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.
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.
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
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 (*).
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
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.
Code Actions
Code Actions (also known as Quick Fixes) are provided to help fix issues when there are warnings in your code. These helpful hints are displayed in the editor left margin as a lightbulb (💡). Select the light bulb to display Code Action options. These Code Action can come from extensions such as Python, Pylance, or VS Code itself. For more information about Code Actions, see Python Quick Fixes.
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!
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.
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.
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.
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.
Conclusion
VSCode is not just a code editor. It is a complete ecosystem for efficient Python development. It provides us with shortcuts, Commands Palette, IntelliSense, linting, formatting, debugging, formatting, Git integrations, Jupyter notebook, third-party extensions, and a fully customizable development experience.
VSCode is highly recommended to beginners who are learning the basics of Python and data science. Complete Data Scientist with a Python career track to become a master in Python and data science. The career track consists of 25 courses and six projects to prepare you to become a professional data scientist.
A Deep Dive into the Phi-2 Model
Python List Size: 8 Different Methods for Finding the Length of a List in Python
An End-to-End ML Model Monitoring Workflow with NannyML in Python
Bex Tuychiev
15 min
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.
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.
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
Types of Python environments
Global environments
By default, any Python interpreter installed runs in its own global environment. For example, if you just run
python
,
python3
, or
py
at a new terminal (depending on how you installed Python), you’re running in that interpreter’s global environment. Any packages that you install or uninstall affect the global environment and all programs that you run within it.
Tip: In Python, it is best practice to create a workspace-specific environment, for example, by using a local environment.
Local environments
There are two types of environments that you can create for your workspace: virtual and conda. These environments allow you to install packages without affecting other environments, isolating your workspace’s package installations.
Virtual environments
A virtual environment is a built-in way to create an environment. A virtual environment creates a folder that contains a copy (or symlink) to a specific interpreter. When you install packages into a virtual environment it will end up in this new folder, and thus isolated from other packages used by other workspaces.
Note: While it’s possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension.
Conda environments
A conda environment is a Python environment that’s managed using the
conda
package manager (see Getting started with conda).Choosing between conda and virtual environments depends on your packaging needs, team standards, etc.
Python environment tools
The following table lists the various tools involved with Python environments:
Tool | Definition and Purpose |
pip | The Python package manager that installs and updates packages. It’s installed with Python 3.9+ by default (unless you are on a Debian-based OS; install |
venv | Allows you to manage separate package installations for different projects and is installed with Python 3 by default (unless you are on a Debian-based OS; install |
conda | Installed with Miniconda. It can be used to manage both packages and virtual environments. Generally used for data science projects. |
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.
More Python resources
- Getting Started with Python in VS Code – Learn how to edit, run, and debug code in VS Code.
- Virtual Environments and Packages (Python.org) – Learn more about virtual environments and packages.
- Installing Python Modules (Python.org) – Learn how to install Python modules.
- Python tutorial (Python.org) – Learn more about the Python language.
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.
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!
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!
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.
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).
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.
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.
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.
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.
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.
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!
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 environments in VS Code
An “environment” in Python is the context in which a Python program runs that consists of an interpreter and any number of installed packages.
Note: If you’d like to become more familiar with the Python programming language, review More Python resources.
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.
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.
Environment variables
Environment variable definitions file
An environment variable definitions file is a text file containing key-value pairs in the form of
environment_variable=value
, with used for comments. Multiline values aren’t supported, but references to previously defined environment variables are allowed. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but aren’t applied to the terminal.
Note: Environment variable definitions files are not necessarily cross-platform. For instance, while Unix uses
as a path separator in environment variables, Windows uses. There is no normalization of such operating system differences, and so you need to make sure any environment definitions file use values that are compatible with your operating system.
By default, the Python extension looks for and loads a file named
.env
in the current workspace folder, then applies those definitions. The file is identified by the default entry
"python.envFile": "${workspaceFolder}/.env"
in your user settings (see General Python settings). You can change the
python.envFile
setting at any time to use a different definitions file.
