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.
2.Register for a PyPI account and get an authentication token#
The Python Package Index (PyPI) is the official online software repository for Python. A software repository is a storage location for downloadable software, like Python packages. In this book we’ll be publishing a package to PyPI. Before publishing packages to PyPI, it is typical to “test drive” their publication on TestPyPI, which is a test version of PyPI. To follow along with this book, you should register for a TestPyPI account on the TestPyPI website and a PyPI account on the PyPI website.
Both TestPyPI and PyPI will require an authentication token for publishing your Python packages using Poetry. You should obtain one from each of these repositories and store them in a safe place, such as some password management software (e.g., LastPass or 1Password). You can find the location of where to generate an authentication token by logging into the repository, and choosing “Account Settings” from the drop down menu under your username. Then scroll down to “API tokens” and click “Add API token”. You will then be prompted to give the token a name and to set its scope. For scope choose “Entire account (all projects)”.
Download and install the Python workload
Complete the following steps to download and install the Python workload.
-
Download and run the latest Visual Studio Installer for Windows. Python support is present in release 15.2 and later. If you have Visual Studio installed already, open Visual Studio and run the installer by selecting Tools > Get Tools and Features.
Tip
The Community edition is for individual developers, classroom learning, academic research, and open source development. For other uses, install Visual Studio Professional or Visual Studio Enterprise.
-
The installer provides a list of workloads that are groups of related options for specific development areas. For Python, select the Python development workload and select Install:
Python installation options Description Python distributions Choose any combination of Python distribution that you plan to work with. Common options include 32-bit and 64-bit variants of Python 2, Python 3, Miniconda, Anaconda 2, and Anaconda 3. Each option includes the distribution’s interpreter, runtime, and libraries. Anaconda, specifically, is an open data science platform that includes a wide range of preinstalled packages. Visual Studio automatically detects existing Python installations. For more information, see The Python Environments window. Also, if a newer version of Python is available than the version shown in the installer, you can install the new version separately and Visual Studio detects it. Cookiecutter template support Install the Cookiecutter graphical UI to discover templates, input template options, and create projects and files. For more information, see Use the Cookiecutter extension. Python web support Install tools for web development including HTML, CSS, and JavaScript editing support, along with templates for projects using the Bottle, Flask, and Django frameworks. For more information, see Python web project templates. Python native development tools Install the C++ compiler and other necessary components to develop native extensions for Python. For more information, see Create a C++ extension for Python. Also install the Desktop development with C++ workload for full C++ support. By default, the Python workload installs for all users on a computer under:
%ProgramFiles%\Microsoft Visual Studio\
\
Common7\IDE\Extensions\Microsoft\Python
where
is 2022 and
is Community, Professional, or Enterprise.
%ProgramFiles(x86)%\Microsoft Visual Studio\
\
Common7\IDE\Extensions\Microsoft\Python
where
is 2019 or 2017 and
is Community, Professional, or Enterprise.
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.
-
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.
-
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.
-
Select the Python Application template, specify a name for the project, and select OK.
-
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.
-
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.
-
Select the Python Application template, and select Next.
-
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.
Mojave privacy protections
After upgrading to macOS Mojave version, you may see dialogs saying “Visual Studio Code would like to access your {calendar/contacts/photos}.” This is due to the new privacy protections in Mojave and is not specific to VS Code. The same dialogs may be displayed when running other applications as well. The dialog is shown once for each type of personal data and it is fine to choose Don’t Allow since VS Code does not need access to those folders.
Feedback
Submit and view feedback for
Cài đặt và cấu hình Python3 cho Visual Studio Code hệ điều hành MacOS
Bài đăng này đã không được cập nhật trong 4 năm
Trước khi cài đặt Visual Studio Code bạn cần phải cài đặt Python. Xem bài viết hướng dẫn dưới đây:
Visual Studio Code: là mã nguồn mở, là công cụ đắc lực cho việc lập trình với ưu điểm là nhẹ và hỗ trợ nhiều ngôn ngữ: Python, ReactJS, Node,…
Chúng ta sẽ sử dụng Homebrew để cài đặt Visual Studio Code.
