Real Python

The Real Python Podcast – Episode #87: Building a Content Aggregator and Working With RSS in Python

2021-11-19T12:00:00+00:00

Have you wanted to work with RSS feeds in Python? Maybe you're looking for a new project to build for your portfolio that uses Django, unit tests, and custom commands. This week on the show, we have Real Python author Ricky White to talk about his recent step-by-step project titled, "Build a Content Aggregator in Python."

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Python News: What's New From October 2021?

2021-11-17T14:00:00+00:00

A culmination of great work done by volunteers worldwide, the release of Python 3.10 dominated the Python community’s news cycle in October 2021. At the same time that this release was making new features available, Python got recognition as the top programming language for the month in the TIOBE Programming Community index.

There are also some new opportunities for you to support the community by participating in the Python Developer Survey and answering the PyCon US 2022 Call for Proposals.

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

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

The Python 3.10 Release

New versions of Python are now released annually. We can look forward to the core developers sharing a lovely goody bag with the rest of us every October. With Python 3.10, which came out of beta on October 4th, everyone had something exciting to anticipate.

Each release of Python has a release manager who’s responsible for coordinating all changes and for building and preparing the files for distribution. The release manager for Python 3.10 and 3.11 is Pablo Galindo Salgado. In a first for Python, he built and released Python live on YouTube.

Python 3.10 Highlights

The new release includes lots of improvements to the language. Among our favorites are improved error messages, simplified syntax for type unions, and structural pattern matching.

Improved error messages will make your life easier, whether you’re a new Python developer or an experienced one. In particular, the feedback that you get when your code isn’t valid Python is more pointed and actionable in Python 3.10 than in previous versions. As an example, consider the following code, where there’s no closing bracket at the end of the first line:

news = ["errors", "types", "patterns"
print(", ".join(news))

In Python 3.9 and earlier, you’ll see the following if you try to run this code:

  File "errors.py", line 2
    print(", ".join(news))
        ^
SyntaxError: invalid syntax

Read the full article at https://realpython.com/python-news-october-2021/ »

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Building Lists With Python's .append()

2021-11-16T14:00:00+00:00

Adding items to a list is a fairly common task in Python, so the language provides a bunch of methods and operators that can help you out with this operation. One of those methods is .append(). With .append(), you can add items to the end of an existing list object. You can also use .append() in a for loop to populate lists programmatically.

In this course, you’ll learn how to:

Work with .append()
Populate lists using .append() and a for loop
Replace .append() with list comprehensions
Work with .append() in array.array() and collections.deque()

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Securely Deploy a Django App With Gunicorn, Nginx, & HTTPS

2021-11-15T14:00:00+00:00

Taking a Django app from development to production is a demanding but rewarding process. This tutorial will take you through that process step by step, providing an in-depth guide that starts at square one with a no-frills Django application and adds in Gunicorn, Nginx, domain registration, and security-focused HTTP headers. After going over this tutorial, you’ll be better equipped to take your Django app into production and serve it to the world.

In this tutorial, you’ll learn:

How you can take your Django app from development to production
How you can host your app on a real-world public domain
How to introduce Gunicorn and Nginx into the request and response chain
How HTTP headers can fortify your site’s HTTPS security

To make the most out of this tutorial, you should have an introductory-level understanding of Python, Django, and the high-level mechanics of HTTP requests.

You can download the Django project used in this tutorial by following the link below:

Get Source Code: Click here to get the companion Django project used in this tutorial.

Starting With Django and WSGIServer

You’ll use Django as the framework at the core of your web app, using it for URL routing, HTML rendering, authentication, administration, and backend logic. In this tutorial, you’ll supplement the Django component with two other layers, Gunicorn and Nginx, in order to serve the application scalably. But before you do that, you’ll need to set up your environment and get the Django application itself up and running.

Setting Up a Cloud Virtual Machine (VM)

First, you’ll need to launch and set up a virtual machine (VM) on which the web application will run. You should familiarize yourself with at least one infrastructure as a service (IaaS) cloud service provider to provision a VM. This section will walk you through the process at a high level but won’t cover every step in detail.

Using a VM to serve a web app is an example of IaaS, where you have full control over the server software. Other options besides IaaS do exist:

A serverless architecture allows you to compose the Django app only and let a separate framework or cloud provider handle the infrastructure side.
A containerized approach allows multiple apps to run independently on the same host operating system.

For this tutorial, though, you’ll use the tried-and-true route of serving Nginx and Django directly on IaaS.

Two popular options for virtual machines are Azure VMs and Amazon EC2. To get more help with launching the instance, you should refer to the documentation for your cloud provider:

For Azure VMs, follow their quickstart guide for creating a Linux virtual machine in the Azure portal.
For Amazon EC2, learn how to get set up.

The Django project and everything else involved in this tutorial sit on a t2.micro Amazon EC2 instance running Ubuntu Server 20.04.

One important component of VM setup is inbound security rules. These are fine-grained rules that control the inbound traffic to your instance. Create the following inbound security rules for initial development, which you’ll modify in production:

Reference	Type	Protocol	Port Range	Source
1	Custom	TCP	8000	`my-laptop-ip-address/32`
2	Custom	All	All	`security-group-id`
3	SSH	TCP	22	`my-laptop-ip-address/32`

Now you’ll walk through these one at a time:

Rule 1 allows TCP over port 8000 from your personal computer’s IPv4 address, allowing you to send requests to your Django app when you serve it in development over port 8000.
Rule 2 allows inbound traffic from network interfaces and instances that are assigned to the same security group, using the security group ID as the source. This is a rule included in the default AWS security group that you should tie to your instance.
Rule 3 allows you to access your VM via SSH from your personal computer.

