Planet Python

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

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

In this tutorial, 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
• Use appropriate style in your assignment expressions

Note that all walrus operator examples in this tutorial require Python 3.8 or later to work.

Get Your Code: Click here to download the free sample code that shows you how to use Python’s walrus operator.

Take the Quiz: Test your knowledge with our interactive “The Walrus Operator: Python's Assignment Expressions” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

The Walrus Operator: Python's Assignment Expressions

In this quiz, you'll test your understanding of the Python Walrus Operator. This operator was introduced in Python 3.8, and understanding it can help you write more concise and efficient code.

Walrus Operator Fundamentals

First, look at some different terms that programmers use to refer to this new syntax. You’ve already seen a few in this tutorial.

The := operator is officially known as the assignment expression operator. During early discussions, it was dubbed the walrus operator because the := syntax resembles the eyes and tusks of a walrus lying on its side. You may also see the := operator referred to as the colon equals operator. Yet another term used for assignment expressions is named expressions.

Hello, Walrus!

To get a first impression of what assignment expressions are all about, start your REPL and play around with the following code:

Python 1>>> walrus = False 2>>> walrus 3False 4 5>>> (walrus := True) 6True 7>>> walrus 8True Copied!

Line 1 shows a traditional assignment statement where the value False is assigned to walrus. Next, on line 5, you use an assignment expression to assign the value True to walrus. After both lines 1 and 5, you can refer to the assigned values by using the variable name walrus.

You might be wondering why you’re using parentheses on line 5, and you’ll learn why the parentheses are needed later on in this tutorial.

Note: A statement in Python is a unit of code. An expression is a special statement that can be evaluated to some value.

For example, 1 + 2 is an expression that evaluates to the value 3, while number = 1 + 2 is an assignment statement that doesn’t evaluate to a value. Although running the statement number = 1 + 2 doesn’t evaluate to 3, it does assign the value 3 to number.

In Python, you often see simple statements like return statements and import statements, as well as compound statements like if statements and function definitions. These are all statements, not expressions.

There’s a subtle—but important—difference between the two types of assignments with the walrus variable. An assignment expression returns the value, while a traditional assignment doesn’t. You can see this in action when the REPL doesn’t print any value after walrus = False on line 1 but prints out True after the assignment expression on line 5.

You can see another important aspect about walrus operators in this example. Though it might look new, the := operator does not do anything that isn’t possible without it. It only makes certain constructs more convenient and can sometimes communicate the intent of your code more clearly.

Now you have a basic idea of what the := operator is and what it can do. It’s an operator used in assignment expressions, which can return the value being assigned, unlike traditional assignment statements. To get deeper and really learn about the walrus operator, continue reading to see where you should and shouldn’t use it.

Implementation

Like most new features in Python, assignment expressions were introduced through a Python Enhancement Proposal (PEP). PEP 572 describes the motivation for introducing the walrus operator, the details of the syntax, and examples where the := operator can be used to improve your code.

This PEP was originally written by Chris Angelico in February 2018. Following some heated discussion, PEP 572 was accepted by Guido van Rossum in July 2018.

Since then, Guido announced that he was stepping down from his role as benevolent dictator for life (BDFL). Since early 2019, the Python language has been governed by an elected steering council instead.

The walrus operator was implemented by Emily Morehouse, and made available in the first alpha release of Python 3.8.

Motivation Read the full article at https://realpython.com/python-walrus-operator/ »

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In this quiz, you’ll test your understanding of the Python Walrus Operator. This operator, used for assignment expressions, was introduced in Python 3.8 and can be used to assign values to variables as part of an expression.

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TL;DR

We recently started validating that the postal codes used for paid PythonAnywhere accounts match the ones that people’s banks have on file for the card used. This has led to some confusion, in particular because banks handle postal code validation in a complicated way – charges that fail because of this kind of error can show up in your bank app as a payment that then disappears later, or even as a charge followed by a refund. This blog post is to summarise why that is, so hopefully it will make things a bit less confusing!