Homebrew là công cụ quản lý package phổ biến, được sử dụng để cài đặt phần mềm mã nguồn mở như là: Node.
Cài đặt HomeBrew
Mở ứng dụng terminal (/Applications/Utilities/Terminal) và chạy lệnh sau:
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Trong lúc cài đặt sẽ yêu cầu bạn nhập mật khẩu, bạn nhập mật khẩu máy tính vào, và đợi cài đặt trong 1-2 phút, tuỳ theo tốc độ mạng.
Cài đặt Visual Studio Code
Trước khi cài đặt Visual Studio Code, chúng ta cập nhập HomeBrew bằng lệnh sau:
brew update brew tap caskroom/cask
Sau đó cài đặt Visual Studio Code bằng cách nhập lệnh dưới đây:
brew cask install visual-studio-code
Mở Visual Studio Code (/Applications/Visual Studio Code). Tại màn hình welcome chọn
Add workspace folder...
tạo thư mục với tên là
hello
Click phải vào thư mục, chọn
New File
đặt tên file là
hello_world.py
.
Nhập đoạn code sau vào file
hello_world.py
.
print("Hello to Python world!")
Click vào góc dưới bên trái màn hình chọn phiên bản Python mới nhất, ở đây mình chọn 3.8.1
Click phải vào khung soạn thảo văn bản, chọn
Run Python File in Terminal
Kết quả sẽ xuất ra là
Hello to Python world!
Tổng kết
Homebrew là công cụ quản lý package phổ biến, được sử dụng để cài đặt phần mềm mã nguồn mở. Sử dụng Homebrew giúp cho chúng ta cài đặt Visual Studio Code trong thời gian ngắn.
Trong lúc cài đặt. Nếu có vấn đề khi cài đặt, bạn hãy comment bên dưới, mình sẽ hỗ trợ trong thời gian sớm nhất!
Cảm ơn các bạn đã quan tâm bài viết này.
Tham khảo Cài đặt Homebrew.
All rights reserved
Common questions
Why do I see “Visual Studio Code would like access to your calendar.”
If you are running macOS Mojave version, you may see dialogs saying “Visual Studio Code would like to access your {calendar/contacts/photos}.” This is due to the new privacy protections in Mojave discussed above. It is fine to choose Don’t Allow since VS Code does not need access to those folders.
VS Code fails to update
If VS Code doesn’t update once it restarts, it might be set under quarantine by macOS. Follow the steps in this issue for resolution.
Does VS Code run on Apple silicon machines?
Yes, VS Code supports macOS Arm64 builds that can run on Macs with the Apple silicon chipsets. You can install the Universal build, which includes both Intel and Apple silicon builds, or one of the platform specific builds.
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.
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!
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.
2.Developing with Docker#
If you have issues installing or using any of the software in this book on your specific operating system, or would prefer to use Docker to help develop your Python packages, we have provided an alternative software setup with Docker that has everything you need already installed to get started. Docker is a platform that allows you to run and develop software in an isolated environment called a container. Images contain the instructions required to create a container.
We have developed Docker images to support Python package development in Visual Studio Code or JupyterLab, and we describe minimal workflows for using these images to follow along with this book in the sections below. Feel free to customize these images and/or workflows to suit your specific use cases. We will continue to maintain the Docker images via their GitHub repositories (py-pkgs/docker-vscode and py-pkgs/docker-jupyter) to support readers of this book into the future.
2.6.Docker with Visual Studio Code#
To develop with Docker inside Visual Studio Code, you can consult the Visual Studio Code official container tutorial, or try following the steps below:
-
Install Visual Studio Code from the official website.
-
Install and configure Docker Desktop for your operating system following the instructions on the official website.
-
Once docker is installed, open a command-line interface and pull the
pypkgs/vscode
docker image by running the following command:$ docker pull pypkgs/vscode
-
From Visual Studio Code, open/create the working directory you want to develop in (this can be called anything and located wherever you like on your file system).
-
In Visual Studio Code, open the Extensions tab on the Visual Studio Code activity bar and search for the “Dev Containers” extension in the search bar. Install this extension if it is not already installed.