You’ll also want to add an outbound rule to allow outbound traffic to do things such as install packages:

Type	Protocol	Port Range	Source
Custom	All	All	`0.0.0.0/0`

Tying that all together, your initial AWS security rule set can consist of three inbound rules and one outbound rule. These, in turn, come from three separate security groups—the default group, a group for HTTP access, and a group for SSH access:

From your local computer, you can then SSH into the instance:

Read the full article at https://realpython.com/django-nginx-gunicorn/ »

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The Real Python Podcast – Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming

2021-11-12T12:00:00+00:00

Do you remember the One Laptop Per Child program? What went wrong, and what can we learn from the program's failure? What are the potential pitfalls of charismatic technology, and how can we avoid them when introducing students to programming? This week on the show, former guest Al Sweigart and author Morgan Ames are here to talk about her book "The Charisma Machine - The Life, Death, and Legacy of One Laptop per Child."

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Advanced Visual Studio Code for Python Developers

2021-11-10T14:00:00+00:00

Visual Studio Code, or VS Code for short, is a free and open source code editor by Microsoft. You can use VS Code as a lightweight code editor to make quick changes, or you can configure it as an integrated development environment (IDE) through the use of third-party extensions. In this tutorial, you’re going to look at how to get the most out of VS Code for Python development.

During this tutorial, you’ll learn how you can configure, extend, and optimize VS Code for a more effective and productive Python development environment. After finishing this tutorial, you’ll have a variety of tools to help you be more productive using VS Code. It can be a powerful tool for rapid Python development.

In this tutorial, you’ll learn how to:

Customize your user interface
Run and monitor Python tests
Lint and format your code automatically
Leverage type annotations and Pylance to write code faster with higher accuracy
Configure and utilize both local and remote debugging
Set up data science tools

As usual, this tutorial is full of links, tips, and tricks to help you on your way.

If you haven’t already installed Visual Studio Code or the Python extension, you’ll need both for this tutorial. If you’re just getting started with VS Code, you might want to check out Jon Fincher’s tutorial on Python development in Visual Studio Code before continuing with this one.

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Mastering the Visual Studio Code User Interface

By default, VS Code’s user interface is designed for everyone—whether you’re writing C++, doing front-end development with JavaScript and CSS, or using data science tools like Jupyter Notebooks. In this first section, you’ll explore how to tailor VS Code to support how you work.

Keyboard Shortcuts

For almost everything that you do in VS Code, you can do it directly from the keyboard. You can map all activities in VS Code, whether they’re built-in or provided via extensions, to a keyboard shortcut.

A small subset of built-in commands is already mapped in Keyboard Shortcuts. If you want to learn these default shortcuts, print out the PDF for Windows, macOS, or Linux and pin it next to your monitor.

As a Python developer, a lot of the commands you’ll use in Visual Studio Code are provided from extensions, similar to the Python extension that you already installed. These don’t come with shortcuts mapped by default, but you can configure them using VS Code’s Keyboard Shortcuts editor.

Using the Keyboard Shortcuts Editor

Open the Keyboard Shortcuts editor by going to File → Preferences → Keyboard Shortcuts or Code → Preferences → Keyboard Shortcuts on macOS:

In this editor, you can see a list of all the existing shortcuts along with their details:

Command: The command or action to be run
Keybinding: The sequence of keys to press to trigger this command, with a blank indicating that there’s no mapping
When: Conditions that need to be met for this keybinding to work, with a minus sign (-) indicating that there are no requirements
Source: Where this binding was configured, either defined by Default, User, or Extension, the last one indicating that you provided it by installing a keymap extension

To override an existing binding, right-click on the existing action and click Change Keybinding. To assign a keybinding to a command that has no binding, double-click on it. If the key sequence you press is already assigned to something else, VS Code will warn you with a link to see which commands are mapped to this keybinding.

As an example, type python create terminal in the search box at the top. If no results come up, make sure you have the Python extension installed. Press Enter to assign a keybinding, like Ctrl+Alt+T, and then hit Enter again.

To assign that shortcut to only work when you’re editing Python code, right-click on the shortcut and select Change When Expression. Enter the expression editorLangId == 'python':

There are more conditional operators to choose from if you need something more specific. Here are some examples to get you started:

Use resourceFilename == '__init__.py' when the file you’re editing is __init__.py
Use editorLangId == 'python' when working with Python. If needed, you can also replace 'python' with another language identifier.
Use editorHasSelection for when you’re inside the editor.

Once you’ve configured this extension, open up a Python file in the editor to try it out by pressing your newly assigned keyboard shortcut Ctrl+Alt+T to open up a Python terminal.

Read the full article at https://realpython.com/advanced-visual-studio-code-python/ »

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Using plt.scatter() to Visualize Data in Python

2021-11-09T14:00:00+00:00

An important part of working with data is being able to visualize it. Python has several third-party modules you can use for data visualization. One of the most popular modules is Matplotlib and its submodule pyplot, often referred to using the alias plt. Matplotlib provides a very versatile tool called plt.scatter() that allows you to create both basic and more complex scatter plots.

In this course, you’ll learn how to:

Create a scatter plot using plt.scatter()
Use the required and optional input parameters
Customize scatter plots for basic and more advanced plots
Represent more than two dimensions on a scatter plot

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Build a Command-Line To-Do App With Python and Typer

2021-11-08T14:00:00+00:00

Building an application to manage your to-do list can be an interesting project when you’re learning a new programming language or trying to take your skills to the next level. In this tutorial, you’ll build a functional to-do application for the command line using Python and Typer, which is a relatively young library for creating powerful command-line interface (CLI) applications in almost no time.

With a project like this, you’ll apply a wide set of core programming skills while building a real-world application with real features and requirements.