The long version…

Card fraud is, sadly, a fact of life on the Internet. If you have a website that accepts payments, eventually someone will try to use a stolen card on it. If your site is online for some time, hackers might even start using you to test lists of stolen cards – that is, they don’t want to use your product in particular, they’re just trying each of the cards to find the ones that are valid, so that they can use them elsewhere.

We recently saw an uptick in the number of these “card probers” (as we call them internally) on PythonAnywhere. We have processes in place to identify them, so that we can refund all payments they get through, and report them as fraudulent to Stripe – our card processor – so that the cards in question are harder for them to use on other sites. But this takes time – time which we would much rather spend on building new features for PythonAnywhere.

Looking into the recent charges, we discovered that many of them were using the wrong postal code when testing the cards. The probers had the numbers, the expiry dates, the CVVs, but not the billing addresses. So we re-introduced something that had been disabled on our Stripe account for some time: postal code validation for payments. You may be wondering why it wasn’t enabled already, or why it might even be something that anyone would disable; this blog post is an introduction to why postal codes and card payments can be more complicated than you might think.

The Python Software Foundation made me a PSF fellow member, along with Adam Johnson.

The new and improved AI Assistant for the 2024.2 versions of JetBrains IDEs is now out, featuring smarter and faster AI code completion for Java, Kotlin, and Python; an enhanced UX when working with code in the editor; AI functionality for Git conflict resolution, in-terminal code generation, new customizable prompts, improved test generation, and more.

Don’t have AI Assistant yet? 

To experience the latest enhancements, simply open a project in your preferred JetBrains IDE version 2024.2, click the AI icon on the right toolbar to initiate the installation, and follow the instructions to enable it. 

You can also experience free local AI completion functionality with full line code completion (FLCC) in your IDE of choice, including CLion and Rider starting from 2024.2. Learn more about FLCC in this blog post. 

Faster and smarter cloud code completion

One of the main focuses of this release was to enhance the user experience of AI code completion in JetBrains IDEs. Here are some of the major advances we’ve made in this direction:

JetBrains code completion models for Python, Java, and Kotlin

We’ve significantly improved the quality and reduced the latency of our code completion for Java, Kotlin, and Python. These enhancements are powered by JetBrains’ internally trained large language models. Enhanced locations for cloud completion invocation extend the variety of usage scenarios, while improved suffix matching ensures that the predicted code snippet correctly completes the existing code.

Syntax highlighting for suggested code

Inline code completion suggestions now come with syntax highlighting, improving the readability of the suggested code.

Incremental acceptance of code suggestions

To simplify the process of reviewing suggestions, multiline code suggestions are now displayed only after accepting a single-line suggestion, allowing you to review and accept code gradually. Additionally, if you don’t want to accept an entire suggested line, you can accept it word by word using the same shortcut that you’d typically use to move the caret to the following word (Ctrl+→ for Windows and ⌥→ for macOS).

Seamless interaction of all available code completion types

We have made UX improvements to better integrate AI code completion features into IDE workflows. This includes a reworked UX for multiline completion and the ability to display suggestions alongside basic IDE completions.

Enhanced in-editor code generation

With the latest update, JetBrains IDEs now feature an improved AI code generation experience. Previously, generated code would open in a new tab. Now, it’s displayed directly in the current editor tab, allowing for an immediate review of the generated content. Check it out using the shortcut ⌘\ on macOS or Ctrl+\ on Windows and Linux.

AI chat becomes smarter GPT-4o support

With the new release, AI Assistant now supports the latest GPT-4o model, bringing a boost to the AI Assistant’s chat-related functionalities, such as finding and explaining errors, explaining code, and refactoring.

Chat references and commands

We have introduced chat references and commands to enhance your AI Assistant’s chat experience, giving you more control over your context. Now, you can reference any symbols, allowing you to quickly indicate the context of your query and get more precise responses. Additionally, you can easily mention specific files or uncommitted local changes. Supported commands include /explain and /refactor, allowing you to quickly get explanations or refactor selected code without typing out questions in the chat.