-
Create a file called
.devcontainer.json
in your current working directory (be sure to include the period at the beginning of the file name). This file will tell Visual Studio Code how to run in a Docker container. You can read more about this configuration in the official documentation, but for now, a minimal set up requires adding the following content to that file:{ “name”: “poetry”, “image”: “pypkgs/vscode”, “extensions”: [“ms-python.python”], }
-
Now, open the Visual Studio Code Command Palette and search for and select the command “Dev Containers: Reopen in Container”. This command will open Visual Studio Code inside a container made using the
pypkgs/vscode
Docker image. After Visual Studio Code finishes opening in the container, test that you have access to the three pre-installed pieces of packaging software we need by opening the integrated terminal and trying the following commands:$ poetry –version $ conda –version $ cookiecutter –version
-
Your development environment is now set up, and you can work with Visual Studio Code as if everything were running locally on your machine (except now your development environment exists inside a container). If you exit Visual Studio Code, your container will stop but will persist on your machine. It can be re-opened at a later time using the “Dev Containers: Reopen in Container” command we used in step 7.
-
If you want to completely remove your development container to free up memory on your machine, first find the container’s ID:
$ docker ps -a
CONTAINER ID IMAGE 762bca6eb51e pypkgs/vscode
-
Then use the
docker rm
command combined with the container’s ID. This will remove the container, including any packages or virtual environments installed in it. However, any files and directories you created will persist on your machine.$ docker rm 762bca6eb51e
2.6.Docker with JupyterLab#
To develop with Docker in JupyterLab follow the instructions below. Helpful information and tutorials can also be found in the Jupyter Docker Stacks documentation.
-
Install and configure Docker Desktop for your operating system following the instructions on the official website.
-
Once Docker has been installed, open a command-line interface and pull the
pypkgs/jupyter
Docker image by running the
docker pull
command as follows:$ docker pull pypkgs/jupyter
-
From the command line, navigate to the directory you want to develop in (this can be called anything and located wherever you like on your file system).
-
Start a new container from that directory by running the following command from the command line:
$ docker run -p 8888:8888 \ -v “${PWD}”:/home/jovyan/work \ pypkgs/jupyter
Tip
In the command above,
-p
binds port 8888 in the container to port 8888 on the host machine and
-v
mounts the current directory into the container at the location
/home/jovyan/work
. Windows users that run into issues with the command above may need to try double-slashes in the volume mount path, for example:
-v /$(pwd)://home//jovyan//work
. You can read more about the
docker run
command and its arguments in the Docker command-line interface documentation. -
Copy the unique URL printed to screen (that looks something like this:
http://127.0.0.1:8888/lab?token=45d53a348580b3acfafa
) to your browser. This will open an instance of JupyterLab running inside a Docker container. -
Navigate to the
work
directory in JupyterLab. This is where you can develop and create new files and directories that will persist in the directory from where you launched your container. -
Test that you have access to the three pre-installed pieces of packaging software we need by opening a terminal in JupyterLab and trying the following commands:
$ poetry –version $ conda –version $ cookiecutter –version
-
When you’ve finished a working session, you can exit JupyterLab, and kill your terminal, and your container will persist. You can restart the container and launch JupyterLab again by first finding its ID:
$ docker ps -a
CONTAINER ID IMAGE 653daa2cd48e pypkgs/jupyter
-
Then, to restart the container and launch JupyterLab, use the
docker start -a
command combined with the container’s ID:$ docker start -a 653daa2cd48e
-
If you want to completely remove the container you can use the
docker rm
command. This will remove the container, including any packages or virtual environments installed in it. However, all files and directories added to the
work
directory will persist on your machine.$ docker rm 653daa2cd48e
Visual Studio Code on macOS
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.
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.
Modern development with .NET 7
Visual Studio 2022 for Mac includes nearly everything you’ll need for .NET 7 development, from responsive C# web UIs in Blazor to event-driven solutions using Azure Functions.
Advanced IntelliSense
With the power of Roslyn, Visual Studio for Mac brings IntelliSense to your fingertips. IntelliSense describes APIs as you type and uses auto-completion to increase the speed and accuracy of how you write code.