In this tutorial, you’ll learn how to:

Build a functional to-do application with a Typer CLI in Python
Use Typer to add commands, arguments, and options to your to-do app
Test your Python to-do application with Typer’s CliRunner and pytest

Additionally, you’ll practice your skills related to processing JSON files by using Python’s json module and managing configuration files with Python’s configparser module. With this knowledge, you’ll be ready to start creating CLI applications right away.

You can download the entire code and all the additional resources for this to-do CLI application by clicking the link below and going to the source_code_final/ directory:

Get Source Code: Click here to get the source code you’ll use to build a to-do app for your command line using Python and Typer.

Demo

In this step-by-step project, you’ll build a command-line interface (CLI) application to manage a to-do list. Your application will provide a CLI based on Typer, a modern and versatile library for creating CLI applications.

Before you get started, check out this demo of how your to-do application will look and work once you get to the end of this tutorial. The first part of the demo shows how to get help on working with the app. It also shows how to initialize and configure the app. The rest of the video demonstrates how to interact with the essential features, such as adding, removing, and listing to-dos:

Nice! The application has a user-friendly CLI that allows you to set up the to-do database. Once there, you can add, remove, and complete to-dos using appropriate commands, arguments, and options. If you ever get stuck, then you can ask for help using the --help option with proper arguments.

Do you feel like kicking off this to-do app project? Cool! In the next section, you’ll plan out how to structure the layout of the project and what tools you’ll use to build it.

Project Overview

When you want to start a new application, you typically start by thinking about how you want the app to work. In this tutorial, you’ll build a to-do app for the command line. You’ll call that application rptodo.

You want your application to have a user-friendly command-line interface that allows your users to interact with the app and manage their to-do lists.

To start off, you want your CLI to provide the following global options:

-v or --version shows the current version and exits the application.
--help shows the global help message for the entire application.

You’ll see these same options in many other CLI applications out there. It’s a nice idea to provide them because most users who work with the command line expect to find them in every app.

Regarding managing a to-do list, your application will provide commands to initialize the app, add and remove to-dos, and manage the to-do completion status:

Command	Description
`init`	Initializes the application’s to-do database
`add DESCRIPTION`	Adds a new to-do to the database with a description
`list`	Lists all the to-dos in the database
`complete TODO_ID`	Completes a to-do by setting it as done using its ID
`remove TODO_ID`	Removes a to-do from the database using its ID
`clear`	Removes all the to-dos by clearing the database

These commands provide all the functionality you need to turn your to-do application into a minimum viable product (MVP) so that you can publish it to PyPI or the platform of your choice and start getting feedback from your users.

To provide all these features in your to-do application, you’ll need to complete a few tasks:

Build a command-line interface capable of taking and processing commands, options, and arguments
Select an appropriate data type to represent your to-dos
Implement a way to persistently store your to-do list
Define a way to connect that user interface with the to-do data

These tasks relate well to what is known as the Model-View-Controller design, which is an architectural pattern. In this pattern, the model takes care of the data, the view deals with the user interface, and the controller connects both ends to make the application work.

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

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The Real Python Podcast – Episode #85: Exploring Django Templates, Tags, and Filters

2021-11-05T12:00:00+00:00

Are you getting the most out of the Django framework? It's a powerful web framework if you're not interested in reinventing the wheel. Django includes a useful template system with inheritance for composing reusable HTML. This week on the show, we have previous guest and Real Python author Christopher Trudeau to talk about his recent articles and courses about Django.

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Build a Content Aggregator in Python

2021-11-03T14:00:00+00:00

In this project-based tutorial, you’ll build a content aggregator from scratch using Python and the popular framework Django.

With so much content coming out online daily, it can be time consuming to go to multiple sites and sources to consume information about your favorite subjects. This is why content aggregators are so popular and powerful, as you can use them to view all the latest news and content in one place.

Whether you’re looking for a portfolio project or ways in which to extend future projects beyond simple CRUD capabilities, this tutorial will have something for you.

In this tutorial, you’ll learn:

How to work with RSS feeds
How to create a Django custom management command
How to run your custom command automatically on a schedule
How to use unit tests to test your Django app’s functionality

Click the link below to download the code for this project and follow along as you build your own content aggregator:

Get Source Code: Click here to get the source code you’ll use to build a content aggregator with Django and Python in this tutorial.

Demo: What You’ll Build

You’ll build your own podcast content aggregator in Python named pyCasts! by following this tutorial from start to finish.

The application will be a single web page displaying the latest Python podcast episodes from The Real Python Podcast and the Talk Python to Me Podcast. After completing this tutorial, you can practice what you’ve learned by adding more podcast feeds to the application.

Here’s a quick demo video of how it will look in action:

There are many moving parts behind the scenes that make this work efficiently and effectively in an automated fashion. You’ll learn about all of them in this tutorial. Get ready to delve in.

Project Overview

In order to be able to display the content to the end user, you have several steps to follow:

Set up the project
Build the podcast model
Create the homepage view
Parse a podcast RSS feed
Create a Django custom command
Add additional feeds
Schedule tasks with django-apscheduler

You’ll walk through each of these over the course of this tutorial. Now you’ll look at which technologies and frameworks you’re going to use for the above steps.

In order to fetch the podcast RSS feeds into your application and parse them, you’ll learn how to use the feedparser library. You’ll use this library to extract only the newest episode data from the feed, which you’ll marshall into an Episode model and save to the database with the Django ORM.

You could add this code to a script and manually run it periodically, but that would defeat the point of having an aggregator to save time. Instead, you’ll learn how to use a built-in Django tool called a custom management command. To parse and save the data, you’ll run your code from within Django itself.

With help from the django-apscheduler library, you’ll set a schedule for your function calls, which are also called jobs. You can then use the Django admin panel to view which jobs ran and when. This will ensure that the fetching and parsing of your feed will happen automatically without needing admin intervention.