Note: Environment variable definitions files are not used in all situations where environment variables are available for use. Unless Visual Studio Code documentation states otherwise, these only affect certain scenarios as per their definition. For example, the extension doesn’t use environment variable definitions files when resolving setting values.
A debug configuration also contains an
envFile
property that also defaults to the
.env
file in the current workspace (see Debugging – Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default
.env
file.
For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:
dev.env file
# dev.env - development configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/dev/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/dev MYPROJECT_DBUSER=devadmin MYPROJECT_DBPASSWORD=!dfka**213=
prod.env file
# prod.env - production configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/ MYPROJECT_DBUSER=coreuser MYPROJECT_DBPASSWORD=kKKfa98*11@
You can then set the
python.envFile
setting to
${workspaceFolder}/prod.env
, then set the
envFile
property in the debug configuration to
${workspaceFolder}/dev.env
.
Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. All
env
variables defined in the
launch.json
file will override variables contained in the
.env
file, specified by the
python.envFile
setting (user or workspace). Similarly,
env
variables defined in the
launch.json
file will override the environment variables defined in the
envFile
that are specified in
launch.json
.
Use of the PYTHONPATH variable
The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (
terminal.integrated.env.*
) and/or within an
.env
file.
When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn’t routed through the terminal, such as the use of a linter or formatter, then this setting won’t have an effect on module look-up.
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.
Debugging and Testing in VSCode
Debugging
The Python extension comes with Debugging for all kinds of applications like multi-threaded, web, and remote applications. We can set breakpoints, inspect data, and run programs step by step.
Select a debug configuration
Launch the debug tab by clicking on the debug icon on the action bar or by using the keyboard shortcut Ctrl + Shift +D. To customize Debug options, click on create a launch.json file and select Python File.
Debug Panel
Run the Debug by clicking on the blue button Run and Debug, and it will run the Python file and show us the Variables, Watch, Call Stack, and breakpoints.
Quick debug
For quick debugging, you can always click on the down arrow beside the Run button and select Debug Python File.
Testing
The Python extension supports unittest and pytest testing frameworks. Instead of reading the test results in a terminal, you can review and resolve the issues within the Testing tab in an active bar.
Configure Python tests
After clicking on the Testing button, we will click on the Configure Python Tests button and select the testing framework. Usually, VSCode automatically detects the framework and displays all the tests in a tree view.
Learn about Python unit testing, implementing Python’s pytest testing framework by following our how to use pytest for unit testing tutorial.
Note: The testing example that we are using is from Visual Studio Code official documentation.
Run the unittest
We can run the Unit test by clicking on the Run Test button in the Testing tab and analyzing the results.
As we can observe, 1 of 2 tests have passed, and it has displayed the reasoning behind the failed result. VSCode testing is highly interactive and user-friendly.
Run, debug, and test
Now that you are more familiar with Python in VS Code, let’s learn how to run, debug, and test your code.
Run
There are a few ways to run Python code in VS Code.
To run the Python script you have open on the editor, select the Run Python File in Terminal play button in the top-right of the editor.
There are also additional ways you can iteratively run snippets of your Python code within VS Code:
- 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.
Debug
The debugger is a helpful tool that allows you to inspect the flow of your code execution and more easily identify errors, as well as explore how your variables and data change as your program is run. You can start debugging by setting a breakpoint in your Python project by clicking in the gutter next to the line you wish to inspect.
To start debugging, initialize the debugger by pressing F5. Since this is your first time debugging this file, a configuration menu will open allowing you to select the type of application you want to debug. If it’s a Python script, you can select Python File.
Once your program reaches the breakpoint, it will stop and allow you to track data in the Python Debug console, and progress through your program using the debug toolbar.
For a deeper dive into Python debugging functionality, see Python debugging in VS Code.
Test
The Python extension provides robust testing support for Unittest and pytest.
