Code Visual Studio Python | Installing Essential Vscode Python Extensions

Python in Visual Studio Code

Working with Python in Visual Studio Code, using the Microsoft Python extension, is simple, fun, and productive. The extension makes VS Code an excellent Python editor, and works on any operating system with a variety of Python interpreters. It leverages all of VS Code’s power to provide auto complete and IntelliSense, linting, debugging, and unit testing, along with the ability to easily switch between Python environments, including virtual and conda environments.

This article provides only an overview of the different capabilities of the Python extension for VS Code. For a walkthrough of editing, running, and debugging code, use the button below.

Intro to CS at Harvey Mudd College

Professor Zachary Dodds is a Computer Science professor at Harvey Mudd College who teaches several introductory classes both for students new to Computer Science and students from a non-Computer Science background. He co-created the popular introduction to Computer Science class CS5, which attracts students from all backgrounds to develop programming and problem-solving skills and to build “a coherent, intellectually compelling picture of Computer Science”. The class is taught with Python and uses VS Code as the recommended editor.

Why Visual Studio Code?

Professor Dodds has been recommending and using Visual Studio Code in his classes since it debuted in 2015.

“Visual Studio Code is the best balance of authenticity and accessibility… Visual Studio Code doesn’t feel ‘fake’, it’s what real software developers use. Plus, Visual Studio Code works on every OS!”

VS Code runs on Windows, macOS, Linux, and even Chromebooks.

Classroom settings

Since VS Code is easy to customize, Professor Dodds is able to tailor the editor for his students, preferring to hide IntelliSense, or code completion suggestions, so they can learn from what they type and reinforce the conceptual models being built.

Here are the settings his students use:


"editor.quickSuggestions": false, "editor.acceptSuggestionOnCommitCharacter": false, "editor.suggest.filterGraceful": true, "editor.suggestOnTriggerCharacters": false, "editor.acceptSuggestionOnEnter": "on", "editor.suggest.showIcons": false, "editor.suggest.maxVisibleSuggestions": 7, "editor.hover.enabled": false, "editor.hover.sticky": false, "editor.suggest.snippetsPreventQuickSuggestions": false, "editor.parameterHints.enabled": false, "editor.wordBasedSuggestions": "matchingDocuments", "editor.tabCompletion": "on", "extensions.ignoreRecommendations": true, "files.autoSave": "afterDelay",


You can find the most up-to-date settings on his course website: CS5 – Python Tips.

Integrated Terminal

Professor Dodds also utilizes the built-in terminal heavily in his class as an introduction to running programs from the command line and navigating around their machine all within Visual Studio Code. He appreciates how “the built-in terminal panel does not try to automate too much (which, if it did, would deprive newcomers of the experience of the information-flow that’s going on).”

In the video below, the student does all of their command line and coding work in one place, such as installing Python libraries, while working on Lab 3 from the CS5 class:

Thank you, Professor Dodds, for sharing your story! If you’re interested in using VS Code to teach Python in your classes, you can get started with the Python Education Extension Pack below!

Debugging

No more

For more specific information on debugging in Python, such as configuring your

launch.json

settings and implementing remote debugging, see Debugging. General VS Code debugging information is found in the debugging document.

Additionally, the Django and Flask tutorials provide examples of how to implement debugging in the context of web applications, including debugging Django templates.

Create a virtual environment

A best practice among Python developers is to use a project-specific

virtual environment

. Once you activate that environment, any packages you then install are isolated from other environments, including the global interpreter environment, reducing many complications that can arise from conflicting package versions. You can create non-global environments in VS Code using Venv or Anaconda with Python: Create Environment.

Open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Create Environment command to search, and then select the command.

The command presents a list of environment types, Venv or Conda. For this example, select Venv.

The command then presents a list of interpreters that can be used for your project. Select the interpreter you installed at the beginning of the tutorial.

After selecting the interpreter, a notification will show the progress of the environment creation and the environment folder (

/.venv

) will appear in your workspace.

Ensure your new environment is selected by using the Python: Select Interpreter command from the Command Palette.

Note: For additional information about virtual environments, or if you run into an error in the environment creation process, see Environments.

Installing and Configuring Visual Studio Code for Python Development

Installing Visual Studio Code is very accessible on any platform. Full instructions for Windows, Mac, and Linux are available, and the editor is updated monthly with new features and bug fixes. You can find everything at the Visual Studio Code website:

In case you were wondering, Visual Studio Code (or VS Code for short) shares almost nothing other than a name with its larger Windows-based namesake, Visual Studio.

Note: To learn how to set up VS Code as part of a full Python coding environment on a Windows machine, check out this comprehensive guide.

Visual Studio Code has built-in support for multiple languages and an extension model with a rich ecosystem of support for others. VS Code is updated monthly, and you can keep up to date at the Microsoft Python blog. Microsoft even makes the VS Code GitHub repo available for anyone to clone and contribute. (Cue the PR flood.)

The VS Code UI is well documented, so I won’t rehash it here:

Extensions for Python Development

As stated above, VS Code supports development in multiple programming languages through a well-documented extension model. The Python extension enables Python development in Visual Studio Code, with the following features:

• Support for Python 3.4 and higher, as well as Python 2.7
• Code completion with IntelliSense
• Linting
• Debugging support
• Code snippets
• Unit testing support
• Automatic use of conda and virtual environments
• Code editing in Jupyter environments and Jupyter Notebooks

Visual Studio Code extensions cover more than just programming language capabilities:

• Keymaps allow users already familiar with Atom, Sublime Text, Emacs, Vim, PyCharm, or other environments to feel at home.

• Themes customize the UI whether you like coding in the light, dark, or something more colorful.

• Language packs provide a localized experience.

Here are some other extensions and settings I find useful:

• GitLens provides tons of useful Git features directly in your editing window, including blame annotations and repository exploration features.

• Auto save is easily turned on by selecting

  
File, Auto Save

  from the menu. The default delay time is 1000 milliseconds, which is also configurable.

• Settings Sync allows you to synchronize your VS Code settings across different installations using GitHub. If you work on different machines, this helps keep your environment consistent across them.

• Docker lets you quickly and easily work with Docker, helping author

  
Dockerfile

  and
  
docker-compose.yml

  , package and deploy your projects, and even generate the proper Docker files for your project.

Of course, you may discover other useful extensions as you use VS Code. Please share your discoveries and settings in the comments!

Discovering and installing new extensions and themes is accessible by clicking on the Extensions icon on the Activity Bar. You can search for extensions using keywords, sort the results numerous ways, and install extensions quickly and easily. For this article, install the Python extension by typing

python

in the Extensions item on the Activity Bar, and clicking Install:

You can find and install any of the extensions mentioned above in the same manner.

Visual Studio Code Configuration Files

One important thing to mention is that Visual Studio Code is highly configurable through user and workspace settings.