Quick Info tool tips let you inspect API definitions. Squiggly lines in the editor highlight issues in real time as you type.
Intelligent Refactoring
As your project grows, chances are, you’ll find yourself restructuring and refactoring code that you or someone else wrote earlier. That’s a whole lot easier when Visual Studio for Mac takes care of the heavy lifting for you.
Learn more about Xamarin.Essentials
Integrated Version Control
Visual Studio 2022 has built-in support for Git version control to clone, create, and open your own repositories. The Git tool window has everything you need for committing and pushing changes to code.
Powerful Debugging
Integrated debugging is a core part of every Visual Studio product. You can step through your code and look at the values stored in variables, set watches on variables to see when values change, examine the execution path of your code, and just about anything else you need to check out under the hood.
.NET
Build modern solutions for the web and cloud with ASP.NET Core.
Xamarin
Build apps for iOS, Android, macOS, and more with C# and .NET
Unity
Build your next game or real-time 3D Unity application with best-in-class debugging.
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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.
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
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
Install Python support in Visual Studio
Python support is available only on Visual Studio for Windows. On Mac and Linux, Python support is available through Visual Studio Code.
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.
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 (*).
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!
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.
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
Next steps
Once you have installed VS Code, these topics will help you learn more about VS Code:
- Additional Components – Learn how to install Git, Node.js, TypeScript, and tools like Yeoman.
- User Interface – A quick orientation around VS Code.
- User/Workspace Settings – Learn how to configure VS Code to your preferences settings.
Feedback
Submit and view feedback for
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.
Jupyter notebooks
To enable Python support for Jupyter notebook files (
.ipynb
) in VS Code, you can install the Jupyter extension. The Python and Jupyter extensions work together to give you a great Notebook experience in VS Code, providing you the ability to directly view and modify code cells with IntelliSense support, as well as run and debug them.
You can also convert and open the notebook as a Python code file through the Jupyter: Export to Python Script command. The notebook’s cells are delimited in the Python file with
#%%
comments, and the Jupyter extension shows Run Cell or Run Below CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:
You can also connect to a remote Jupyter server to run your notebooks. For more information, see Jupyter support.
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.
Test your install
Quickly check your installation of Python support:
-
Launch Visual Studio.
-
Select Alt + I to open the Python Interactive window.
-
In the window, enter the statement
2+2
.The statement output
displays in the window. If you don’t see the correct output, recheck your steps.
2.Installing software#
Section 2.2.1 and Section 2.2.2 describe how to install the software you’ll need to develop a Python package and follow along with the text and examples in this book. However, we also support an alternative setup with Docker that has everything you need already installed to get started. The Docker approach is recommended for anyone that runs into issues installing or using any of the software below on their specific operating system, or anyone who would simply prefer to use Docker — if that’s you, skip to Section 2.3 for now, and we’ll describe the Docker setup later in Section 2.6.
2.2.Installing Python#
We recommend installing the latest version of Python via the Miniconda distribution by following the instructions in the Miniconda documentation. Miniconda is a lightweight version of the popular Anaconda distribution. If you have previously installed the Anaconda or Miniconda distribution feel free to skip to Section 2.2.2.
If you are unfamiliar with Miniconda and Anaconda, they are distributions of Python that also include the
conda
package and environment manager, and a number of other useful packages. The difference between Anaconda and Miniconda is that Anaconda installs over 250 additional packages (many of which you might never use), while Miniconda is a much smaller distribution that comes bundled with just a few key packages; you can then install additional packages as you need them using the command
conda install
.
conda
is a piece of software that supports the process of installing and updating software (like Python packages). It is also an environment manager, which is the key function we’ll be using it for in this book. An environment manager helps you create “virtual environments” on your machine, where you can safely install different packages and their dependencies in an isolated location. Installing all the packages you need in the same place (i.e., the system default location) can be problematic because different packages often depend on different versions of the same dependencies; as you install more packages, you’ll inevitably get conflicts between dependencies, and your code will start to break. Virtual environments help you compartmentalize and isolate the packages you are using for different projects to avoid this issue. You can read more about virtual environments in the
conda
documentation. While alternative package and environment managers exist, we choose to use
conda
in this book because of its popularity, ease-of-use, and ability to handle any software stack (not just Python).
