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.
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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
Quick Start Guide for Python in VS Code
The Python extension makes Visual Studio Code an excellent Python editor, works on any operating system, and is usable with a variety of Python interpreters.
Get started by installing:
- VS Code
- A Python Interpreter (any actively supported Python version)
- Python extension from the VS Code Marketplace
To further customize VS Code for Python, you can leverage the Python profile template, automatically installing recommended extensions and settings. For Data Science projects, consider using the Data Science profile template.
Python Extension Pack
Unsure which extensions to recommend to your students? You can point your students to the Python Education Extension Pack that contains essential and helpful extensions for the classroom. You can download the extension pack from the VS Code Marketplace:
The extension pack contains:
- Python for basic Python functionality like compiling, debugging support, linting, Jupyter Notebooks, unit tests, and more.
- Live Share to enable real-time collaboration.
- Remote – SSH to work on remote projects (for example, to access lab machines) through SSH with full VS Code functionality.
- Markdown+Math for full LaTeX support in Markdown.
- Python Test Explorer for Visual Studio Code to visualize and run Python tests in the side bar.
- Code Runner to run snippets (selected code) and single files of any code with a single click.
Set a working directory
By default, Visual Studio runs a Python project opened as a folder in the root of that same folder. The code in your project, however, might assume that Python is being run in a subfolder. For example, now suppose you open the root folder of the pythonkoans repository and there is a subfolder called python3 where _contemplate-koans.py exists. You set the python3/contemplate-koans.py file as startup item. If you then run the code, you would see an error that the koans.txt file can’t be found. This error happens because contemplate-koans.py assumes that Python is being run in the python3 folder rather than the repository root.
In such cases, you must also add a line to the launch configuration JSON file to specify the working directory:
-
Right-click the Python (.py) startup file in Solution Explorer and select Debug and Launch Settings.
-
In the Select debugger dialog box that appears, select Default and then choose Select.
Note
If you don’t see Default as a choice, be sure that you chose a Python .py file when selecting the Debug and Launch Settings command. Visual Studio uses the file type to determine which debugger options to display.
-
Visual Studio opens a file named launch.vs.json, which is located in the hidden
.vs
folder. This file describes the debugging context for the project. To specify a working directory, add a value for
"workingDirectory"
, as in
"workingDirectory": "python3"
for python-koans example:
{ "version": "0.2.1", "defaults": {}, "configurations": [ { "type": "python", "interpreter": "(default)", "interpreterArguments": "", "scriptArguments": "", "env": {}, "nativeDebug": false, "webBrowserUrl": "", "project": "contemplate_koans.py", "projectTarget": "", "name": "contemplate_koans.py", "workingDirectory": "python3" } ] }
-
Save the file and launch the program again, which now runs in the specified folder.
By default, Visual Studio runs a Python project opened as a folder in the root of that same folder. The code in your project, however, might assume that Python is being run in a subfolder. For example, now suppose you open the root folder of the pythonkoans repository and there is a subfolder called python3 where _contemplate-koans.py exists. You set the python3/contemplate-koans.py file as startup item. If you then run the code, you would see an error that the koans.txt file can’t be found. This error happens because contemplate-koans.py assumes that Python is being run in the python3 folder rather than the repository root.
In such cases, you must also add a line to the launch configuration JSON file to specify the working directory:
-
Right-click the Python (.py) startup file in Solution Explorer and select Add Debug Configuration.
-
In the Select debugger dialog box that appears, select Default and then choose Select.
Note
If you don’t see Default as a choice, be sure that you chose a Python .py file when selecting the Add Debug Configuration command. Visual Studio uses the file type to determine which debugger options to display.