User settings are global across all Visual Studio Code instances, while workspace settings are local to the specific folder or project workspace. Workspace settings give VS Code tons of flexibility, and I call out workspace settings throughout this article. Workspace settings are stored as

.json

files in a folder local to the project workspace called

.vscode

.

Python and Visual Studio Code Setup

In this part, we will learn to install Python and VSCode and run a simple Python code.

Installing Python

Downloading and installing the latest version of Python is straightforward. Go to Python.org and download the latest version for Windows. The installer is also available for Linux/Unix, macOS, and other platforms. After downloading the installer, install Python with default settings.

Image from Python.org

The most popular way of installing Python is through Anaconda Distribution. It comes with a pre-installed package and software for us to start coding without hiccups. It is available for Windows, macOS, and Linux operating systems.

Image from Anaconda

After installing Python on our operating system, check whether it is properly working by typing the following command in CLI / Terminal.


python --version


Output:


Python 3.9.13


Other Python installation methods

We can also install Python using various CLI tools or through the Windows store.

You can check out our full guide on how to install Python for more details. Similarly, our interactive Introduction to Python course helps you master the basics of Python syntax, lists, functions, packages, and Numpy.

Installing VSCode

Installing VSCode is super simple. Download and install the stable build from the official website. The installer is available for all kinds of operating systems, including web browsers.

Image from Visual Studio Code

Other VSCode installation methods

We can install using Microsoft store, Snap Store, and multiple CLI tools for Windows, Linux, and macOS.

Running Python in VSCode

After installing Python and VSCode, it is time to write a simple code and run the Python file within the IDE.

Create a new file

At start, you will see the welcome note. Ignore that and go to File > New Text File or use the keyboard shortcut Ctrl + N to create a new file. After that, write a simple print expression to display “Hello World.”

Save Python file

Save the file using Ctrl + S. Select the file directory and type the file name. Make sure to add `.py` at the end of the file name.

Select the interpreter

To run the Python file, we need to select the Python interpreter. By default, the Anaconda environment comes with Python version 3.9.13.

Run a Python file

To run the Python file, simply click on the Run button on the top left, as shown in the image. It will initialize the terminal and run the Python file to display the output.

You can also type python test.py in the terminal to run the file present in the current directory.

Visual Studio Code Python for Data Science

Visual Studio Code allows users to simply run the data science code in Jupyter Notebook. We can run the cell and visualize the result within VSCode. It supports all kinds of programming languages and comes with built-in features to mimic the browser-based Jupyter notebook that we all love.

Learn more about Jupyter Notebooks by reading our How to use Jupyter Notebook tutorial.

To use the Jupyter notebook extension, we need to first install a Jupyter notebook.


pip install jupyterlab


Or


pip install notebook


Note: Jupyter Notebook and Jupyter Lab come with Anaconda Distribution, so we don’t have to install anything.

Install Jupyter Extension

After that, install the Jupyter extension from the Visual Studio marketplace.

To create a Jupyter notebook file, we can either create a new file with .ipynb extension or access the command palette (Ctrl+Shift+P) and select Jupyter: Create New Jupyter Notebook.

Pick the Ipython Kernel

To initialize the Jupyter server, we need to select the kernel by clicking on the kernel picker in the top right of the notebook, as shown in the image.

Note: By default, Anaconda comes with Python version 3.9.13. You can download the latest version of Python 3.11, but it won’t support all packages.

Run the Jupyter cell

Write a print expression to display “Hello World” and press the run button.

Add another cell

You can use the B key or click on + Code to add a new cell and run the cell with Ctrl + ⤶ Enter. You can learn about Jupyter keyboard shortcuts on defkey.

For R language users, we have got a Notebooks for R tutorial. You will learn to use R in a Jupyter Notebook and useful features.

Note: if you are looking for a hassle-free way of using Jupyter Notebook, then try DataCamp Workspace. It comes with essential Python libraries, a pre-build environment, and it supports various database integration.

Start VS Code in a workspace folder

By starting VS Code in a folder, that folder becomes your “workspace”.

Using a command prompt or terminal, create an empty folder called “hello”, navigate into it, and open VS Code (

code

) in that folder () by entering the following commands:


mkdir hello cd hello code .


Note: If you’re using an Anaconda distribution, be sure to use an Anaconda command prompt.

Alternately, you can create a folder through the operating system UI, then use VS Code’s File > Open Folder to open the project folder.

Debugging Support

Even though VS Code is a code editor, debugging Python directly within VS Code is possible. VS Code offers many of the features you would expect from a good code debugger, including:

• Automatic variable tracking
• Watch expressions
• Breakpoints
• Call stack inspection

You can see them all as part of the Debug view on the Activity Bar:

The debugger can control Python apps running in the built-in terminal or an external terminal instance. It can attach to an already running Python instances, and can even debug Django and Flask apps.

Debugging code in a single Python file is as simple as starting the debugger using F5. You use F10 and F11 to step over and into functions respectively, and Shift+F5 to exit the debugger. Breakpoints are set using F9, or using the mouse by clicking in the left margin in the editor window.

Before you start debugging more complicated projects, including Django or Flask applications, you need to setup and then select a debug configuration. Setting up the debug configuration is relatively straightforward. From the Debug view, select the Configuration drop-down, then Add Configuration, and select Python:

Visual Studio Code will create a debug configuration file under the current folder called

.vscode/launch.json

, which allows you to setup specific Python configurations as well as settings for debugging specific apps, like Django and Flask.

You can even perform remote debugging, and debug Jinja and Django templates. Close the

launch.json

file in the editor and select the proper configuration for your application from the Configuration drop-down.

Run Python code

Click the Run Python File in Terminal play button in the top-right side of the editor.

The button opens a terminal panel in which your Python interpreter is automatically activated, then runs

python3 hello.py

(macOS/Linux) or

python hello.py

(Windows):

There are three other ways you can run Python code within VS Code:

1. Right-click anywhere in the editor window and select Run > Python File in Terminal (which saves the file automatically):

2. Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.

3. From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Congrats, you just ran your first Python code in Visual Studio Code!

Next steps

• Python Hello World tutorial – Get started with Python in VS Code.
• Editing Python – Learn about auto-completion, formatting, and refactoring for Python.
• Basic Editing – Learn about the powerful VS Code editor.
• Code Navigation – Move quickly through your source code.
• Django tutorial
• Flask tutorial

Getting Started with Python in VS Code

In this tutorial, you will learn how to use Python 3 in Visual Studio Code to create, run, and debug a Python “Roll a dice” application, work with virtual environments, use packages, and more! By using the Python extension, you turn VS Code into a great, lightweight Python editor.

If you are new to programming, check out the Visual Studio Code for Education – Introduction to Python course. This course offers a comprehensive introduction to Python, featuring structured modules in a ready-to-code browser-based development environment.

