Python is one of the top computer languages used by developers worldwide for creating a variety of programs, from simple to scientific applications. But VS Code does not directly support Python. Therefore, if you want to use Python in VS Code, it is important to add good Python extensions. Luckily, there are many options available. However, the biggest challenge is to find the most complete and suitable extensions for your requirements.

Top 8 VS Code Python Extensions

To simplify your search for the most suitable Python extensions for your needs, we put together a list of the top 8 VS Code Python extensions available on the market:

1. Python (Microsoft)

Python VS Code extension developed by Microsoft is a feature-rich Python extension that is completely free. VS Code will automatically suggest this extension when you start to create a .py file. Its IntelliSense feature enables useful functionality like code auto-completion, navigation, and syntax checking.

When you install it, the Python VS Code extension will automatically add the Pylance extension, which gives you rich language support, and the Jupyter extension for using Jupyter notebooks. To run tests, you can also use unittest or pytest through its Test Explorer feature. Other valuable capabilities include code debugging, formatting, refactoring, and automatic switching between various Python environments.

2. Lightrun

Lightrun is a real-time debugging platform that supports applications written in several languages, including Python, and is available as a VS Code extension. It consists of an intuitive interface for you to add logs, traces, and metrics in real-time for debugging code in production. You can add Lightrun snapshots to explore the stack trace and variables without stopping your live application.

Also, you can add real-time performance metrics to measure your code’s performance and synchronization, which will allow you to find performance bottlenecks in your applications. Lightrun supports multi-instance applications such as microservices and big data workers with a tagging mechanism. Lightrun it a commercial product, but it comes with a 14-day free trial.

3. Python Preview

This VS Code extension helps understand and debug Python code faster by visualizing code execution with animations and graphics. It previews object allocation and stack frames side-by-side with your code editor, even before you start debugging. This Python extension is also 100% free to use.

4. Better Comments

Comments are critical for any code as they help the developers understand the code better. The Better Comments Python extension is slightly different than the others. It focuses solely on making more human-friendly and readable comments for your Python code. With this extension, you can organize your annotations and improve code clarity. You can use several categories and colors to categorize your annotations—for example, Alerts, Queries, TODOs, and Highlights.

You can also mark commented-out code in a different style and set various comment settings as you see fit. This free VS Code extension also supports many other programming languages.

5. Python Test Explorer

When developing an application, testing is a must to maintain code quality, and you will have to use different types of test frameworks. The Python Test Explorer extension for VS Code lets you run Unittest, Pytest, or Testplan tests.

The Test Explorer extension will show you a complete view of tests and test suites along with their state in VS Code’s sidebar. You can easily see which tests are failing and focus on fixing them.

In addition, this VS Code extension supports convenient error reporting. It will indicate tests having errors, and you can see the complete error message by clicking on them. If you are working with multiple project folders in VS Code, it enables you to run tests on such multi-root workspaces.

6. Python Indent

Having the correct indentation is vital when developing in Python, and adding closing brackets can sometimes get cumbersome. The Python Indent extension helps you maintain proper Python indentation in VS Code. This extension adds closing brackets automatically when you press the Tab key, which speeds up coding and enables you to save a lot of your valuable time.

It can also indent keywords, extend comments and trim whitespace lines. This free VS Code Python extension works by registering the Enter key as a keyboard shortcut, though sometimes it can unexpectedly override the Enter behavior.

7. Python Snippets 3

Python Snippets 3 is a helpful VS Code extension that makes Python code snippets available while you are typing. It provides snippets like built-in strings, lists, sets, tuples, and dictionaries. Other code snippets include if/else, for, while, while/else, try/catch, etc.

There are also Python snippets for Object-Oriented Programming concepts such as inheritance, encapsulation, polymorphism, etc. Since this VS Code extension provides many Python code examples, it is helpful for beginners. However, note that this extension can sometimes add incorrect tab spaces.

8. Bracket Pair Colorizer 2 (CoenraadS)

Bracket Pair Colorizer 2 is another VS Code Python extension that lets developers quickly identify which brackets pair with each other and makes it easier to read code. Matching brackets are highlighted with colors, and you can set tokens and colors that you want to use. This free VS Code extension can be even more helpful if your Python code contains nested conditions and loops.

Although marked as deprecated, this extension is still popular, and many users prefer it to the native Python bracket matching functionality that has since been added to VS Code.

Write Better Python in VS Code

The VS Code Python extensions we discussed here provide helpful features like automatic code completion, test running, indentation, useful snippets to learn Python, and adding different kinds of comments. These extensions help make code more accurate, improve readability, and detect bugs in the system.