You’ll then use the Django template engine to show the user the queried context—in other words, the latest episodes.

Prerequisites

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

Python basics
Virtual environment setup and usage
A basic level of HTML and CSS
Django fundamentals, such as its folder structure, URL routing, migrations, and how to create a project and app

You might also find it helpful to have some experience with Bootstrap 4.

Read the full article at https://realpython.com/build-a-content-aggregator-python/ »

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Reading Input and Writing Output in Python

2021-11-02T14:00:00+00:00

You often need to set up a program to communicate with the outside world by obtaining input data from the user and displaying data back to the user. This course will introduce you to reading input and writing output in Python.

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

Take user input from the keyboard with the built-in function input()
Display output to the console with the built-in function print()

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Python's zipapp: Build Executable Zip Applications

2021-11-01T14:00:00+00:00

A Python Zip application is a quick and cool option for you to bundle and distribute an executable application in a single ready-to-run file, which will make your end users’ experience more pleasant. If you want to learn about Python applications and how to create them using zipapp from the standard library, then this tutorial is for you.

You’ll be able to create Python Zip applications as a quick and accessible way to distribute your software products to your end users and clients.

In this tutorial, you’ll learn:

What a Python Zip application is
How Zip applications work internally
How to build Python Zip applications with zipapp
What standalone Python Zip apps are and how to create them
How to create Python Zip apps manually using command-line tools

You’ll also learn about a few third-party libraries for creating Zip applications that overcome some limitations of zipapp.

To better understand this tutorial, you need to know how to structure Python application layouts, run Python scripts, build Python packages, work with Python virtual environments, and install and manage dependencies with pip. You also need to be comfortable using the command line or terminal.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Getting Started With Python Zip Applications

One of the most challenging problems in the Python ecosystem is finding an effective way to distribute executable applications, such as graphical user interface (GUI) and command-line interface (CLI) programs.

Compiled programming languages, such as C, C++, and Go, can generate executable files that you can run directly on different operating systems and architectures. This ability makes it easy for you to distribute software to your end users.

However, Python doesn’t work like that. Python is an interpreted language, which means that you need a suitable Python interpreter to run your applications. There’s no direct way to generate a standalone executable file that doesn’t need an interpreter to run.

There are many solutions out there that aim to solve this issue. You’ll find tools such as PyInstaller, py2exe, py2app, Nuitka, and more. Those tools allow you to create self-contained executable applications that you can distribute to your end users. However, setting these tools up can be a complex and challenging process.

Sometimes you don’t need that extra complexity. You just need to build an executable app from a script or a small program so that you can distribute it to your end users quickly. If your application is small enough and uses pure Python code, then you can be well-served with a Python Zip application.

What Is a Python Zip Application?

PEP 441 – Improving Python ZIP Application Support formalized the idea, terminology, and specification around Python Zip applications. This type of application consists of a single file that uses the ZIP file format and contains code that Python can execute as a program. These applications rely on Python’s ability to run code from ZIP files that have a __main__.py module at their root, which works as an entry-point script.

Python has been able to run scripts from ZIP files since versions 2.6 and 3.0. The steps to achieve that are pretty straightforward. You just need a ZIP file with a __main__.py module at its root. You can then pass that file to Python, which adds it to sys.path and executes __main__.py as a program. Having the application’s archive in sys.path allows you to access its code through Python’s import system.

As a quick example of how all that works, say you’re on a Unix-like operating system, such as Linux or macOS, and you run the following commands:

$ echo 'print("Hello, World!")' > __main__.py

$ zip hello.zip __main__.py
  adding: __main__.py (stored 0%)

$ python ./hello.zip
Hello, World!

You use the echo command to create a __main__.py file containing the code print("Hello, World!"). Then you use the zip command to archive __main__.py into hello.zip. Once you’ve done that, you can run hello.zip as a program by passing the filename as an argument to the python command.

To round up the internal structure of Python Zip applications, you need a way to tell the operating system how to execute them. The ZIP file format allows you to prepend arbitrary data at the beginning of a ZIP archive. Python Zip applications take advantage of that feature to include a standard Unix shebang line in the application’s archive:

#!/usr/bin/env python3

On Unix systems, this line tells the operating system which program to use for executing the file at hand so that you can run the file directly without the python command. On Windows systems, the Python launcher properly understands the shebang line and runs the Zip application for you.

Even with a shebang line, you can always execute a Python Zip application by passing the application’s filename as an argument to the python command.

In summary, to build a Python Zip application, you need:

An archive that uses the standard ZIP file format and contains a __main__.py module at its root
An optional shebang line that specifies the appropriate Python interpreter to run the application

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

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The Real Python Podcast – Episode #84: Creating and Manipulating PDFs in Python With borb

2021-10-29T12:00:00+00:00

Have you wanted to generate PDFs from your Python project? Many of the current libraries require designing the document down at the pixel level. Would you be interested in a tool that lets you specify the page layout while it handles the specific details of laying out the text? This week on the show, we talk with Joris Schellekens about his library for creating and manipulating PDFs named borb.

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Django Templates: Implementing Custom Tags and Filters

2021-10-27T14:00:00+00:00

Django templates help you manage your web application’s HTML. Templates use a mini-language with variables, tags, and filters. You can conditionally include blocks, create loops, and modify variables before they’re shown. Django comes with many built-in tags and filters, but what if they’re not enough? In that case, write your own! This tutorial covers the ins and outs of writing your own Django template custom tags and filters.

In this tutorial, you’ll learn how to:

Write and register a function as a custom filter
Understand how autoescaping works in custom tags and filters
Use @simple_tag to write a custom template tag
Use @inclusion_tag to render a tag based on a subtemplate
Write a complex template tag with a parser and renderer

By the end of the tutorial, you’ll be able to write custom filters to modify data in your templates and custom tags that give you access to the full power of Python within your templates.