You can configure Python tests through the Testing view on the Activity Bar by selecting Configure Python Tests and selecting your test framework of choice.
You can also create tests for your Python project, which the Python extension will attempt to discover once your framework of choice is configured. The Python extension also allows you to run and debug your tests in the Testing view and inspect the test run output in the Test Results panel.
For a comprehensive look at testing functionality, see Python testing in VS Code.
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.
How to create and open a Python project or file
If you have an existing Python project you wish to work on in VS Code, you can begin by opening your folder or file from the VS Code Welcome page or File Explorer view, or by selecting File > Open Folder (Ctrl+K Ctrl+O) or File > Open File (⌘O (Windows, Linux Ctrl+O)).
You can create a new Python file by selecting New File on the VS Code Welcome page and then selecting Python file, or by navigating to File > New File ().
Tip: If you already have a workspace folder open in VS Code, you can add new files or folders directly into your existing project. You can create new folders and files by using the corresponding New Folder or New File icons on the top level folder in the File Explorer view.
Working with Python interpreters
Select and activate an environment
The Python extension tries to find and then select what it deems the best environment for the workspace. If you would prefer to select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).
Note: If the Python extension doesn’t find an interpreter, it issues a warning. On macOS 12.2 and older, the extension also issues a warning if you’re using the OS-installed Python interpreter as it is known to have compatibility issues. In either case, you can disable these warnings by setting
python.disableInstallationCheck
to
true
in your user settings.
The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (
env
) that’s located within the workspace folder:
Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see “(cached)” before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.
If you have a folder or a workspace open in VS Code and you select an interpreter from the list, the Python extension will store that information internally. This ensures that the same interpreter will be used when you reopen the workspace.
The selected environment is used by the Python extension for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a
.py
file open in the editor, and opening a terminal with the Terminal: Create New Terminal command. In the latter case, VS Code automatically activates the selected environment.
Tip: To prevent automatic activation of a selected environment, add
"python.terminal.activateEnvironment": false
to your
settings.json
file (it can be placed anywhere as a sibling to the existing settings).
Tip: If the activate command generates the message “Activate.ps1 is not digitally signed. You cannot run this script on the current system.”, then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process
Note: By default, VS Code uses the interpreter selected for your workspace when debugging code. You can override this behavior by specifying a different path in the
python
property of a debug configuration. See Choose a debugging environment.
The selected interpreter version will show on the right side of the Status Bar.
The Status Bar also reflects when no interpreter is selected.
In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.
Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue so we can help determine the cause.
Manually specify an interpreter
If VS Code doesn’t automatically locate an interpreter you want to use, you can browse for the interpreter on your file system or provide the path to it manually.
You can do so by running the Python: Select Interpreter command and select the Enter interpreter path… option that shows on the top of the interpreters list:
You can then either enter the full path of the Python interpreter directly in the text box (for example, “.venv/Scripts/python.exe”), or you can select the Find… button and browse your file system to find the python executable you wish to select.
If you want to manually specify a default interpreter that will be used when you first open your workspace, you can create or modify an entry for the
python.defaultInterpreterPath
setting.
Note: Changes to the
python.defaultInterpreterPath
setting are not picked up after an interpreter has already been selected for a workspace; any changes to the setting will be ignored once an initial interpreter is selected for the workspace.
Additionally, if you’d like to set up a default interpreter to all of your Python applications, you can add an entry for
python.defaultInterpreterPath
manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set
python.defaultInterpreterPath
, which is in the Python extension section of User Settings, with the appropriate interpreter.
How the extension chooses an environment automatically
If an interpreter hasn’t been specified, then the Python extension automatically selects the interpreter with the highest version in the following priority order:
- Virtual environments located directly under the workspace folder.
- Virtual environments related to the workspace but stored globally. For example, Pipenv or Poetry environments that are located outside of the workspace folder.
-
Globally installed interpreters. For example, the ones found in
/usr/local/bin
,
C:\\python38
, etc.