One of these Python extensions, Lightrun, enables a robust Python debugging experience in production right from VS Code, and if this is what you need, get started with Lightrun today.

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Getting a good grasp on your application, especially when it is distributed across multiple clouds, kubernetes clusters and serverless functions is not an easy fit.

When trying to keep up with everything that’s going on inside the application, Dynatrace’s software intelligence observability solution is an excellent platform for helping developers and DevOps be alerted to the existence of issues on the infrastructure and application levels. Lightrun, the developer-native observability platform, helps practitioners go beyond the realm of static application telemetry – traditionally added during the application’s development phase – and enables the real-time extraction of line-by-line, debugger-grade information from live environments right inside Dynatrace.

In order to get the “full lay of the land” when things inevitably go wrong, it’s important to be able to access any level of granularity of information from all the relevant components that could have been the root cause of the issue at hand. There’s a term that refers to a system that enables its maintainers to achieve that level of comprehension: Observability. 

Observability is best defined as a property of a system – a system that is observable is built in a way that makes it possible to answer any question that might arise about the system without shipping any new code. An observable system is, of course, a great determining factor in whether we can troubleshoot tough bugs quickly and whether our system is considered reliable – faster resolution (usually measured as MTTR – mean time to resolve) is a great indicator for reliability.

When trying to understand what type of information an observable system can produce on its own, it’s best to think about the context where the value of such a system is most apparent – when we’re deep into complex troubleshooting sessions. When tackling tough issues, we often rely on two main types of telemetry data to clarify what is actually happening under the hood: Infrastructure-level information and application-level information. 

The integration between Dynatrace and Lightrun allows us to create full-cycle observability – which takes shape in the following form:

1. We’ll first use Dynatrace’s analysis and alerting capabilities to become aware of issues, then explore the existing information.This will give us a sense into how the machines running our application or the application itself is behaving, and will surface up various problems (like performance degradations).
2. Then, Developers and DevOps can use Lightrun from the IDE to add real-time, on-demand logs, metrics and traces to the running application – without stopping the application or shipping new code. 
3. The information provided by Lightrun is automatically collected by Dynatrace and can be consumed right next to information provided by Dynatrace itself – closing the aforementioned cycle.

Combining both tools to explore issues from various angles provides a level of visibility into the running application that is enough to solve many critical production issues and understand how the live application is behaving in real-time.

Check out this tutorial on how to use Dynatrace with Lightrun to get started.

The post Full-Cycle Observability With Dynatrace and Lightrun appeared first on Lightrun.

Understanding everything that happens inside a production environment is a notoriously difficult task.

Instana’s solution helps developers and DevOps become aware of problems quickly – problems that are rooted in both infrastructure-level information and application-level information. Lightrun, on the other hand, enables practitioners to drill deeper into line-by-line, debugger-grade information from your production systems – enriching the existing information Instana delivers.

When application problems occur in production, it’s important to gain immediate access to information regarding the “full lay of the land”, including all the relevant components that could have been the root cause. There’s a term that refers to that level of comprehension of an application: Observability.

Observability is a property of an application system. An observable system enables DevOps to answer any question about it from outside the system. Observability is a great determining factor in whether we can troubleshoot tough bugs quickly and whether our system is considered reliable. Fast issue resolution (usually measured as MTTR – mean time to resolve) is a great indicator for reliability.

However, Observability is not just “one thing” – there isn’t a single button you can push to get all the information you want. In fact, when tackling tough issues we often rely on various types of telemetry data to clarify what is actually happening under the hood. We can divide this data, broadly, into two levels of granularity: Infrastructure-level information and application-level information.

The integration between Instana and Lightrun allows us to create full-cycle observability, which in practice looks like this:

1. We’ll first use Instana to understand how the machine running our application or our application itself is feeling, and identify various issues (like performance degradations).
2. Then, Developers and DevOps can use Lightrun from the IDE (Internal Development Environment) to add real-time, on-demand logs, metrics and traces to the running application – without stopping the application or shipping new code.
3. The information provided by Lightrun automatically makes its way to Instana and can be consumed right next to information provided by Instana – closing the aforementioned cycle.

These capabilities are important for Development, DevOps, and SRE practitioners for maintaining application performance and reliability.

For DevOps teams, it helps them instantly review live application environment problems that require triage and optimize the procedures for delivering issue remediation.

For SRE teams, it helps them rapidly identify and repair issues that impact application operations, scaling and reliability.

Developers can debug application code, from their IDE, in production, test, and development without stopping the application or installing updates. This can significantly reduce issue MTTR.

Combining these two tools to effectively tag-team the problem is a good idea, and will provide enough visibility into the running application to solve many critical production issues.

Check out this tutorial on how to use Instana with Lightrun to get started

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