To gain a deeper understanding of the Python language, you can explore any of the programming tutorials listed on python.org within the context of VS Code.

For a Data Science focused tutorial with Python, check out our Data Science section.

Configuring Linting and Formatting in VSCode

Linting

Linting highlights the problems in the Python source code and provides us with suggestions. It generally highlights syntactical and stylistic issues. Linting helps you identify and correct coding issues that can lead to errors.

You can select the linting method by selecting Python: Select Linter command in the command palette (Ctrl+Shift+P). You can also manually enable the linting method in settings.

Select linting method

In our case, we have selected the flake8 method. You can also review the list of available linting methods.

• Enable/ Disable Linting: select Python: Enable/Disable Linting in command palette.
• Run Linting: command palette (Ctrl+Shift+P) > Python: Run Linting.

Fixing the error

After running the Python linter, you will see the issues with the suggestions.

Note: Enabling a different linter will prompt you to install the required Python package.

Formatting

Formatting makes code readable. It follows specific rules for line spacing, indents, spacing around operators, and closing brackets. The Python extension supports three Python formatting methods: autopep8, black, or yapf.

By reading about PEP-8: Python Naming Conventions & Code Standards, you can learn Python’s style guide and formatting rules.

Select the Python formatter

To access the formatting option, we have to open the settings panel by going to Preferences -> Settings or using the keyboard shortcut: Ctrl +,. After that, type “python formatting provider” in the search bar and select “black” from the dropdown menu.

Configure Python formatter

For formatting the Python file on save, we have to search for format on save in the Settings and enable the Editor: Format on Save option.

Run Python code

To experience Python, create a file (using the File Explorer) named

hello.py

and paste in the following code:


print("Hello World")


The Python extension then provides shortcuts to run Python code using the currently selected interpreter (Python: Select Interpreter in the Command Palette). To run the active Python file, click the Run Python File in Terminal play button in the top-right side of the editor.

You can also run individual lines or a selection of code with the Python: Run Selection/Line in Python Terminal command (Shift+Enter). If there isn’t a selection, the line with your cursor will be run in the Python Terminal. An identical Run Selection/Line in Python Terminal command is available on the context menu for a selection in the editor. The same terminal will be used every time you run a selection or a line in the terminal/REPL, until that terminal is closed. The same terminal is also used for Run Python File in Terminal. If that terminal is still running the REPL, you should exit the REPL (

exit()

) or switch to a different terminal before running a Python file.

The Python extension automatically removes indents based on the first non-empty line of the selection, shifting all other lines left as needed.

The command opens the Python Terminal if necessary; you can also open the interactive REPL environment directly using the Python: Start REPL command that activates a terminal with the currently selected interpreter and then runs the Python REPL.

For a more specific walkthrough and other ways of running code, see the run code tutorial.

Environments

The Python extension automatically detects Python interpreters that are installed in standard locations. It also detects conda environments as well as virtual environments in the workspace folder. See Configuring Python environments.

The current environment is shown on the right side of the VS Code Status Bar:

The Status Bar also indicates if no interpreter is selected:

The selected environment is used for IntelliSense, auto-completions, linting, formatting, and any other language-related feature. It is also activated when you run or debug Python in a terminal, or when you create a new terminal with the Terminal: Create New Terminal command.

To change the current interpreter, which includes switching to conda or virtual environments, select the interpreter name on the Status Bar or use the Python: Select Interpreter command.

VS Code prompts you with a list of detected environments as well as any you’ve added manually to your user settings (see Configuring Python environments).

Conclusion

Visual Studio Code is one of the coolest general purpose editors and a great candidate for Python development. In this article, you learned:

• How to install VS Code on any platform
• How to find and install extensions to enable Python-specific features
• How VS Code makes writing a simple Python application easier
• How to run and debug existing Python programs within VS Code
• How to work with Git and GitHub repositories from VS Code

Visual Studio Code has become my default editor for Python and other tasks, and I hope you give it a chance to become yours as well.

If you have questions or comments, please reach out in the comments below. There is also a lot more information at the Visual Studio Code website than we could cover here.

The author sends thanks to Dan Taylor from the Visual Studio Code team at Microsoft for his time and invaluable input in this article.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Development in Visual Studio Code (Setup Guide)

Enable Visual Studio Code to seamlessly identify the correct runtime for your project, letting you leverage the security and compliance features inherent in the ActiveState project within your integrated development environment (IDE).

Before using your ActiveState project’s Python interpreter in your VS Code environment, make sure the following conditions are met:


state update. If you have installed the State Tool after opening VS Code you will need to restart VS Code for the changes to take effect.


The following will help you integrate Visual Studio Code with your ActiveState project.

Begin by creating a Python file in your project, or opening an existing Python file. If this is a new file you may be prompted to select an interpreter, or if it is an existing file you can select a new interpreter using the VS Code Command Palette.

To access the “Select Interpreter” drop-down from the Command Palette

This will automatically generate a list of all of the interpreters currently available on your machine (including those provided by ActiveState). Click to select your preferred interpreter and begin working on your file.

After making your selection your new interpreter will be displayed at the bottom of the VS Code window (“andrewd-activestate/PythonWindows3’:ActiveState” shown above).

If your ActiveState Python interpreter is not immediately available in the “Select Interpreter” dropdown:


python.activeStateToolPathunder the “Settings” list.


$PATHor


%PATH%variable. To enter the absolute path to the State Tool’s executable go to File > Preferences> Settings and search for ”State Tool”. Enter the absolute path to the State Tool executable in the field shown.



state projects


Sử dụng Visual Studio Code Python

Python đã trở thành một trong những ngôn ngữ lập trình phổ biến năm 2022.

Bài viết này sẽ hướng dẫn các bạn sử dụng Visual Studio Code – Một Editor đa năng phát triển bởi MicroSoft.

Để cài đặt

Các bạn download từ link bên dưới (Link chính thức của Microsoft) DOWNLOAD VISUAL STUDIO CODE

Việc cài đặt rất dễ dàng, bạn chọn file cài đặt tương ứng với hệ điều hành sử dụng, click đúp cài đặt phần mềm.

Các extension hỗ trợ lập trình

Visual Studio Code được Microsoft phát triển cho nhiều ngôn ngữ lập trình, nên để lập trình Python trên đó các bạn cài một số extension cần thiết.

Để cài extension bằng lệnh, trên VS code bấm tổ hợp phím [ Ctrl + P ], nhập lệnh cài đặt và gõ phím [ Enter ]

Để cài đặt thông thường các bạn bấm tổ hợp phím [ Ctrl + Shift + X ] hoặc bấm vào biểu tượng Extension trên VS code, tìm kiếm extension cần thiết bấm [ Install ] để cài đặt.