Note: The interpreter selected may differ from what
python
refers to in your terminal.
If Visual Studio Code doesn’t locate your interpreter automatically, you can manually specify an interpreter.
Where the extension looks for environments
The extension automatically looks for interpreters in the following locations, in no particular order:
-
Standard install paths such as
/usr/local/bin
,
/usr/sbin
,
/sbin
,
c:\\python36
, etc. - Virtual environments located directly under the workspace (project) folder.
-
Virtual environments located in the folder identified by the
python.venvPath
setting (see General Python settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of
venvPath
. -
Virtual environments located in a
~/.virtualenvs
folder for virtualenvwrapper. - Interpreters created by pyenv, Pipenv, and Poetry.
-
Virtual environments located in the path identified by
WORKON_HOME
(as used by virtualenvwrapper). -
Conda environments found by
conda env list
. Conda environments which do not have an interpreter will have one installed for them upon selection. -
Interpreters installed in a
.direnv
folder for direnv under the workspace folder.
Environments and Terminal windows
After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Terminal command unless you change the
python.terminal.activateEnvironment
setting to
false
.
Please note that launching VS Code from a shell in which a specific Python environment is activated doesn’t automatically activate that environment in the default Integrated Terminal.
Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal – Terminal profiles for how to change the shell.
Changing interpreters with the Python: Select Interpreter command doesn’t affect terminal panels that are already open. Thus, you can activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.
Choose a debugging environment
By default, the debugger will use the Python interpreter chosen with the Python extension. However, if there is a
python
property specified in the debug configuration of
launch.json
, it takes precedence. If this property is not defined, it will fall back to using the Python interpreter path selected for the workspace.
For more details on debug configuration, see Debugging configurations.
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.
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.
Debugging and Testing in VSCode
Debugging
The Python extension comes with Debugging for all kinds of applications like multi-threaded, web, and remote applications. We can set breakpoints, inspect data, and run programs step by step.
Select a debug configuration
Launch the debug tab by clicking on the debug icon on the action bar or by using the keyboard shortcut Ctrl + Shift +D. To customize Debug options, click on create a launch.json file and select Python File.
Debug Panel
Run the Debug by clicking on the blue button Run and Debug, and it will run the Python file and show us the Variables, Watch, Call Stack, and breakpoints.
Quick debug
For quick debugging, you can always click on the down arrow beside the Run button and select Debug Python File.
Testing
The Python extension supports unittest and pytest testing frameworks. Instead of reading the test results in a terminal, you can review and resolve the issues within the Testing tab in an active bar.
Configure Python tests
After clicking on the Testing button, we will click on the Configure Python Tests button and select the testing framework. Usually, VSCode automatically detects the framework and displays all the tests in a tree view.
Learn about Python unit testing, implementing Python’s pytest testing framework by following our how to use pytest for unit testing tutorial.
Note: The testing example that we are using is from Visual Studio Code official documentation.
Run the unittest
We can run the Unit test by clicking on the Run Test button in the Testing tab and analyzing the results.
As we can observe, 1 of 2 tests have passed, and it has displayed the reasoning behind the failed result. VSCode testing is highly interactive and user-friendly.
Autocomplete and IntelliSense
The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion (in-context method and variable suggestions) across all your files and for built-in and third-party modules.
IntelliSense quickly shows methods, class members, and documentation as you type. You can also trigger completions at any time with ⌃Space (Windows, Linux Ctrl+Space). Hovering over identifiers will show more information about them.
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.
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
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.
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.
Git Integration
VSCode comes with built-in Git integration. No more writing Git commands on terminals. Git integration provides a user-friendly GUI and helpful functions for diff, views, staging, branching, committing, merge, and more.
Check out our Git Cheat Sheet to learn about the various Git commands and functionalities.
Note: To enable Git integration, you need to install Git from official site.
Initializing Git
We can access it through the action bar or by using the keyboard shortcut: Ctrl + Shift + G. Before we start committing, we need to initialize the repository.