2.2.Install packaging software#
Once you’ve installed the Miniconda distribution, ensure that Python and
conda
are up to date by running the following command at the command line:
$ conda update –all
Now we’ll install the two main pieces of software we’ll be using to help us create Python packages in this book:
-
poetry
: software that will help us build our own Python packages.
poetry
is under active development, thus we recommend installing Poetry using their official installer while referring to their official
poetry
documentation for detailed installation instructions and support. -
cookiecutter
: software that will help us create packages from pre-made templates. It can be installed with
conda
as follows:$ conda install -c conda-forge cookiecutter
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.
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
Install Python support in Visual Studio
Python support is available only on Visual Studio for Windows. On Mac and Linux, Python support is available through 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.
Download and install the Python workload
Complete the following steps to download and install the Python workload.
-
Download and run the latest Visual Studio Installer for Windows. Python support is present in release 15.2 and later. If you have Visual Studio installed already, open Visual Studio and run the installer by selecting Tools > Get Tools and Features.
Tip
The Community edition is for individual developers, classroom learning, academic research, and open source development. For other uses, install Visual Studio Professional or Visual Studio Enterprise.
-
The installer provides a list of workloads that are groups of related options for specific development areas. For Python, select the Python development workload and select Install:
Python installation options Description Python distributions Choose any combination of Python distribution that you plan to work with. Common options include 32-bit and 64-bit variants of Python 2, Python 3, Miniconda, Anaconda 2, and Anaconda 3. Each option includes the distribution’s interpreter, runtime, and libraries. Anaconda, specifically, is an open data science platform that includes a wide range of preinstalled packages. Visual Studio automatically detects existing Python installations. For more information, see The Python Environments window. Also, if a newer version of Python is available than the version shown in the installer, you can install the new version separately and Visual Studio detects it. Cookiecutter template support Install the Cookiecutter graphical UI to discover templates, input template options, and create projects and files. For more information, see Use the Cookiecutter extension. Python web support Install tools for web development including HTML, CSS, and JavaScript editing support, along with templates for projects using the Bottle, Flask, and Django frameworks. For more information, see Python web project templates. Python native development tools Install the C++ compiler and other necessary components to develop native extensions for Python. For more information, see Create a C++ extension for Python. Also install the Desktop development with C++ workload for full C++ support. By default, the Python workload installs for all users on a computer under:
%ProgramFiles%\Microsoft Visual Studio\
\
Common7\IDE\Extensions\Microsoft\Python
where
is 2022 and
is Community, Professional, or Enterprise.
%ProgramFiles(x86)%\Microsoft Visual Studio\
\
Common7\IDE\Extensions\Microsoft\Python
where
is 2019 or 2017 and
is Community, Professional, or Enterprise.
2.Python integrated development environments#
A Python integrated development environment (IDE) will make the process of creating Python packages significantly easier. An IDE is a piece of software that provides advanced functionality for code development, such as directory and file creation and navigation, autocomplete, debugging, and syntax highlighting, to name a few. An IDE will save you time and help you write better code. Commonly used free Python IDEs include Visual Studio Code, Atom, Sublime Text, Spyder, and PyCharm Community Edition. For those more familiar with the Jupyter ecosystem, JupyterLab is a suitable browser-based IDE. Finally, for the R community, the RStudio IDE also supports Python.
You’ll be able to follow along with the examples presented in this book regardless of what IDE you choose to develop your Python code in. If you don’t know which IDE to use, we recommend starting with Visual Studio Code. Below we briefly describe how to set up Visual Studio Code, JupyterLab, and RStudio as Python IDEs (these are the IDEs we personally use in our day-to-day work). If you’d like to use Docker to help develop Python packages and follow along with this book, we’ll describe how to do so with Visual Studio Code or JupyterLab in Section 2.6.