Hướng dẫn sử dụng VS

Tạo Workspace

Từ cửa sổ VS Code, bấm tổ hợp phím [ Ctrl + N ]

Tạo file hello-world.py

Lần sau bạn muốn mở lại Project chỉ cần chọn [ Ctrl + O ] browser tới file này.

Chạy python script

Sau khi tạo file hello-world.py, để chạy file này chúng ta kích chuột phải vào file chọn “Run python file in terminal”

Kết quả:

Một số mẹo hay khi lập trình Python bằng VS

a. Nhảy tới 1 function

Giữ phím [ Ctrl ] và bấm vào function, method để nhảy tới function mà bạn đã định nghĩa.

b. Format source code theo chuẩn PEP 8


python -m pip install -U autopep8 --user


Bấm tổ hợp phím [ Ctrl + Shift + I ] để format file source code cho chúng.

All rights reserved

Bài này sẽ hướng dẫn bạn lập trình Python với Visual Studio Code và cài đặt các plugin hữu ích trong Visual Studio Code. Visual Studio Code là một công cụ mã nguồn mở.

Nội dung chính

Run/Debug chương trình python

Nhấn F5 để chạy chương trình Python.

Nhấn Ctrl + F5 để debug chương trình Python.

Kết quả:

Tutorial: Work with Python in Visual Studio

In this tutorial, you learn how to work with Python in Visual Studio. Python is a popular programming language that’s reliable, flexible, easy to learn, and free to use on all operating systems. Python is supported by a strong developer community and many free libraries. The language supports all kinds of development, including web applications, web services, desktop apps, scripting, and scientific computing. Many universities, scientists, casual developers, and professional developers use Python. Visual Studio provides first-class language support for Python.

This tutorial guides you through a six-step process:

• Step 1: Create a Python project (this article)
• Step 2: Write and run code to see Visual Studio IntelliSense at work
• Step 3: Create more code in the Interactive REPL window
• Step 4: Run the completed program in the Visual Studio debugger
• Step 5: Install packages and manage Python environments
• Step 6: Work with Git

This article covers the tasks in Step 1. You create a new project and review the UI elements visible in Solution Explorer.

Debugging and Testing in VSCode

Debugging

The Python extension comes with Debugging for all kinds of applications like multi-threaded, web, and remote applications. We can set breakpoints, inspect data, and run programs step by step.

Select a debug configuration

Launch the debug tab by clicking on the debug icon on the action bar or by using the keyboard shortcut Ctrl + Shift +D. To customize Debug options, click on create a launch.json file and select Python File.

Debug Panel

Run the Debug by clicking on the blue button Run and Debug, and it will run the Python file and show us the Variables, Watch, Call Stack, and breakpoints.

Quick debug

For quick debugging, you can always click on the down arrow beside the Run button and select Debug Python File.

Testing

The Python extension supports unittest and pytest testing frameworks. Instead of reading the test results in a terminal, you can review and resolve the issues within the Testing tab in an active bar.

Configure Python tests

After clicking on the Testing button, we will click on the Configure Python Tests button and select the testing framework. Usually, VSCode automatically detects the framework and displays all the tests in a tree view.

Learn about Python unit testing, implementing Python’s pytest testing framework by following our how to use pytest for unit testing tutorial.

Note: The testing example that we are using is from Visual Studio Code official documentation.

Run the unittest

We can run the Unit test by clicking on the Run Test button in the Testing tab and analyzing the results.

As we can observe, 1 of 2 tests have passed, and it has displayed the reasoning behind the failed result. VSCode testing is highly interactive and user-friendly.

Autocomplete and IntelliSense

The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion (in-context method and variable suggestions) across all your files and for built-in and third-party modules.

IntelliSense quickly shows methods, class members, and documentation as you type. You can also trigger completions at any time with ⌃Space (Windows, Linux Ctrl+Space). Hovering over identifiers will show more information about them.

Testing Support

VS Code can automatically recognize existing Python tests written in the

unittest

framework, or the

pytest

or

Nose

frameworks if those frameworks are installed in the current environment. I have a unit test written in

unittest

for the equation eval library, which you can use for this example.

To run your existing unit tests, from any Python file in the project, right-click and select Run Current Unit Test File. You’ll be prompted to specify the test framework, where in the project to search for tests, and the filename pattern your tests utilize.

All of these are saved as workspace settings in your local

.vscode/settings.json

file and can be modified there. For this equation project, you select

unittest

, the current folder, and the pattern

*_test.py

.

Once the test framework is set up and the tests have been discovered, you can run all your tests by clicking Run Tests on the Status Bar and selecting an option from the Command Palette:

You can even run individual tests by opening the test file in VS Code, clicking Run Tests on the Status Bar, and selecting the Run Unit Test Method… and the specific test to run. This makes it trivial to address individual test failures and re-run only failed tests, which is a huge time-saver! Test results are shown in the Output pane under Python Test Log.

Conclusion

VSCode is not just a code editor. It is a complete ecosystem for efficient Python development. It provides us with shortcuts, Commands Palette, IntelliSense, linting, formatting, debugging, formatting, Git integrations, Jupyter notebook, third-party extensions, and a fully customizable development experience.

VSCode is highly recommended to beginners who are learning the basics of Python and data science. Complete Data Scientist with a Python career track to become a master in Python and data science. The career track consists of 25 courses and six projects to prepare you to become a professional data scientist.

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Install and configure Visual Studio Code for Python development

Install and configure Visual Studio Code to create a development environment for learning to build Python applications.

Next steps

To learn how to build web apps with popular Python web frameworks, see the following tutorials:

There is then much more to explore with Python in Visual Studio Code:

• Python profile template – Create a new profile with a curated set of extensions, settings, and snippets
• Editing code – Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
• Linting – Enable, configure, and apply a variety of Python linters.
• Debugging – Learn to debug Python both locally and remotely.
• Testing – Configure test environments and discover, run, and debug tests.
• Settings reference – Explore the full range of Python-related settings in VS Code.
• Deploy Python to Azure App Service
• Deploy Python to Container Apps

Python in Visual Studio Code

Visual Studio Code is a free source code editor that fully supports Python and useful features such as real-time collaboration. It’s highly customizable to support your classroom the way you like to teach.

“Visual Studio Code is the best balance of authenticity and accessibility… Visual Studio Code doesn’t feel ‘fake’, it’s what real software developers use. Plus, Visual Studio Code works on every OS!” – Professor Zachary Dodds from Harvey Mudd College

Read below for recommendations for extensions, settings, and links to free lessons that you can use in your classes.

Other popular Python extensions

The Microsoft Python extension provides all of the features described previously in this article. Additional Python language support can be added to VS Code by installing other popular Python extensions.

1. Open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)).
2. Filter the extension list by typing ‘python’.

The extensions shown above are dynamically queried. Click on an extension tile above to read the description and reviews to decide which extension is best for you. See more in the Marketplace.