Git Commit
After that, add and commit the changes with the message. It is that simple.
Create a GitHub repository and push the code
You can even create a GitHub repository and push your code to a remote server by logging into your GitHub account.
Private GitHub repository
We have created a GitHub private repository of Python files and folders.
You can now simply commit and push the changes to the remote server without leaving the VSCode.
Follow our Github and Git tutorial to learn everything about Git and GitHub.
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.
Creating environments
Using the Create Environment command
To create local environments in VS Code using virtual environments or Anaconda, you can follow these steps: open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), search for the Python: Create Environment command, and select it.
The command presents a list of environment types: Venv or Conda.
If you are creating an environment using Venv, the command presents a list of interpreters that can be used as a base for the new virtual environment.
If you are creating an environment using Conda, the command presents a list of Python versions that can be used for your project.
After selecting the desired interpreter or Python version, a notification will show the progress of the environment creation and the environment folder will appear in your workspace.
Note: The command will also install necessary packages outlined in a requirements/dependencies file, such as
requirements.txt
,
pyproject.toml
, or
environment.yml
, located in the project folder. It will also add a
.gitignore
file to the virtual environment to help prevent you from accidentally committing the virtual environment to source control.
Create a virtual environment in the terminal
If you choose to create a virtual environment manually, use the following command (where “.venv” is the name of the environment folder):
# macOS/Linux # You may need to run `sudo apt-get install python3-venv` first on Debian-based OSs python3 -m venv .venv # Windows # You can also use `py -3 -m venv .venv` python -m venv .venv
Note: To learn more about the
venv
module, read Creation of virtual environments on Python.org.
When you create a new virtual environment, a prompt will be displayed in VS Code to allow you to select it for the workspace.
Tip: Make sure to update your source control settings to prevent accidentally committing your virtual environment (in for example
.gitignore
). Since virtual environments are not portable, it typically does not make sense to commit them for others to use.
Create a conda environment in the terminal
The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment named
env-01
with a Python 3.9 interpreter and several libraries:
conda create -n env-01 python=3.9 scipy=0.15.0 numpy
Note: For more information on the conda command line, you can read Conda environments.
Additional notes:
-
If you create a new conda environment while VS Code is running, use the refresh icon on the top right of the Python: Select Interpreter window; otherwise you may not find the environment there.
-
To ensure the environment is properly set up from a shell perspective, use an Anaconda prompt and activate the desired environment. Then, you can launch VS Code by entering the
code .
command. Once VS Code is open, you can select the interpreter either by using the Command Palette or by clicking on the status bar. -
Although the Python extension for VS Code doesn’t currently have direct integration with conda
environment.yml
files, VS Code itself is a great YAML editor. -
Conda environments can’t be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal – Terminal profiles.
-
You can manually specify the path to the
conda
executable to use for activation (version 4.4+). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and run Preferences: Open User Settings. Then set
python.condaPath
, which is in the Python extension section of User Settings, with the appropriate path.
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
Visual Studio Code on macOS
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.
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
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.
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.
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.
Git Integration
VSCode comes with built-in Git integration. No more writing Git commands on terminals. Git integration provides a user-friendly GUI and helpful functions for diff, views, staging, branching, committing, merge, and more.
Check out our Git Cheat Sheet to learn about the various Git commands and functionalities.
Note: To enable Git integration, you need to install Git from official site.
Initializing Git
We can access it through the action bar or by using the keyboard shortcut: Ctrl + Shift + G. Before we start committing, we need to initialize the repository.
Git Commit
After that, add and commit the changes with the message. It is that simple.
Create a GitHub repository and push the code
You can even create a GitHub repository and push your code to a remote server by logging into your GitHub account.
Private GitHub repository
We have created a GitHub private repository of Python files and folders.
You can now simply commit and push the changes to the remote server without leaving the VSCode.
Follow our Github and Git tutorial to learn everything about Git and GitHub.