-
Visual Studio opens a file named launch.vs.json, which is located in the hidden
.vs
folder. This file describes the debugging context for the project. To specify a working directory, add a value for
"workingDirectory"
, as in
"workingDirectory": "python3"
for python-koans example:
{ "version": "0.2.1", "defaults": {}, "configurations": [ { "type": "python", "interpreter": "(default)", "interpreterArguments": "", "scriptArguments": "", "env": {}, "nativeDebug": false, "webBrowserUrl": "", "project": "contemplate_koans.py", "projectTarget": "", "name": "contemplate_koans.py", "workingDirectory": "python3" } ] }
-
Save the file and launch the program again, which now runs in the specified folder.
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:
-
Right-click anywhere in the editor window and select Run > Python File in Terminal (which saves the file automatically):
-
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.
-
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!
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.
- Open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)).
- 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.
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.
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.
Next steps
To learn how to build web apps with popular Python web frameworks, see the following tutorials:
There is 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.
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.
Useful commands
Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux) and type in one of the following commands:
Command | Description |
|
Switch between Python interpreters, versions, and environments. |
|
Start an interactive Python REPL using the selected interpreter in the VS Code terminal. |
|
Runs the active Python file in the VS Code terminal. You can also run a Python file by right-clicking on the file and selecting
. |
|
Formats code using the provided formatter in the
file. |
|
Select a test framework and configure it to display the Test Explorer. |
To see all available Python commands, open the Command Palette and type
Python
. For Jupyter extension commands, just type
Jupyter
.
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Quickstart: Open and run Python code in a folder
Once you’ve installed Python support in Visual Studio 2019, it’s easy to run existing Python code in Visual Studio 2019 without creating a Visual Studio project.
Once you’ve installed Python support in Visual Studio 2022, it’s easy to run existing Python code in Visual Studio 2022 without creating a Visual Studio project.
Note
Visual Studio 2017 and earlier require you to create a Visual Studio project to run Python code, which you can easily do using a built-in project template. See Quickstart: Create a Python project from existing code.
-
For this walkthrough, you can use any folder with Python code that you like. To follow along with the example shown here, clone the gregmalcolm/python_koans GitHub repository to your computer using the command
git clone https://github.com/gregmalcolm/python_koans
in an appropriate folder. -
Launch Visual Studio 2019 and in the start window, select Open at the bottom of the Get started column. Alternately, if you already have Visual Studio running, select the File > Open > Folder command instead.
-
Navigate to the folder containing your Python code, then choose Select Folder. If you’re using the python_koans code, make sure to select the
python3
folder within the clone folder. -
Visual Studio displays the folder in Solution Explorer in what’s called Folder View. You can expand and collapse folders using the arrows on the left edges of the folder names:
-
When opening a Python folder, Visual Studio creates several hidden folders to manage settings related to the project. To see these folders (and any other hidden files and folders, such as the
.git
folder), select the Show All Files toolbar button: -
To run the code, you first need to identify the startup or primary program file. In the example shown here, select the startup file contemplate-koans.py, right-click that file and select Set as Startup Item.
Important
If your startup item is not located in the root of the folder you opened, you must also add a line to the launch configuration JSON file as described in the section, Set a working directory.
-
Run the code by pressing Ctrl+F5 or selecting Debug > Start without Debugging. You can also select the toolbar button that shows the startup item with a play button, which runs code in the Visual Studio debugger. In all cases, Visual Studio detects that your startup item is a Python file, so it automatically runs the code in the default Python environment. (That environment is shown to the right of the startup item on the toolbar.)
-
The program’s output appears in a separate command window:
-
To run the code in a different environment, select that environment from the drop-down control on the toolbar, then launch the startup item again.
-
To close the folder in Visual Studio, select the File > Close folder menu command.
-
For this walkthrough, you can use any folder with Python code that you like. To follow along with the example shown here, clone the gregmalcolm/python_koans GitHub repository to your computer using the command
git clone https://github.com/gregmalcolm/python_koans
in an appropriate folder. -
Launch Visual Studio 2022 and in the start window, select Open at the bottom of the Get started column. Alternately, if you already have Visual Studio running, select the File > Open > Folder command instead.