Review elements in Solution Explorer

Take some time to familiarize yourself with Solution Explorer, where you can browse files and folders in your project.

• (1) At the top level is the solution, which by default has the same name as your project. A solution, which is shown as an .sln file on disk, is a container for one or more related projects. For example, if you write a C++ extension for your Python application, that C++ project can be in the same solution. The solution might also contain a project for a web service, and projects for dedicated test programs.

• (2) Your project is highlighted in bold and uses the name you entered in the Create a new project dialog. On disk, this project is represented by a .pyproj file in your project folder.

• (3) Under your project you see source files. In this example, you have only a single .py file. Selecting a file displays its properties in the Properties window. If you don’t see the Properties window, select the wrench icon in the Solution Explorer banner. Double-clicking a file opens it in whatever way is appropriate for that file.

• (4) Also under the project is the Python Environments node. Expand the node to show the available Python interpreters.

• (5) Expand an interpreter node to see the libraries installed in that environment.

Right-click any node or item in Solution Explorer to show a context menu of applicable commands. For example, Rename lets you change the name of a node or item, including the project and the solution.

Installing Essential VSCode Python Extensions

The VSCode’s Python extensions will provide us with the helping functionalities for code editing, docstrings, linting, formatting, debugging, testing, and environment selection.

How to install a VSCode Extension

Click on the box icon on the activity bar or use a keyboard shortcut: Ctrl + Shift + X to open the extension panel. Type any keyword in the search bar to explore all kinds of extensions.

Install VSCode Python extension

In our case, we will type Python and install the Python extension by clicking on the install button, as shown above.

List of Essential Python Extensions

Python

The Python extension automatically installs Pylance, Jupyter, and isort extensions. It comes with a complete collection of tools for Data Science, web development, and software engineering.

Key Features:

Python extension comes with IntelliSense, linting, debugging, code navigation, code formatting, refactoring, variable explorer, and test explorer.

• IntelliSense (code autocomplete)
• Linting (Pylint, Flake8)
• Code formatting (black, autopep)
• Debugging
• Testing (unittest, pytest)
• Jupyter Notebooks
• Environments (venv, pipenv, conda)
• Refactoring

Indent-rainbow

Indent-rainbow extensions provide us with a multilevel colorized indentation for improved code readability. We get alternating colors on each step, and it helps us avoid common indentation errors.

Python Indent

Python Indent extension helps us with creating indentations. By pressing the Enter key, the extension will parse the Python file and determine how the next line should be indented. It is a time saver.

Jupyter Notebook Renderers

Jupyter Notebook Renderers is part of the Jupyter extension pack. It helps us render plotly, vega, gif, png, svg, and jpeg output.

autoDocstring

The autoDocstring extension helps us quickly generate docstring for Python functions. By typing triple quotes “”” or ”’ within the function, we can generate and modify docstring. Learn more about doc strings by following our Python Docstrings tutorial.

Note: Most Python development extensions and features come with Python extensions.

Create a Python source code file

From the File Explorer toolbar, select the New File button on the

hello

folder:

Name the file

hello.py

, and VS Code will automatically open it in the editor:

By using the

.py

file extension, you tell VS Code to interpret this file as a Python program, so that it evaluates the contents with the Python extension and the selected interpreter.

Note: The File Explorer toolbar also allows you to create folders within your workspace to better organize your code. You can use the New folder button to quickly create a folder.

Now that you have a code file in your Workspace, enter the following source code in

hello.py

:


msg = "Roll a dice" print(msg)


When you start typing

IntelliSense and auto-completions work for standard Python modules as well as other packages you’ve installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the

msg

variable contains a string, IntelliSense provides string methods when you type

msg.

:

Finally, save the file (⌘S (Windows, Linux Ctrl+S)). At this point, you’re ready to run your first Python file in VS Code.

For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.

Why use VSCode for Python?

Virtual Studio Code (VSCode) is a perfect Integrated Development Environment for Python. It is simple and comes with built-in features that enhance the development experience. VSCode Python extensions come with powerful features like syntax autocomplete, linting, debugging, unit testing, GitOps, virtual environments, notebooks, editing tools, and the ability to customize the editor.

Key Features:

1. Command Palette to access all commands by typing keywords.
2. Fully customizable keyboard shortcuts.
3. Jupyter extension for data science. Run Jupyter notebook within the IDE.
4. Auto linting and formatting.
5. Debugging and Testing.
6. Git integration.
7. Custom code snippets.
8. Enhanced editing tools. Multi cursor selection, column selection, outline view, side-by-side preview, and search and modify.

In this tutorial, we will start by installing Python and VSCode, then run a Python script in VSCode. After that, we will customize the editor to enhance the Python development experience by installing essential extensions and learning about built-in features. In the end, we will learn about Python productivity hacks.

Install and use packages

Let’s build upon the previous example by using packages.

In Python, packages are how you obtain any number of useful code libraries, typically from PyPI, that provide additional functionality to your program. For this example, you use the

numpy

package to generate a random number.

Return to the Explorer view (the top-most icon on the left side, which shows files), open

hello.py

, and paste in the following source code:


import numpy as np msg = "Roll a dice" print(msg) print(np.random.randint(1,9))


Tip: If you enter the above code by hand, you may find that auto-completions change the names after the


as

keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.

Next, run the file in the debugger using the “Python: Current file” configuration as described in the last section.

You should see the message, “ModuleNotFoundError: No module named ‘numpy'”. This message indicates that the required package isn’t available in your interpreter. If you’re using an Anaconda distribution or have previously installed the

numpy

package you may not see this message.

To install the

numpy

package, stop the debugger and use the Command Palette to run Terminal: Create New Terminal (⌃⇧` (Windows, Linux Ctrl+Shift+`)). This command opens a command prompt for your selected interpreter.

To install the required packages in your virtual environment, enter the following commands as appropriate for your operating system:

1. Install the packages

   
# Don't use with Anaconda distributions because they include matplotlib already. # macOS python3 -m pip install numpy # Windows (may require elevation) py -m pip install numpy # Linux (Debian) apt-get install python3-tk python3 -m pip install numpy


2. Now, rerun the program, with or without the debugger, to view the output!

Congrats on completing the Python tutorial! During the course of this tutorial, you learned how to create a Python project, create a virtual environment, run and debug your Python code, and install Python packages. Explore additional resources to learn how to get the most out of Python in Visual Studio Code!

Install a Python interpreter

Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.

Windows

Install Python from python.org. Use the Download Python button that appears first on the page to download the latest version.

Note: If you don’t have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of supported Python versions.

For additional information about using Python on Windows, see Using Python on Windows at Python.org

macOS

The system install of Python on macOS is not supported. Instead, a package management system like Homebrew is recommended. To install Python using Homebrew on macOS use

brew install python3

at the Terminal prompt.