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
Conclusion
VSCode is not just a code editor. It is a complete ecosystem for efficient Python development. It provides us with shortcuts, Commands Palette, IntelliSense, linting, formatting, debugging, formatting, Git integrations, Jupyter notebook, third-party extensions, and a fully customizable development experience.
VSCode is highly recommended to beginners who are learning the basics of Python and data science. Complete Data Scientist with a Python career track to become a master in Python and data science. The career track consists of 25 courses and six projects to prepare you to become a professional data scientist.
A Deep Dive into the Phi-2 Model
Python List Size: 8 Different Methods for Finding the Length of a List in Python
An End-to-End ML Model Monitoring Workflow with NannyML in Python
Bex Tuychiev
15 min
Visual Studio Code on macOS
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.
Next steps
- Editing code – Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
- 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.
Tips and Tricks for Efficient Python Development in VSCode
VSCode comes with awesome Python development features and extensions. We can customize them to our needs and improve the productivity. In this section, we will learn about tips and tricks for efficient Python development.
- Getting started: Help > Get Started. Learn about VSCode’s customization and features by following guided tutorials.
- Command Palette: access entire all available commands by using the Keyboard shortcut: Ctrl+Shift+P. By writing keywords, we can access specific commands.
- Keyboard shortcuts: better than command palettes. We can modify keyboard shortcuts or memorize them by using keyboard reference sheets. It will help us access the commands directly, instead of searching with the keyword.
- Command line: launch the VSCode editor through the command line interface by typing `code .`. We can also customize how the editor is launched by adding additional arguments.
- Errors and warnings: quick jump to errors and warnings in a project by using the keyboard shortcut: Ctrl+Shift+M. We can also cycle through the error with F8 or Shift+F8.
- Customization: VSCode allows us to customize themes, keyboard shortcuts, JSON validation, debugging settings, fonts, and many more. It is a fully customizable IDE.
- Extensions: other Python extensions improve our development experience. Look for popular extensions on the Visual Studio Marketplace.
- Multi cursor selection: is a lifesaver. Add cursors at arbitrary positions by using Alt+Click. It will allow us to modify multiple lines of code at once. We can also use Ctrl+Shift+L to modify all occurrences of the current selection.
- Search and modify: this is the best tool for searching and modifying multiple expressions at once. We can also rename the symbol by selecting the symbol and typing F2.
- Git integration: allows us to perform all Git-related tasks within IDE. It provides an easy-to-use GUI for diff, views, staging, branching, committing, merging, and more.
- Code Snippets: is our best friend. Just like Autohotkey, we are creating templates for repeating code patterns. To create a custom code snippet, select File > Preferences > Configure User Snippets and then select the language.
- GitHub Copilot: is a winner extension for all kinds of development. It enhances the coding experience with artificial intelligence (AI) by suggesting lines of code or entire functions.
Bonus: sync your settings by logging into your GitHub account. It will sync your settings across all of the machines.
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.
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.
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
Configuring Linting and Formatting in VSCode
Linting
Linting highlights the problems in the Python source code and provides us with suggestions. It generally highlights syntactical and stylistic issues. Linting helps you identify and correct coding issues that can lead to errors.
You can select the linting method by selecting Python: Select Linter command in the command palette (Ctrl+Shift+P). You can also manually enable the linting method in settings.
Select linting method
In our case, we have selected the flake8 method. You can also review the list of available linting methods.
- Enable/ Disable Linting: select Python: Enable/Disable Linting in command palette.
- Run Linting: command palette (Ctrl+Shift+P) > Python: Run Linting.
Fixing the error
After running the Python linter, you will see the issues with the suggestions.
Note: Enabling a different linter will prompt you to install the required Python package.
Formatting
Formatting makes code readable. It follows specific rules for line spacing, indents, spacing around operators, and closing brackets. The Python extension supports three Python formatting methods: autopep8, black, or yapf.
By reading about PEP-8: Python Naming Conventions & Code Standards, you can learn Python’s style guide and formatting rules.