2.5.Visual Studio Code#
You can download Visual Studio Code (VS Code) from the Visual Studio Code website. Once you’ve installed VS Code, you should install the “Python” extension from the VS Code Marketplace. To do this, follow the steps listed below and illustrated in Fig. 2.1:
-
Open the Marketplace by clicking the Extensions tab on the VS Code activity bar.
-
Search for “Python” in the search bar.
-
Select the extension named “Python” and then click Install.
Fig. 2.1 Installing the Python extension in Visual Studio Code.#
Once this is done, you have everything you need to start creating packages! For example, you can create files and directories from the File Explorer tab on the VS Code activity bar, and you can open up an integrated CLI by selecting Terminal from the View menu. Fig. 2.2 shows an example of executing a Python .py file from the command line in VS Code.
Fig. 2.2 Executing a simple Python file called hello-world.py from the integrated terminal in Visual Studio Code.#
We recommend you take a look at the VS Code Getting Started Guide to learn more about using VS Code. While you don’t need to install any additional extensions to start creating packages in VS Code, there are many extensions available that can support and streamline your programming workflows in VS Code. Below are a few we recommend installing to support the workflows we use in this book (you can search for and install these from the “Marketplace” as we did earlier):
-
Python Docstring Generator: an extension to quickly generate documentation strings (docstrings) for Python functions.
-
Markdown All in One: an extension that provides keyboard shortcuts, automatic table of contents, and preview functionality for Markdown files. Markdown is a plain-text markup language that we’ll use and learn about in this book.
2.5.JupyterLab#
For those comfortable in the Jupyter ecosystem feel free to stay there to create your Python packages! JupyterLab is a browser-based IDE that supports all of the core functionality we need to create packages. As per the JupyterLab installation instructions, you can install JupyterLab with:
$ conda install -c conda-forge jupyterlab
Once installed, you can launch JupyterLab from your current directory by typing the following command in your terminal:
$ jupyter lab
In JupyterLab, you can create files and directories from the File Browser and can open up an integrated terminal from the File menu. Fig. 2.3 shows an example of executing a Python .py file from the command line in JupyterLab.
Fig. 2.3 Executing a simple Python file called hello-world.py from a terminal in JupyterLab.#
We recommend you take a look at the JupyterLab documentation to learn more about how to use Jupyterlab. In particular, we’ll note that, like VS Code, JupyterLab supports an ecosystem of extensions that can add additional functionality to the IDE. We won’t install any here, but you can browse them in the JupyterLab Extension Manager if you’re interested.
2.5.RStudio#
Users with an R background may prefer to stay in the RStudio IDE. We recommend installing the most recent version of the IDE from the RStudio website (we recommend installing at least version ^1.4) and then installing the most recent version of R from CRAN. To use Python in RStudio, you will need to install the reticulate R package by typing the following in the R console inside RStudio:
install.packages(“reticulate”)
When installing reticulate, you may be prompted to install the Anaconda distribution. We already installed the Miniconda distribution of Python in Section 2.2.1, so answer “no” to this prompt. Before being able to use Python in RStudio, you will need to configure
reticulate
. We will briefly describe how to do this for different operating systems below, but we encourage you to look at the
reticulate
documentation for more help.
Mac and Linux
-
Find the path to the Python interpreter installed with Miniconda by typing
which python
at the command line. -
Open (or create) an
.Rprofile
file in your HOME directory and add the line
Sys.setenv(RETICULATE_PYTHON = "path_to_python")
, where
"path_to_python"
is the path identified in step 1. -
Open (or create) a
.bash_profile
file in your HOME directory and add the line
export PATH="/opt/miniconda3/bin:$PATH"
, replacing
/opt/miniconda3/bin
with the path you identified in step 1 but without the
python
at the end. -
Restart R.
-
Try using Python in RStudio by running the following in the R console:
library(reticulate) repl_python()
Windows
-
Find the path to the Python interpreter installed with Miniconda by opening an Anaconda Prompt from the Start Menu and typing
where python
in a terminal. -
Open (or create) an
.Rprofile
file in your HOME directory and add the line
Sys.setenv(RETICULATE_PYTHON = "path_to_python")
, where
"path_to_python"
is the path identified in step 1. Note that in Windows, you need
\\
instead ofto separate the directories; for example your path might look like:
C:\\Users\\miniconda3\\python.exe
. -
Open (or create) a
.bash_profile
file in your HOME directory and add the line
export PATH="/opt/miniconda3/bin:$PATH"
, replacing
/opt/miniconda3/bin
with the path you identified in step 1 but without the
python
at the end. -
Restart R.
