling Python Errors

2022-06-24T12:00:00+00:00

Have you heard about PyScript? The brand-new framework has the community excited about building interactive Python applications that run entirely within the user's browser. Would you like to dig into the details beyond the "Hello World" examples? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Python mmap: Doing File I/O With Memory Mapping

2022-06-21T14:00:00+00:00

The Zen of Python has a lot of wisdom to offer. One especially useful idea is that “There should be one—and preferably only one—obvious way to do it.” Yet there are multiple ways to do most things in Python, and often for good reason. For example, there are multiple ways to read a file in Python, including the rarely used mmap module.

Python’s mmap provides memory-mapped file input and output (I/O). It allows you to take advantage of lower-level operating system functionality to read files as if they were one large string or array. This can provide significant performance improvements in code that requires a lot of file I/O.

In this video course, you’ll learn:

What kinds of computer memory exist
What problems you can solve with mmap
How use memory mapping to read large files faster
How to change a portion of a file without rewriting the entire file
How to use mmap to share information between multiple processes

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The Real Python Podcast – Episode #114: Getting Started in Python Cybersecurity and Forensics

2022-06-17T12:00:00+00:00

Are you interested in a career in security using Python? Would you like to stay ahead of potential vulnerabilities in your Python applications? This week on the show, James Pleger talks about Python information security, incident response, and forensics.

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Combining Data in pandas With concat() and merge()

2022-06-14T14:00:00+00:00

The Series and DataFrame objects in pandas are powerful tools for exploring and analyzing data. Part of their power comes from a multifaceted approach to combining separate datasets. With pandas, you can merge and concatenate your datasets, allowing you to unify and better understand your data as you analyze it.

In this video course, you’ll learn how and when to combine your data in pandas with:

merge() for combining data on common columns or indices
concat() for combining DataFrames across rows or columns

If you have some experience using DataFrame and Series objects in pandas and you’re ready to learn how to combine them, then this video course will help you do exactly that. If you’re feeling a bit rusty, then you can watch a quick refresher on DataFrames before proceeding.

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The Real Python Podcast – Episode #113: Build Streamlit Data Science Dashboards & Verbose Regex f-Strings

2022-06-10T12:00:00+00:00

Would you like a fast way to share your data science project results as an interactive dashboard instead of a Jupyter notebook? Streamlit is a library for creating simple web apps and dashboards using just Python. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder's Weekly articles and projects.

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Write and Test a Python Function: Interview Practice

2022-06-07T14:00:00+00:00

Whether you’re looking to ace your coding interview or simply to level up your development skills, solving coding challenges can help you grow as a programmer. In this Real Python Code Conversation, Philipp challenges Martin to write a function that doubles each character in a string. Through their conversation, you’ll learn how to tackle a coding challenge and stand out from the pack in your job search.

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

Use functions to write reusable, maintainable code
Communicate your thoughts in a coding interview
Use if __name__ == "__main__" to build multipurpose code
Write tests and use test-driven development

At the end of the course, you’ll get to hear how Martin did with the coding challenge, and you’ll leave with resources for further exploration.

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The Real Python Podcast – Episode #112: Managing Large Python Data Science Projects With Dask

2022-06-03T12:00:00+00:00

What do you do when your data science project doesn't fit within your computer's memory? One solution is to distribute it across multiple worker machines. This week on the show, Guido Imperiale from Coiled talks about Dask and managing large data science projects through distributed computing.

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Data Cleaning With pandas and NumPy

2022-05-31T14:00:00+00:00

Data scientists spend a large amount of their time cleaning datasets so that they’re easier to work with. In fact, the 80/20 rule says that the initial steps of obtaining and cleaning data account for 80% of the time spent on any given project.

So, if you’re just stepping into this field or planning to step into this field, it’s important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers.

In this video course, you’ll leverage Python’s pandas and NumPy libraries to clean data.

Along the way, you’ll learn about:

Dropping unnecessary columns in a DataFrame
Changing the index of a DataFrame
Using .str() methods to clean columns
Renaming columns to a more recognizable set of labels
Skipping unnecessary rows in a CSV file

To get the most out of this tutorial, you should have a basic understanding of the pandas and NumPy libraries, including pandas’ workhorse Series and DataFrame objects, common methods that can be applied to these objects, and NumPy’s NaN values.

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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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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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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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Real Python

The Real Python Podcast – Episode #119: Natural Language Processing and How ML Models Understand Text

Primer on Jinja Templating

Get Started With Jinja

Install Jinja

Managing Attributes With Python's property()

Your Python Coding Environment on Windows: Setup Guide

Setting Up Your Basic Python Coding Environment in Windows

Updating Your Windows Installation

Discovering the Windows Terminal

The Real Python Podcast – Episode #118: Creating Documentation With MkDocs & When to Use a Python dict

Custom Python Dictionaries: Inheriting From dict vs UserDict

Creating Dictionary-Like Classes in Python

Building a Dictionary-Like Class From an Abstract Base Class

Inheriting From the Python Built-in dict Class

Using the Python not Operator

Build a Flashcards App With Django

Demo: Your Django Flashcards App

Project Overview

Prerequisites

Step 1: Prepare Your Django Project

Create a Virtual Environment

The Real Python Podcast – Episode #117: Measuring Python Code Quality, Simplicity, and Maintainability

NumPy's max() and maximum(): Find Extreme Values in Arrays

NumPy: Numerical Python

Introducing NumPy

Creating and Using NumPy Arrays

SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files

Python and TOML: New Best Friends

Use TOML as a Configuration Format

Configurations and Configuration Files

TOML: Tom’s Obvious Minimal Language

Python News: What's New From June 2022

The Python Software Foundation Has News

PSF Elects New Board Directors

PSF Releases 2021 Annual Report

OpenSSF Funds A Security Role for the Python Ecosystem

Survey Results Are In!

Using the Python and Operator

Python 3.11 Preview: TOML and tomllib

Python 3.11 Beta

Cool New Features

Installation

The Real Python Podcast – Episode #116: Exploring Functional Programming in Python With Bruce Eckel

Python Stacks, Queues, and Priority Queues in Practice

Learning About the Types of Queues

Queue: First-In, First-Out (FIFO)

Building a Site Connectivity Checker

Build a Tic-Tac-Toe Game With Python and Tkinter

Demo: A Tic-Tac-Toe Game in Python

Project Overview

Prerequisites

The Real Python Podcast – Episode #115: Digging Into PyScript & Preventing or Handling Python Errors

Python mmap: Doing File I/O With Memory Mapping

The Real Python Podcast – Episode #114: Getting Started in Python Cybersecurity and Forensics

Combining Data in pandas With concat() and merge()

The Real Python Podcast – Episode #113: Build Streamlit Data Science Dashboards & Verbose Regex f-Strings

Write and Test a Python Function: Interview Practice

The Real Python Podcast – Episode #112: Managing Large Python Data Science Projects With Dask

Data Cleaning With pandas and NumPy

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

Exploring Scopes and Closures in Python

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

Inheriting From the Python Built-in `dict` Class