Free Bonus: Click here to get access to a free Django Learning Resources Guide (PDF) that shows you tips and tricks as well as common pitfalls to avoid when building Python + Django web applications.

Getting Started

To play around with your own Django template custom tags and filters, you’re going to need a Django project. You’ll build dinosoar, a small website with all sorts of dinosaur info. Although the name implies that you’ll only include flying dinos, that’s just for marketing spin. All your favorite heavyweights will be there as well.

If you’ve never set up a Django project before or if you need a refresher, you may want to read Get Started With Django Part 1: Build a Portfolio App first.

Django is a third-party library, so it should be installed in a virtual environment. If you’re new to virtual environments, check out Python Virtual Environments: A Primer. Create and activate a new virtual environment for yourself and then run the following commands:

 1$ python -m pip install django==3.2.5
 2$ django-admin startproject dinosoar
 3$ cd dinosoar
 4$ python manage.py startapp dinofacts
 5$ python manage.py migrate

These commands perform the following actions:

Line 1 runs the pip command to install Django.
Line 2 creates your new Django project.
Line 3 changes the current working directory to the dinosoar project.
Line 4 uses the manage.py command to create a Django app called dinofacts, where your main view will live.
Line 5 migrates any database changes. Even if you aren’t creating models, this line is necessary because the Django admin is active by default.

With the project created, it’s time to make some configuration changes and write a quick view to help you test your custom tags and filters.

Setting Up a Django Project

You need to make some changes to your project’s settings to make Django aware of your newly created app and to configure your templates. Edit dinosoar/dinosoar/settings.py and add dinofacts to the INSTALLED_APPS list:

34# dinosoar/dinosoar/settings.py
35
36INSTALLED_APPS = [
37    "django.contrib.admin",
38    "django.contrib.auth",
39    "django.contrib.contenttypes",
40    "django.contrib.sessions",
41    "django.contrib.messages",
42    "django.contrib.staticfiles",
43    "dinofacts",
44]

Within the same file, you’ll need to update the DIR value in the TEMPLATES attribute. This tells Django where to look for your template files:

57# dinosoar/dinosoar/settings.py
58
59TEMPLATES = [
60    {
61        "BACKEND": "django.template.backends.django.DjangoTemplates",
62        "DIRS": [
63            BASE_DIR / "templates",
64        ],
65        "APP_DIRS": True,
66        "OPTIONS": {
67            "context_processors": [
68                "django.template.context_processors.debug",
69                "django.template.context_processors.request",
70                "django.contrib.auth.context_processors.auth",
71                "django.contrib.messages.context_processors.messages",
72            ],
73        },

Starting with Django 3.1, the BASE_DIR value that specifies where the project lives is a pathlib object. The change to the DIRS value above tells Django to look in a templates/ subdirectory within your project directory.

Note: If you use Django 3.0 or earlier, you’ll set BASE_DIR using the os.path module. In that case, use os.path.join() to specify the path.

With the settings changed, don’t forget to create the templates/ directory within your project:

$ pwd
/home/realpython/dinosoar
$ mkdir templates

It’s time to start writing some code. To test your custom template tags and filters, you’ll need a view. Edit dinosoar/dinofacts/views.py as follows:

 1# dinosoar/dinofacts/views.py
 2
 3from datetime import datetime
 4from django.shortcuts import render
 5
 6def show_dino(request, name):
 7    data = {
 8        "dinosaurs": [
 9            "Tyrannosaurus",
10            "Stegosaurus",
11            "Raptor",
12            "Triceratops",
13        ],
14        "now": datetime.now(),
15    }
16
17    return render(request, name + ".html", data)

Lines 7 to 15 create a dictionary with some sample data. You’ll use this in your templates to test your tags and filters. The rest of this view does something a little unorthodox: it takes a parameter that specifies the name of a template.

Read the full article at https://realpython.com/django-template-custom-tags-filters/ »

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Writing Idiomatic Python

2021-10-26T14:00:00+00:00

What programming idioms are unique to Python? This course is both a short overview for people coming from other languages as well as an introduction for programming beginners to the idiomatic practices within Python.

In this course, you’ll learn:

How to access and interpret The Zen of Python
How to set up a script
How to test truth values
How to swap variables in-place
How to create Pythonic for loops

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Using the "not" Boolean Operator in Python

2021-10-25T14:00:00+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 tutorial, 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

You’ll also code a few practical examples that will allow you to better understand some of the primary use cases of the not operator and the best practices around its use. To get the most out of this tutorial, you should have some previous knowledge about Boolean logic, conditional statements, and while loops.

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Working With Boolean Logic in Python

George Boole put together what is now known as Boolean algebra, which relies on true and false values. It also defines a set of Boolean operations: AND, OR, and NOT. These Boolean values and operators are helpful in programming because they help you decide the course of action in your programs.

In Python, the Boolean type, bool, is a subclass of int:

>>>

>>> issubclass(bool, int)
True
>>> help(bool)
Help on class bool in module builtins:

class bool(int)
    bool(x) -> bool
    ...

This type has two possible values, True and False, which are built-in constants in Python and must be capitalized. Internally, Python implements them as integer numbers:

>>>

>>> type(True)
<class 'bool'>
>>> type(False)
<class 'bool'>

>>> isinstance(True, int)
True
>>> isinstance(False, int)
True

>>> int(True)
1
>>> int(False)
0

Python internally implements its Boolean values as 1 for True and 0 for False. Go ahead and execute True + True in your interactive shell to see what happens.

Python provides three Boolean or logical operators:

Operator	Logical Operation
`and`	Conjunction
`or`	Disjunction
`not`	Negation

With these operators, you can build expressions by connecting Boolean expressions with each other, objects with each other, and even Boolean expressions with objects. Python uses English words for the Boolean operators. These words are keywords of the language, so you can’t use them as identifiers without causing a syntax error.