Note: On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

Linux

The built-in Python 3 installation on Linux works well, but to install other Python packages you must install

pip

with get-pip.py.

Other options

• Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.

• Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you’ll also want to install the WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.

Note: To verify that you’ve installed Python successfully on your machine, run one of the following commands (depending on your operating system):

Linux/macOS: open a Terminal Window and type the following command:


python3 --version


Windows: open a command prompt and run the following command:


py -3 --version


If the installation was successful, the output window should show the version of Python that you installed. Alternatively, you can use the


py -0

command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

Tips and Tricks for Efficient Python Development in VSCode

VSCode comes with awesome Python development features and extensions. We can customize them to our needs and improve the productivity. In this section, we will learn about tips and tricks for efficient Python development.

1. Getting started: Help > Get Started. Learn about VSCode’s customization and features by following guided tutorials.
2. Command Palette: access entire all available commands by using the Keyboard shortcut: Ctrl+Shift+P. By writing keywords, we can access specific commands.
3. Keyboard shortcuts: better than command palettes. We can modify keyboard shortcuts or memorize them by using keyboard reference sheets. It will help us access the commands directly, instead of searching with the keyword.
4. Command line: launch the VSCode editor through the command line interface by typing `code .`. We can also customize how the editor is launched by adding additional arguments.
5. Errors and warnings: quick jump to errors and warnings in a project by using the keyboard shortcut: Ctrl+Shift+M. We can also cycle through the error with F8 or Shift+F8.
6. Customization: VSCode allows us to customize themes, keyboard shortcuts, JSON validation, debugging settings, fonts, and many more. It is a fully customizable IDE.
7. Extensions: other Python extensions improve our development experience. Look for popular extensions on the Visual Studio Marketplace.
8. Multi cursor selection: is a lifesaver. Add cursors at arbitrary positions by using Alt+Click. It will allow us to modify multiple lines of code at once. We can also use Ctrl+Shift+L to modify all occurrences of the current selection.
9. Search and modify: this is the best tool for searching and modifying multiple expressions at once. We can also rename the symbol by selecting the symbol and typing F2.
10. Git integration: allows us to perform all Git-related tasks within IDE. It provides an easy-to-use GUI for diff, views, staging, branching, committing, merging, and more.
11. Code Snippets: is our best friend. Just like Autohotkey, we are creating templates for repeating code patterns. To create a custom code snippet, select File > Preferences > Configure User Snippets and then select the language.
12. GitHub Copilot: is a winner extension for all kinds of development. It enhances the coding experience with artificial intelligence (AI) by suggesting lines of code or entire functions.

Bonus: sync your settings by logging into your GitHub account. It will sync your settings across all of the machines.

Free Python and Data Science lessons

NASA-inspired lessons

This learning path enables students to use Python to explore doing analyses and projects inspired from real-world problems faced by National Aeronautics and Space Administration (NASA) scientists. View full details of the lessons under NASA-inspired Lessons.

Learn Python with Over The Moon

These space-themed lessons were inspired by the Netflix film, Over the Moon, and will introduce students to data science, machine learning, and artificial intelligence using Python and Azure. View full details on Learn Python with Over The Moon.

Wonder Woman-inspired lessons

Give an introduction to Python with “Wonder Woman 1984”-inspired lessons that help students learn about the basics like conditionals and variables. Get full lesson details under Learn Python with Wonder Woman.

Python in Notebooks

Learn the basics of Python. View the full lesson at Write basic Python in Notebooks in Visual Studio Code.

Set up your Python beginner development environment

A step-by-step guide to installing and setting up your Python and VS Code environment. View the full lesson at Set up your Python beginner development environment with Visual Studio Code.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Development in Visual Studio Code (Setup Guide)

One of the coolest code editors available to programmers, Visual Studio Code, is an open-source, extensible, light-weight editor available on all platforms. It’s these qualities that make Visual Studio Code from Microsoft very popular, and a great platform for Python development.

In this article, you’ll learn about Python development in Visual Studio Code, including how to:

• Install Visual Studio Code
• Discover and install extensions that make Python development easy
• Write a straightforward Python application
• Learn how to run and debug existing Python programs in VS Code
• Connect Visual Studio Code to Git and GitHub to share your code with the world

We assume you are familiar with Python development and already have some form of Python installed on your system (Python 2.7, Python 3.6/3.7, Anaconda, or others). Screenshots and demos for Ubuntu and Windows are provided. Because Visual Studio Code runs on all major platforms, you may see slightly different UI elements and may need to modify certain commands.

If you already have a basic VS Code setup and you’re hoping to dig deeper than the goals in this tutorial, you might want to explore some advanced features in VS Code.

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.

Hướng dẫn lập trình Python với Visual Studio Code

Bước 1: Cài đặt Python và VSCode

Cài đặt Python:

1. Truy cập trang chính thức của Python: https://www.python.org/.
2. Tải và cài đặt phiên bản Python mới nhất cho hệ điều hành của bạn. Trong quá trình cài đặt, hãy chọn tùy chọn “Add Python to PATH” để thêm Python vào biến môi trường.

Cài đặt Visual Studio Code:

1. Truy cập trang chính thức của VSCode: https://code.visualstudio.com/.
2. Tải và cài đặt phiên bản mới nhất cho hệ điều hành của bạn.

Bước 2: Cài đặt Extension Python cho VSCode

1. Mở VSCode.
2. Trong thanh bên trái, chọn biểu tượng “Extensions” (hoặc nhấn
   
Ctrl+Shift+X

   ).
3. Tìm kiếm “Python” trong ô tìm kiếm.
4. Chọn extension “Python” được phát triển bởi Microsoft và nhấn nút “Install”.

Bước 3: Tạo và Chạy một File Python

1. Mở VSCode.
2. Chọn “File” > “New File” để tạo một file mới.
3. Lưu file với đuôi
   
.py

   , ví dụ:
   
hello.py

   .
4. Viết mã Python vào file. Ví dụ:pythonCopy code
   
print("Hello, World!")

5. Chọn “View” > “Terminal” để mở terminal tích hợp tại đáy của VSCode.
6. Gõ lệnh sau để chạy script Python:bashCopy code
   
python hello.py

   hoặcbashCopy code
   
python3 hello.py


Bước 4: Debugging

1. Đặt breakpoints bằng cách nhấn vào cạnh số dòng code bạn muốn dừng lại.
2. Nhấn
   
F5

   để bắt đầu chế độ debug. Bạn cũng có thể sử dụng các biểu tượng debug trên thanh công cụ.