-
Try using Python in RStudio by running the following in the R console:
library(reticulate) repl_python()
Fig. 2.4 shows an example of executing Python code interactively within the RStudio console.
Fig. 2.4 Executing Python code in RStudio.#
Installation
- Download Visual Studio Code for macOS.
- Open the browser’s download list and locate the downloaded app or archive.
- If archive, extract the archive contents. Use double-click for some browsers or select the ‘magnifying glass’ icon with Safari.
-
Drag
Visual Studio Code.app
to the Applications folder, making it available in the macOS Launchpad. - Open VS Code from the Applications folder, by double clicking the icon.
- Add VS Code to your Dock by right-clicking on the icon, located in the Dock, to bring up the context menu and choosing Options, Keep in Dock.
Test your install
Quickly check your installation of Python support:
-
Launch Visual Studio.
-
Select Alt + I to open the Python Interactive window.
-
In the window, enter the statement
2+2
.The statement output
displays in the window. If you don’t see the correct output, recheck your steps.
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.
2.Set up Git and GitHub#
If you’re not using a version control system, we highly recommend you get into the habit! A version control system tracks changes to the file(s) of your project in a clear and organized way (no more “document_1.doc”, “document_1_new.doc”, “document_final.doc”, etc.). As a result, a version control system contains a full history of all the revisions made to your project, which you can view and retrieve at any time. You don’t need to use or be familiar with version control to read this book, but if you’re serious about creating Python packages, version control will become an invaluable part of your workflow, so now is a good time to learn!
There are many version control systems available, but the most common is Git and we’ll be using it throughout this book. You can download Git by following the instructions in the Git documentation. Git helps track changes to a project on a local computer, but what if we want to collaborate with others? Or, what happens if your computer crashes and you lose all your work? That’s where GitHub comes in. GitHub is one of many online services for hosting Git-managed projects. GitHub helps you create an online copy of your local Git repository, which acts as a backup of your local work and allows others to easily and transparently collaborate on your project. You can sign up for a free GitHub account on the GitHub website. Once signed up, you should also set up SSH authentication to help push and pull files from GitHub by following the steps in the official GitHub documentation.
We assume that those who choose to follow the optional version control sections of this book have basic familiarity with Git and GitHub (or equivalent). Two excellent learning resources are Happy Git and GitHub for the useR2 and Research Software Engineering with Python3.
Launching from the command line
You can also run VS Code from the terminal by typing ‘code’ after adding it to the path:
- Launch VS Code.
- Open the Command Palette (Cmd+Shift+P) and type ‘shell command’ to find the Shell Command: Install ‘code’ command in PATH command.
-
Restart the terminal for the new
$PATH
value to take effect. You’ll be able to type ‘code .’ in any folder to start editing files in that folder.
Note: If you still have the old
code
alias in your
.bash_profile
(or equivalent) from an early VS Code version, remove it and replace it by executing the Shell Command: Install ‘code’ command in PATH command.
Alternative manual instructions
Instead of running the command above, you can manually add VS Code to your path, to do so run the following commands:
cat << EOF >> ~/.bash_profile # Add Visual Studio Code (code) export PATH="\$PATH:/Applications/Visual Studio Code.app/Contents/Resources/app/bin" EOF
Start a new terminal to pick up your
.bash_profile
changes.
Note: The leading slash is required to prevent
$PATH
from expanding during the concatenation. Remove the leading slash if you want to run the export command directly in a terminal.
Note: Since
zsh
became the default shell in macOS Catalina, run the following commands to add VS Code to your path:
cat << EOF >> ~/.zprofile # Add Visual Studio Code (code) export PATH="\$PATH:/Applications/Visual Studio Code.app/Contents/Resources/app/bin" EOF
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).
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.
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