In this tutorial, you’ll learn about Python’s not operator, which implements the logical NOT operation or negation.

Getting Started With Python’s `not` Operator

The not operator is the Boolean or logical operator that implements negation in Python. It’s unary, which means that it takes only one operand. The operand can be a Boolean expression or any Python object. Even user-defined objects work. The task of not is to reverse the truth value of its operand.

If you apply not to an operand that evaluates to True, then you get False as a result. If you apply not to a false operand, then you get True:

>>>

>>> not True
False

>>> not False
True

The not operator negates the truth value of its operand. A true operand returns False. A false operand returns True. These two statements uncover what is commonly known as the truth table of not:

`operand`	`not operand`
`True`	`False`
`False`	`True`

With not, you can negate the truth value of any Boolean expression or object. This functionality makes it worthwhile in several situations:

Checking unmet conditions in the context of if statements and while loops
Inverting the truth value of an object or expression
Checking if a value is not in a given container
Checking for an object’s identity

Read the full article at https://realpython.com/python-not-operator/ »

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The Real Python Podcast – Episode #83: Ready to Publish Your Python Packages?

2021-10-22T12:00:00+00:00

Are you interested in sharing your Python project with the broader world? Would you like to make it easily installable using pip? How do you create Python packages that share your code in a scalable and maintainable way? This week on the show, Real Python author and former guest Dane Hillard returns to talk about his new book, "Publishing Python Packages."

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Using the len() Function in Python

2021-10-20T14:00:00+00:00

In many situations, you’ll need to find the number of items stored in a data structure. Python’s built-in function len() is the tool that will help you with this task.

There are some cases in which the use of len() is straightforward. However, there are other times when you’ll need to understand how this function works in more detail and how to apply it to different data types.

In this tutorial, you’ll learn how to:

Find the length of built-in data types using len()
Use len() with third-party data types
Provide support for len() with user-defined classes

By the end of this article, you’ll know when to use the len() Python function and how to use it effectively. You’ll know which built-in data types are valid arguments for len() and which ones you can’t use. You’ll also understand how to use len() with third-party types, such as ndarray in NumPy and DataFrame in pandas, and with your own classes.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Getting Started With Python’s `len()`

The function len() is one of Python’s built-in functions. It returns the length of an object. For example, it can return the number of items in a list. You can use the function with many different data types. However, not all data types are valid arguments for len().

You can start by looking at the help for this function:

>>>

>>> help(len)
Help on built-in function len in module builtins:
len(obj, /)
    Return the number of items in a container.

The function takes an object as an argument and returns the length of that object. The documentation for len() goes a bit further:

Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). (Source)

When you use built-in data types and many third-party types with len(), the function doesn’t need to iterate through the data structure. The length of a container object is stored as an attribute of the object. The value of this attribute is modified each time items are added to or removed from the data structure, and len() returns the value of the length attribute. This ensures that len() works efficiently.

In the following sections, you’ll learn about how to use len() with sequences and collections. You’ll also learn about some data types that you cannot use as arguments for the len() Python function.

Using `len()` With Built-in Sequences

A sequence is a container with ordered items. Lists, tuples, and strings are three of the basic built-in sequences in Python. You can find the length of a sequence by calling len():

>>>

>>> greeting = "Good Day!"
>>> len(greeting)
9

>>> office_days = ["Tuesday", "Thursday", "Friday"]
>>> len(office_days)
3

>>> london_coordinates = (51.50722, -0.1275)
>>> len(london_coordinates)
2

When finding the length of the string greeting, the list office_days, and the tuple london_coordinates, you use len() in the same manner. All three data types are valid arguments for len().

The function len() always returns an integer as it’s counting the number of items in the object that you pass to it. The function returns 0 if the argument is an empty sequence:

>>>

>>> len("")
0
>>> len([])
0
>>> len(())
0

In the examples above, you find the length of an empty string, an empty list, and an empty tuple. The function returns 0 in each case.

A range object is also a sequence that you can create using range(). A range object doesn’t store all the values but generates them when they’re needed. However, you can still find the length of a range object using len():

>>>

>>> len(range(1, 20, 2))
10

This range of numbers includes the integers from 1 to 19 with increments of 2. The length of a range object can be determined from the start, stop, and step values.

In this section, you’ve used the len() Python function with strings, lists, tuples, and range objects. However, you can also use the function with any other built-in sequence.

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

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Python Assignment Expressions and Using the Walrus Operator

2021-10-19T14:00:00+00:00

Each new version of Python adds new features to the language. For Python 3.8, the biggest change is the addition of assignment expressions. Specifically, the := operator gives you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator.

This course is an in-depth introduction to the walrus operator. You’ll learn some of the motivations for the syntax update and explore some examples where assignment expressions can be useful.

In this course, you’ll learn how to:

Identify the walrus operator and understand its meaning
Understand use cases for the walrus operator
Avoid repetitive code by using the walrus operator
Convert between code using the walrus operator and code using other assignment methods
Understand the impacts on backward compatibility when using the walrus operator
Use appropriate style in your assignment expressions

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A Roadmap to XML Parsers in Python

2021-10-18T14:00:00+00:00

If you’ve ever tried to parse an XML document in Python before, then you know how surprisingly difficult such a task can be. On the one hand, the Zen of Python promises only one obvious way to achieve your goal. At the same time, the standard library follows the batteries included motto by letting you choose from not one but several XML parsers. Luckily, the Python community solved this surplus problem by creating even more XML parsing libraries.

Jokes aside, all XML parsers have their place in a world full of smaller or bigger challenges. It’s worthwhile to familiarize yourself with the available tools.