Bước 5: Cài đặt Thêm (Optional)

1. Python Docstring Generator:
   • Cài đặt extension “Python Docstring Generator” để tự động tạo docstrings cho hàm và phương thức.
2. Pylint hoặc flake8:
   • Cài đặt extension “Pylint” hoặc “flake8” để kiểm tra mã nguồn Python và cải thiện chất lượng mã.
3. Jupyter (Nếu cần):
   • Cài đặt extension “Jupyter” để hỗ trợ Jupyter Notebooks trong VSCode.

Bước 6: Cập nhật Extension và VSCode

Hãy thường xuyên kiểm tra và cập nhật các extension Python và VSCode để đảm bảo bạn sử dụng phiên bản mới nhất với các tính năng và sửa lỗi.

Start a New Python Program

Let’s start our exploration of Python development in Visual Studio Code with a new Python program. In VS Code, type Ctrl+N to open a new File. (You can also select File, New from the menu.)

Note: The Visual Studio Code UI provides the Command Palette, from which you can search and execute any command without leaving the keyboard. Open the Command Palette using Ctrl+Shift+P, type

File: New File

, and hit Enter to open a new file.

No matter how you get there, you should see a VS Code window that looks similar to the following:

Once a new file is opened, you can begin entering code.

Entering Python Code

For our test code, let’s quickly code up the Sieve of Eratosthenes (which finds all primes less than a given number). Begin typing the following code in the new tab you just opened:


sieve = [True] * 101 for i in range(2, 100):


You should see something similar to this:

Wait, what’s going on? Why isn’t Visual Studio Code doing any keyword highlighting, any auto-formatting, or anything really helpful? What gives?

The answer is that, right now, VS Code doesn’t know what kind of file it’s dealing with. The buffer is called

Untitled-1

, and if you look in the lower right corner of the window, you’ll see the words Plain Text.

To activate the Python extension, save the file (by selecting File, Save from the menu, File:Save File from the Command Palette, or just using Ctrl+S) as

sieve.py

. VS Code will see the

.py

extension and correctly interpret the file as Python code. Now your window should look like this:

That’s much better! VS Code automatically reformats the file as Python, which you can verify by inspecting the language mode in the lower left corner.

If you have multiple Python installations (like Python 2.7, Python 3.x, or Anaconda), you can change which Python interpreter VS Code uses by clicking the language mode indicator, or selecting Python: Select Interpreter from the Command Palette. VS Code supports formatting using

pep8

by default, but you can select

black

or

yapf

if you wish.

Let’s add the rest of the Sieve code now. To see IntelliSense at work, type this code directly rather than cut and paste, and you should see something like this:

Here’s the full code for a basic Sieve of Eratosthenes:


sieve = [True] * 101 for i in range(2, 100): if sieve[i]: print(i) for j in range(i*i, 100, i): sieve[j] = False


As you type this code, VS Code automatically indents the lines under

for

and

if

statements for you properly, adds closing parentheses, and makes suggestions for you. That’s the power of IntelliSense working for you.

Running Python Code

Now that the code is complete, you can run it. There is no need to leave the editor to do this: Visual Studio Code can run this program directly in the editor. Save the file (using Ctrl+S), then right-click in the editor window and select Run Python File in Terminal:

You should see the Terminal pane appear at the bottom of the window, with your code output showing.

Python Linting Support

You may have seen a pop up appear while you were typing, stating that linting was not available. You can quickly install linting support from that pop up, which defaults to PyLint. VS Code also supports other linters. Here’s the complete list at the time of this writing:

• 
pylint

• 
flake8

• 
mypy

• 
pydocstyle

• 
pep8

• 
prospector

• 
pyllama

• 
bandit


The Python linting page has complete details on how to setup each linter.

Note: The choice of linter is a project workspace setting, and not a global user setting.

Git Integration

VS Code has built-in support for source control management, and ships with support for Git and GitHub right out of the box. You can install support for other SCM’s in VS Code, and use them side by side. Source control is accessible from the Source Control view:

If your project folder contains a

.git

folder, VS Code automatically turns on the full range of Git/GitHub functionality. Here are some of the many tasks you can perform:

• Commit files to Git
• Push changes to, and pull changes from, remote repos
• Check-out existing or create new branches and tags
• View and resolve merge conflicts
• View diffs

All of this functionality is available directly from the VS Code UI:

VS Code will also recognize changes made outside the editor and behave appropriately.

Committing your recent changes within VS Code is a fairly straightforward process. Modified files are shown in the Source Control view with an M marker, while new untracked files are marked with a U. Stage your changes by hovering over the file and then clicking the plus sign (+). Add a commit message at the top of the view, and then click the check mark to commit the changes:

You can push local commits to GitHub from within VS Code as well. Select Sync from the Source Control view menu, or click Synchronize Changes on the status bar next to the branch indicator.

Step 1: Create a new Python project

A project is how Visual Studio manages all the files that come together to produce a single application. Application files include source code, resources, and configurations. A project formalizes and maintains the relationships among all the project’s files. The project also manages external resources that are shared between multiple projects. A project allows your application to effortlessly expand and grow. Using projects is easier than managing relationships by hand in unplanned folders, scripts, text files, and your memory.

This tutorial begins with a simple project containing a single, empty code file.

1. In Visual Studio, select File > New > Project to open the New Project dialog. You can also use the keyboard shortcut Ctrl+Shift+N. In the dialog, you can browse templates across different languages, select a template for your project, and specify where Visual Studio places files.

2. To view Python templates, select Installed > Python on the left menu, or search for “Python.” The search option is a great way to find a template when you can’t remember its location in the languages tree.

   Python support in Visual Studio includes several project templates, including web applications using the Bottle, Flask, and Django frameworks. For the purposes of this walkthrough, however, let’s start with an empty project.

3. Select the Python Application template, specify a name for the project, and select OK.

1. In Visual Studio, select File > New > Project or use the keyboard shortcut Ctrl+Shift+N. The Create a new project screen opens, where you can search and browse templates across different languages.

2. To view Python templates, search for python. Search is a great way to find a template when you can’t remember its location in the languages tree.

   Python web support in Visual Studio includes several project templates, such as web applications in the Bottle, Flask, and Django frameworks. When you install Python with the Visual Studio Installer, select Python Web Support under Optional to install these templates. For this tutorial, start with an empty project.

3. Select the Python Application template, and select Next.

4. On the Configure your new project screen, specify a name and file location for the project, and then select Create.

After a few moments, your new project opens in Visual Studio:

Here’s what you see:

• (1) The Visual Studio Solution Explorer window shows the project structure.
• (2) The default code file opens in the editor.
• (3) The Properties window shows more information for the item selected in Solution Explorer, including its exact location on disk.

Install Python and the Python extension

The tutorial guides you through installing Python and using the extension. You must install a Python interpreter yourself separately from the extension. For a quick install, use Python from python.org and install the extension from the VS Code Marketplace.

Note: To help get you started with Python development, you can use the Python profile template that includes useful extensions, settings, and Python code snippets.