New feature: merge VCS conflicts with AI

When multiple contributors are making changes to the same part of the codebase, and you try to pull your changes, conflicts may arise. To avoid any issues down the line, JetBrains IDEs now provide a tool for reviewing and resolving any such conflicts. Starting from version 2024.2, the Git conflict resolution modal dialog features AI capabilities to assist with merging conflicts. After AI has done its job, you can review the merged result and either accept everything or revert the changes individually. 

New feature: AI-powered command generation in the new Terminal

Generate commands with AI directly in your IDE via the new Terminal tool window. This integration ensures you can efficiently complete command-line tasks without distraction, improving your overall workflow.

Enhanced unit test generation with AI Assistant

Starting from version 2024.2, the Generate Unit Tests action can be invoked not only on methods but also on classes. If a class has multiple methods, the AI will automatically choose the most suitable one for testing. The latest update also includes more customization options for unit test generation. 

Customizable unit test guidelines

Users can set their own unit test guidelines by customizing the test generation prompt in the AI Assistant’s Prompt Library. This allows you to add specific testing rules for Java, Kotlin, JavaScript, Go, Python, PHP, and Ruby.

Adding test cases to existing tests

AI Assistant now supports adding new test cases to existing test files for Java and Kotlin, allowing you to generate new tests using AI.

Сustom prompts for documentation generation 

The latest update to JetBrains IDEs introduces customizable documentation generation prompts. This feature allows the model to generate documentation for a selected code element and inserts it directly into the code. Users can now define the desired content of the generated documentation for different languages and specify various formatting options, such as Javadoc for Java, ensuring the documentation adheres to preferred styles and standards.

Natural Language setting

You can now specify the language in which you want to interact with the AI chat via Settings. After enabling the Natural Language setting, the context of the current chat will be updated, and any new answers generated by the AI will be provided in the user’s chosen language.

Using AI for working with databases

The new release brings AI to a variety of database-specific features within JetBrains IDEs. You can try these out in DataGrip or in a JetBrains IDE of your choice using the bundled Database Tools and SQL plugin.

Get AI assistance when modifying tables

AI Assistant can now help you change the database-specific parameters of a table. Ask AI Assistant to modify a table according to your requirements right in the Modify dialog. Once AI Assistant generates the requested SQL code, you’ll be able to review it in the preview pane of the dialog and then apply the changes.

Explain and fix SQL problems

DataGrip’s code inspections detect various issues with your SQL queries before execution, which are then categorized according to predefined severity levels.

The latest update integrates AI to enhance the comprehension and resolution of SQL problems. For issues with a severity level higher than Weak warning, the AI Assistant offers explanations and fixes. For better context and more accurate suggestions, you can also attach your database schema.

AI Enterprise: unlocking organizational productivity

Are you looking to maximize productivity at an organizational scale? AI Enterprise runs on premises as part of JetBrains IDE Services, ensuring complete control over data and AI operations within your organization’s infrastructure. It also provides AI usage statistics and reports, offering insights into how AI tools are utilized across your development teams. Learn more about AI Enterprise.

Enhance your writing with Grazie, now included in the AI Pro subscription plan

We’re excited to share that Grazie, our AI writing companion for people in tech, is now included in the AI Pro subscription plan. Use Grazie to transform your thoughts into clear, well-articulated writing, with features like instant proofreading, inline text completion, summarization, translation, rephrasing, and more!

Grazie is now available as a plugin for your JetBrains IDEs and as an extension for browsers. While there is a free version, AI Pro subscribers enjoy full volume access to the entire suite of Grazie’s AI features, which is 500 times greater than the basic volume and replenishes weekly.

Explore AI Assistant and share your feedback

You can learn more about AI Assistant’s key features here. However, the best way to explore its capabilities is by trying it out yourself.

As always, we look forward to hearing your feedback. You can also tell us about your experience via the Share your feedback link in the AI Assistant tool window or by submitting feature requests or bug reports in YouTrack.

Happy developing!

#642 – AUGUST 13, 2024
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Happy Pythoning!
This was PyCoder’s Weekly Issue #642.
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