In this tutorial, you’ll learn how to:

Choose the right XML parsing model
Use the XML parsers in the standard library
Use major XML parsing libraries
Parse XML documents declaratively using data binding
Use safe XML parsers to eliminate security vulnerabilities

You can use this tutorial as a roadmap to guide you through the confusing world of XML parsers in Python. By the end of it, you’ll be able to pick the right XML parser for a given problem. To get the most out of this tutorial, you should already be familiar with XML and its building blocks, as well as how to work with files in Python.

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Choose the Right XML Parsing Model

It turns out that you can process XML documents using a few language-agnostic strategies. Each demonstrates different memory and speed trade-offs, which can partially justify the wide range of XML parsers available in Python. In the following section, you’ll find out their differences and strengths.

Document Object Model (DOM)

Historically, the first and the most widespread model for parsing XML has been the DOM, or the Document Object Model, originally defined by the World Wide Web Consortium (W3C). You might have already heard about the DOM because web browsers expose a DOM interface through JavaScript to let you manipulate the HTML code of your websites. Both XML and HTML belong to the same family of markup languages, which makes parsing XML with the DOM possible.

The DOM is arguably the most straightforward and versatile model to use. It defines a handful of standard operations for traversing and modifying document elements arranged in a hierarchy of objects. An abstract representation of the entire document tree is stored in memory, giving you random access to the individual elements.

While the DOM tree allows for fast and omnidirectional navigation, building its abstract representation in the first place can be time-consuming. Moreover, the XML gets parsed at once, as a whole, so it has to be reasonably small to fit the available memory. This renders the DOM suitable only for moderately large configuration files rather than multi-gigabyte XML databases.

Use a DOM parser when convenience is more important than processing time and when memory is not an issue. Some typical use cases are when you need to parse a relatively small document or when you only need to do the parsing infrequently.

Simple API for XML (SAX)

To address the shortcomings of the DOM, the Java community came up with a library through a collaborative effort, which then became an alternative model for parsing XML in other languages. There was no formal specification, only organic discussions on a mailing list. The end result was an event-based streaming API that operates sequentially on individual elements rather than the whole tree.

Elements are processed from top to bottom in the same order they appear in the document. The parser triggers user-defined callbacks to handle specific XML nodes as it finds them in the document. This approach is known as “push” parsing because elements are pushed to your functions by the parser.

SAX also lets you discard elements if you’re not interested in them. This means it has a much lower memory footprint than DOM and can deal with arbitrarily large files, which is great for single-pass processing such as indexing, conversion to other formats, and so on.

However, finding or modifying random tree nodes is cumbersome because it usually requires multiple passes on the document and tracking the visited nodes. SAX is also inconvenient for handling deeply nested elements. Finally, the SAX model just allows for read-only parsing.

In short, SAX is cheap in terms of space and time but more difficult to use than DOM in most cases. It works well for parsing very large documents or parsing incoming XML data in real time.

Streaming API for XML (StAX)

Although somewhat less popular in Python, this third approach to parsing XML builds on top of SAX. It extends the idea of streaming but uses a “pull” parsing model instead, which gives you more control. You can think of StAX as an iterator advancing a cursor object through an XML document, where custom handlers call the parser on demand and not the other way around.

Note: It’s possible to combine more than one XML parsing model. For example, you can use SAX or StAX to quickly find an interesting piece of data in the document and then build a DOM representation of only that particular branch in memory.

Using StAX gives you more control over the parsing process and allows for more convenient state management. The events in the stream are only consumed when requested, enabling lazy evaluation. Other than that, its performance should be on par with SAX, depending on the parser implementation.

Learn About XML Parsers in Python’s Standard Library

In this section, you’ll take a look at Python’s built-in XML parsers, which are available to you in nearly every Python distribution. You’re going to compare those parsers against a sample Scalable Vector Graphics (SVG) image, which is an XML-based format. By processing the same document with different parsers, you’ll be able to choose the one that suits you best.

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

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The Real Python Podcast – Episode #82: Welcoming the CPython Developer in Residence

2021-10-15T12:00:00+00:00

Earlier this year, the Python Software Foundation announced the creation of the Developer in Residence role. The first Visionary Sponsors of the PSF have provided funding for this new role for one year. What development responsibilities does this job address? This week on the show, we talk to previous guest Łukasz Langa about becoming the first CPython Developer in Residence.

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Using Pygame to Build an Asteroids Game in Python

2021-10-12T14:00:00+00:00

Do you want to create your own computer games but like Python too much to abandon it for a career as a game developer? There’s a solution for that! With the Pygame module, you can use your amazing Python skills to create games, from the basic to the very complex. Below, you’ll learn how to use Pygame by making a clone of the Asteroids game!

In this course, you’ll learn how to build a complete game, including:

Loading images and displaying them on the screen
Handling user input in order to control the game
Moving objects according to the game logic
Detecting collisions between objects
Displaying text on the screen
Playing sounds

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The Real Python Podcast – Episode #81: Exploring the New Features of Python 3.10

2021-10-08T12:00:00+00:00

Python 3.10 is here! This week on the show, two former guests and Real Python authors return to talk about the new version. Geir Arne Hjelle's article was posted to the site Monday, and it's titled "Python 3.10: Cool New Features for You to Try". Christopher Trudeau's video course came out on Tuesday, and it covers the topics from the article with multiple visual examples of Python 3.10 code.

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Cool New Features in Python 3.10

2021-10-05T14:00:00+00:00

Python 3.10 is out! Volunteers have been working on the new version since May 2020 to bring you a better, faster, and more secure Python. As of October 4, 2021, the first official version is available.

Each new version of Python brings a host of changes. You can read about all of them in the documentation. Here, you’ll get to learn about the coolest new features.