Once you have a version of Python installed, select it using the Python: Select Interpreter command. If VS Code doesn’t automatically locate the interpreter you’re looking for, refer to Environments – Manually specify an interpreter.

You can configure the Python extension through settings. Learn more in the Python Settings reference.

Windows Subsystem for Linux: If you are on Windows, WSL is a great way to do Python development. You can run Linux distributions on Windows and Python is often already installed. When coupled with the WSL extension, you get full VS Code editing and debugging support while running in the context of WSL. To learn more, go to Developing in WSL or try the Working in WSL tutorial.

Configure and run the debugger

Let’s now try debugging our Python program. Debugging support is provided by the Python Debugger extension, which is automatically installed with the Python extension. To ensure it has been installed correctly, open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)) and search for

@installed python debugger

. You should see the Python Debugger extension listed in the results.

Next, set a breakpoint on line 2 of

hello.py

by placing the cursor on the

Next, to initialize the debugger, press F5. Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.

Note: VS Code uses JSON files for all of its various configurations;


launch.json

is the standard name for a file containing debugging configurations.

Select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

The debugger will start, and then stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you can see that the

msg

variable appears in the Local pane.

A debug toolbar appears along the top with the following commands from left to right: continue (F5), step over (F10), step into (F11), step out (⇧F11 (Windows, Linux Shift+F11)), restart (⇧⌘F5 (Windows, Linux Ctrl+Shift+F5)), and stop (⇧F5 (Windows, Linux Shift+F5)).

The Status Bar also changes color (orange in many themes) to indicate that you’re in debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run, along with the program output.

To continue running the program, select the continue command on the debug toolbar (F5). The debugger runs the program to the end.

Tip Debugging information can also be seen by hovering over code, such as variables. In the case of


msg

, hovering over the variable will display the string

Roll a dice!

in a box above the variable.

You can also work with variables in the Debug Console (If you don’t see it, select Debug Console in the lower right area of VS Code, or select it from the … menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:


msg msg.capitalize() msg.split()


Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. “Roll a dice!” appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.

If you restart the debugger, the debugger again stops on the first breakpoint.

To stop running a program before it’s complete, use the red square stop button on the debug toolbar (⇧F5 (Windows, Linux Shift+F5)), or use the Run > Stop debugging menu command.

For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.

Tip: Use Logpoints instead of print statements: Developers often litter source code with

Editing an Existing Python Project

In the Sieve of Eratosthenes example, you created a single Python file. That’s great as an example, but many times, you’ll create larger projects and work on them over a longer period of time. A typical new project work flow might look like this:

• Create a folder to hold the project (which may include a new GitHub project)
• Change to the new folder
• Create the initial Python code using the command
  
code filename.py


Using Visual Studio Code on a Python project (as opposed to a single Python file) opens up tons more functionality that lets VS Code truly shine. Let’s take a look at how it works with a larger project.

Late in the previous millennium, when I was a much younger programmer, I wrote a calculator program that parsed equations written in infix notation, using an adaptation of Edsger Dijkstra’s shunting yard algorithm.

To demonstrate the project-focused features of Visual Studio Code, I began recreating the shunting yard algorithm as an equation evaluation library in Python. To continue following along, feel free to clone the repo locally.

Once the folder is created locally, you can open the entire folder in VS Code quickly. My preferred method (as mentioned above) is modified as follows, since I already have the folder and basic files created:


cd /path/to/project code .


VS Code understands, and will use, any virtualenv, pipenv, or conda environments it sees when opened this way. You don’t even need to start the virtual environment first! You can even open a folder from the UI, using File, Open Folder from the menu, Ctrl+K, Ctrl+O from the keyboard, or File:Open Folder from the Command Palette.

For my equation eval library project, here’s what I see:

When Visual Studio Code opens the folder, it also opens the files you last had opened. (This is configurable.) You can open, edit, run, and debug any file listed. The Explorer view in the Activity Bar on the left gives you a view of all the files in the folder and shows how many unsaved files exist in the current set of tabs.

Testing

The Python extension supports testing with Python’s built-in unittest framework and pytest.

In order to run tests, you must enable one of the supported testing frameworks in the settings of your project. Each framework has its own specific settings, such as arguments for identifying the paths and patterns for test discovery.

Once the tests have been discovered, VS Code provides a variety of commands (on the Status Bar, the Command Palette, and elsewhere) to run and debug tests. These commands also allow you to run individual test files and methods

Vì sao nên lập trình Python với Visual Studio Code

1. Hỗ Trợ Python Mạnh Mẽ:
   • Extension Python cho VSCode được phát triển chủ yếu bởi Microsoft, đồng nghĩa với việc nó nhận được sự hỗ trợ chủ đạo và cập nhật liên tục. Extension này mang lại các tính năng mạnh mẽ như Intellisense (dự đoán mã), kiểm tra lỗi, đồng bộ hóa môi trường ảo, và tích hợp với các công cụ debugging. Điều này giúp tăng cường trải nghiệm lập trình Python và làm cho quá trình phát triển trở nên dễ dàng.
2. Giao Diện Người Dùng Thân Thiện và Linh Hoạt:
   • VSCode có một giao diện người dùng thân thiện và đơn giản. Bạn có thể dễ dàng tìm hiểu và sử dụng mà không cần nhiều thời gian học cách sử dụng. Nó cũng hỗ trợ các tính năng linh hoạt như split view, giúp bạn làm việc trên nhiều tệp và thư mục cùng một lúc.
3. Tích Hợp Git và Công Cụ Quản Lý Phiên Bản:
   • VSCode tích hợp sẵn với Git, một hệ thống quản lý phiên bản phổ biến. Điều này giúp bạn theo dõi sự thay đổi trong mã nguồn, thực hiện commit, push và pull mà không cần rời khỏi môi trường phát triển. Tích hợp với Git làm cho quản lý phiên bản trở nên thuận tiện và linh hoạt.
4. Mở Rộng Bằng Extension và Cộng Đồng Lớn:
   • VSCode có một cộng đồng lớn và đa dạng, với nhiều extension sẵn có để mở rộng tính năng của nó. Cộng đồng này liên tục phát triển và cung cấp nhiều tài nguyên hữu ích, từ theme đến các extension giúp tối ưu hóa quá trình phát triển Python. Điều này giúp đảm bảo rằng bạn có thể tận dụng những công cụ tốt nhất để phát triển ứng dụng của mình.

Tổng cộng, VSCode không chỉ là một IDE phổ biến mà còn là một môi trường phát triển hiệu quả cho Python với sự linh hoạt, tích hợp tốt và cộng đồng lớn. Nếu bắt đầu học Python, hãy tham khảo hướng dẫn lập trình Python với Visual Studio Code trên.

Xem thêm Cơ sở lập trình Python

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