Select the Python formatter
To access the formatting option, we have to open the settings panel by going to Preferences -> Settings or using the keyboard shortcut: Ctrl +,. After that, type “python formatting provider” in the search bar and select “black” from the dropdown menu.
Configure Python formatter
For formatting the Python file on save, we have to search for format on save in the Settings and enable the Editor: Format on Save option.
Configuring Linting and Formatting in VSCode
Linting
Linting highlights the problems in the Python source code and provides us with suggestions. It generally highlights syntactical and stylistic issues. Linting helps you identify and correct coding issues that can lead to errors.
You can select the linting method by selecting Python: Select Linter command in the command palette (Ctrl+Shift+P). You can also manually enable the linting method in settings.
Select linting method
In our case, we have selected the flake8 method. You can also review the list of available linting methods.
- Enable/ Disable Linting: select Python: Enable/Disable Linting in command palette.
- Run Linting: command palette (Ctrl+Shift+P) > Python: Run Linting.
Fixing the error
After running the Python linter, you will see the issues with the suggestions.
Note: Enabling a different linter will prompt you to install the required Python package.
Formatting
Formatting makes code readable. It follows specific rules for line spacing, indents, spacing around operators, and closing brackets. The Python extension supports three Python formatting methods: autopep8, black, or yapf.
By reading about PEP-8: Python Naming Conventions & Code Standards, you can learn Python’s style guide and formatting rules.
Select the Python formatter
To access the formatting option, we have to open the settings panel by going to Preferences -> Settings or using the keyboard shortcut: Ctrl +,. After that, type “python formatting provider” in the search bar and select “black” from the dropdown menu.
Configure Python formatter
For formatting the Python file on save, we have to search for format on save in the Settings and enable the Editor: Format on Save option.
Tips and Tricks for Efficient Python Development in VSCode
VSCode comes with awesome Python development features and extensions. We can customize them to our needs and improve the productivity. In this section, we will learn about tips and tricks for efficient Python development.
- Getting started: Help > Get Started. Learn about VSCode’s customization and features by following guided tutorials.
- Command Palette: access entire all available commands by using the Keyboard shortcut: Ctrl+Shift+P. By writing keywords, we can access specific commands.
- Keyboard shortcuts: better than command palettes. We can modify keyboard shortcuts or memorize them by using keyboard reference sheets. It will help us access the commands directly, instead of searching with the keyword.
- Command line: launch the VSCode editor through the command line interface by typing `code .`. We can also customize how the editor is launched by adding additional arguments.
- Errors and warnings: quick jump to errors and warnings in a project by using the keyboard shortcut: Ctrl+Shift+M. We can also cycle through the error with F8 or Shift+F8.
- Customization: VSCode allows us to customize themes, keyboard shortcuts, JSON validation, debugging settings, fonts, and many more. It is a fully customizable IDE.
- Extensions: other Python extensions improve our development experience. Look for popular extensions on the Visual Studio Marketplace.
- Multi cursor selection: is a lifesaver. Add cursors at arbitrary positions by using Alt+Click. It will allow us to modify multiple lines of code at once. We can also use Ctrl+Shift+L to modify all occurrences of the current selection.
- Search and modify: this is the best tool for searching and modifying multiple expressions at once. We can also rename the symbol by selecting the symbol and typing F2.
- Git integration: allows us to perform all Git-related tasks within IDE. It provides an easy-to-use GUI for diff, views, staging, branching, committing, merging, and more.
- Code Snippets: is our best friend. Just like Autohotkey, we are creating templates for repeating code patterns. To create a custom code snippet, select File > Preferences > Configure User Snippets and then select the language.
- GitHub Copilot: is a winner extension for all kinds of development. It enhances the coding experience with artificial intelligence (AI) by suggesting lines of code or entire functions.
Bonus: sync your settings by logging into your GitHub account. It will sync your settings across all of the machines.
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.
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