In this course, you’ll learn about:

Debugging with more helpful and precise error messages
Using structural pattern matching to work with data structures
Adding more readable and more specific type hints
Checking the length of sequences when using zip()
Calculating multivariable statistics

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The Real Python Podcast – Episode #80: Make Your Python App Interactive With a Text User Interface (TUI)

2021-10-01T12:00:00+00:00

Have you wanted to create a Python application that goes further than a command-line interface? You would like it to have a friendly interface but don't want to make a GUI (Graphical User Interface) or web application. Maybe a TUI (Text User Interface)would be a perfect fit for the project. This week on the show, we have Will McGugan to talk about his projects Textual and Rich.

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Rock, Paper, Scissors With Python: A Command Line Game

2021-09-28T14:00:00+00:00

Game programming is a great way to learn how to program. You use many tools that you’ll see in the real world, plus you get to play a game to test your results! An ideal game to start your Python game programming journey is rock paper scissors.

In this course, you’ll learn how to:

Code your own rock paper scissors game
Take in user input with input()
Play several games in a row using a while loop
Clean up your code with Enum objects and functions
Define more complex rules with a dictionary

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The Real Python Podcast – Episode #79: Measuring Your Python Learning Progress

2021-09-24T12:00:00+00:00

Where are you along the path of learning Python? Do you feel like you're making progress? What are ways you can put the learning path into a more precise focus? This week on the show, we talk with previous guest Martin Breuss about his recent article "How Long Does It Take to Learn Python?"

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Pass by Reference in Python: Best Practices

2021-09-21T14:00:00+00:00

After gaining some familiarity with Python, you may notice cases in which your functions don’t modify arguments in place as you might expect, especially if you’re familiar with other programming languages. Some languages handle function arguments as references to existing variables, which is known as pass by reference. Other languages handle them as independent values, an approach known as pass by value.

If you’re an intermediate Python programmer who wishes to understand Python’s peculiar way of handling function arguments, then this course is for you. You’ll implement real use cases of pass-by-reference constructs in Python and learn several best practices to avoid pitfalls with your function arguments.

In this course, you’ll learn:

What it means to pass by reference and why you’d want to do so
How passing by reference differs from both passing by value and Python’s unique approach
How function arguments behave in Python
How you can use certain mutable types to pass by reference in Python
What the best practices are for replicating pass by reference in Python

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The Real Python Podcast – Episode #78: Learning Python Through Illustrated Stories

2021-09-17T12:00:00+00:00

Are you a visual learner? Does it help to have programming concepts shared with concrete examples and images? Would you like to see if your child might be interested in programming? This week on the show, we talk with author Shari Eskenas about her books, "A Day in Code - Python: Learn to Code in Python Through an Illustrated Story" and "Learn Python Through Nursery Rhymes & Fairy Tales."

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Using Data Classes in Python

2021-09-14T14:00:00+00:00

One new and exciting feature that came out in Python 3.7 was the data class. A data class is a class typically containing mainly data, although there aren’t really any restrictions.

With data classes, you don’t have to write boilerplate code to get proper initialization, representation, and comparisons for your objects.

In this course, you’ll learn how to:

Define your own data classes
Add default values to the fields in your data class
Customize the ordering of data class objects
Work with immutable data classes

[ 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 ]

Real Python

The Real Python Podcast – Episode #87: Building a Content Aggregator and Working With RSS in Python

Python News: What's New From October 2021?

The Python 3.10 Release

Python 3.10 Highlights

Building Lists With Python's .append()

Securely Deploy a Django App With Gunicorn, Nginx, & HTTPS

Starting With Django and WSGIServer

Setting Up a Cloud Virtual Machine (VM)

The Real Python Podcast – Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming

Advanced Visual Studio Code for Python Developers

Mastering the Visual Studio Code User Interface

Keyboard Shortcuts

Using the Keyboard Shortcuts Editor

Using plt.scatter() to Visualize Data in Python

Build a Command-Line To-Do App With Python and Typer

Demo

Project Overview

The Real Python Podcast – Episode #85: Exploring Django Templates, Tags, and Filters

Build a Content Aggregator in Python

Demo: What You’ll Build

Project Overview

Prerequisites

Reading Input and Writing Output in Python

Python's zipapp: Build Executable Zip Applications

Getting Started With Python Zip Applications

What Is a Python Zip Application?

The Real Python Podcast – Episode #84: Creating and Manipulating PDFs in Python With borb

Django Templates: Implementing Custom Tags and Filters

Getting Started

Setting Up a Django Project

Writing Idiomatic Python

Using the "not" Boolean Operator in Python

Working With Boolean Logic in Python

Getting Started With Python’s not Operator

The Real Python Podcast – Episode #83: Ready to Publish Your Python Packages?

Using the len() Function in Python

Getting Started With Python’s len()

Using len() With Built-in Sequences

Python Assignment Expressions and Using the Walrus Operator

A Roadmap to XML Parsers in Python

Choose the Right XML Parsing Model

Document Object Model (DOM)

Simple API for XML (SAX)

Streaming API for XML (StAX)

Learn About XML Parsers in Python’s Standard Library

The Real Python Podcast – Episode #82: Welcoming the CPython Developer in Residence

Using Pygame to Build an Asteroids Game in Python

The Real Python Podcast – Episode #81: Exploring the New Features of Python 3.10

Cool New Features in Python 3.10

The Real Python Podcast – Episode #80: Make Your Python App Interactive With a Text User Interface (TUI)

Rock, Paper, Scissors With Python: A Command Line Game

The Real Python Podcast – Episode #79: Measuring Your Python Learning Progress

Pass by Reference in Python: Best Practices

The Real Python Podcast – Episode #78: Learning Python Through Illustrated Stories

Using Data Classes in Python

Getting Started With Python’s `not` Operator

Getting Started With Python’s `len()`

Using `len()` With Built-in Sequences