-
Navigate to the folder containing your Python code, then choose Select Folder.
-
Visual Studio displays the folder in Solution Explorer in what’s called Folder View. You can expand and collapse folders using the arrows on the left edges of the folder names:
-
When opening a Python folder, Visual Studio creates several hidden folders to manage settings related to the project. To see these folders (and any other hidden files and folders, such as the
.git
folder), select the Show All Files toolbar button: -
To run the code, you first need to identify the startup or primary program file. In the example shown here, the startup file contemplate-koans.py. Right-click that file and select Set as Startup Item.
Important
If your startup item is not located in the root of the folder you opened, you must also add a line to the launch configuration JSON file as described in the section, Set a working directory.
-
Run the code by pressing Ctrl+F5 or selecting Debug > Start without Debugging. You can also select the toolbar button that shows the startup item with a play button, which runs code in the Visual Studio debugger. In all cases, Visual Studio detects that your startup item is a Python file, so it automatically runs the code in the default Python environment. (That environment is shown to the right of the startup item on the toolbar.)
-
The program’s output appears in a separate command window:
-
To run the code in a different environment, select that environment from the drop-down control on the toolbar, then launch the startup item again.
-
To close the folder in Visual Studio, select the File > Close folder menu command.
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).
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.
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.
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:
- Command Palette to access all commands by typing keywords.
- Fully customizable keyboard shortcuts.
- Jupyter extension for data science. Run Jupyter notebook within the IDE.
- Auto linting and formatting.
- Debugging and Testing.
- Git integration.
- Custom code snippets.
- 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.
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.
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 (*).
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
Feature details
Learn more about the rich features of the Python extension:
- IntelliSense: Edit your code with auto-completion, code navigation, syntax checking and more
- Linting: Get additional code analysis with Pylint, Flake8 and more
- Code formatting: Format your code with black, autopep or yapf
- Debugging: Debug your Python scripts, web apps, remote or multi-threaded processes
- Testing: Run and debug tests through the Test Explorer with unittest or pytest.
- Jupyter Notebooks: Create and edit Jupyter Notebooks, add and run code cells, render plots, visualize variables through the variable explorer, visualize dataframes with the data viewer, and more
- Environments: Automatically activate and switch between virtualenv, venv, pipenv, conda and pyenv environments
- Refactoring: Restructure your Python code with variable extraction and method extraction. Additionally, there is componentized support to enable additional refactoring, such as import sorting, through extensions including isort and Ruff.
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.
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!
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)
Python profile template
Profiles let you quickly switch your extensions, settings, and UI layout depending on your current project or task. To help you get started with Python development, you can use the Python profile template, which is a curated profile with useful extensions, settings, and snippets. You can use the profile template as is or use it as a starting point to customize further for you own workflows.
You select a profile template through the Profiles > Create Profile… dropdown:
Once you select a profile template, you can review the settings and extensions, and remove individual items if you don’t want to include them in your new Profile. After creating the new profile based on the template, changes made to settings, extensions, or UI are persisted in your profile.
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:
-
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
-
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!
Enhance completions with AI
GitHub Copilot is an AI-powered code completion tool that helps you write code faster and smarter. You can use the GitHub Copilot extension in VS Code to generate code, or to learn from the code it generates.
GitHub Copilot provides suggestions for languages beyond Python and a wide variety of frameworks, including JavaScript, TypeScript, Ruby, Go, C# and C++.
You can learn more about how to get started with Copilot in the Copilot documentation.
Data and telemetry
The Microsoft Python Extension for Visual Studio Code collects usage
data and sends it to Microsoft to help improve our products and
services. Read our
privacy statement to
learn more. This extension respects the
telemetry.enableTelemetry
setting which you can learn more about at
https://code.visualstudio.com/docs/supporting/faq#_how-to-disable-telemetry-reporting.