Visual Studio Python Mac | Mojave Privacy Protections

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.

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.

System setup#

If you intend to follow along with the code presented in this book, we recommend you follow these setup instructions so that you will run into fewer technical issues.

Setup Python Using Visual Studio Code On Mac | VSCode Python | Python Mac | Python 3

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.

How to Set Up Python in Visual Studio Code on Mac | VSCode Python Development Basics On MacOS (2024)

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.

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.

How to setup Python for VSCode in 2023 in 3mins! | Install Python and Setup VSCode for MAC 10

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.

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

Visual Studio Code on macOS

🐍 How To Run Python in VSCode on a Mac M1 / Visual Studio Code / VS Code / Macbook Pro Air

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 (*).

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

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.

How to Run Python in Visual Studio Code on Mac OS Big Sur Apple Macbook M1

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.

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 (Mac) - Setting up a Python Development Environment and Complete Overview — Visual Studio Code (Mac) – Setting up a Python Development Environment and Complete Overview

Modern development with .NET 7

Visual Studio 2022 for Mac includes nearly everything you’ll need for .NET 7 development, from responsive C# web UIs in Blazor to event-driven solutions using Azure Functions.

Advanced IntelliSense

With the power of Roslyn, Visual Studio for Mac brings IntelliSense to your fingertips. IntelliSense describes APIs as you type and uses auto-completion to increase the speed and accuracy of how you write code.

Quick Info tool tips let you inspect API definitions. Squiggly lines in the editor highlight issues in real time as you type.

Intelligent Refactoring

As your project grows, chances are, you’ll find yourself restructuring and refactoring code that you or someone else wrote earlier. That’s a whole lot easier when Visual Studio for Mac takes care of the heavy lifting for you.

Learn more about Xamarin.Essentials

Integrated Version Control

Visual Studio 2022 has built-in support for Git version control to clone, create, and open your own repositories. The Git tool window has everything you need for committing and pushing changes to code.

Powerful Debugging

Integrated debugging is a core part of every Visual Studio product. You can step through your code and look at the values stored in variables, set watches on variables to see when values change, examine the execution path of your code, and just about anything else you need to check out under the hood.

.NET

Build modern solutions for the web and cloud with ASP.NET Core.

Xamarin

Build apps for iOS, Android, macOS, and more with C# and .NET

Unity

Build your next game or real-time 3D Unity application with best-in-class debugging.

Visual Studio 2022 for Mac	Visual Studio 2022
Download
Web and Cloud development using C#
Desktop development
Mobile and gaming
Other workloads and tools

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.

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.

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).

How To Install Python Libraries In Visual Studio Code (Mac)

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.

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.

How to Install Python on Mac OS and How to Run Python code

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.

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.

Python Full Course for Beginners | Complete All-in-One Tutorial | 9 Hours

Debugging

No more

For more specific information on debugging in Python, such as configuring your
launch.json
settings and implementing remote debugging, see Debugging. General VS Code debugging information is found in the debugging document.

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

Create a 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

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.

How to Install PyCharm on Mac | Install PyCharm IDE on macOS (2024)

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

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.

How To Install OpenCV Python in Visual Studio Code on Mac | Install OpenCV in VSCode on MacOS (2024)

2.Developing with Docker#

If you have issues installing or using any of the software in this book on your specific operating system, or would prefer to use Docker to help develop your Python packages, we have provided an alternative software setup with Docker that has everything you need already installed to get started. Docker is a platform that allows you to run and develop software in an isolated environment called a container. Images contain the instructions required to create a container.

We have developed Docker images to support Python package development in Visual Studio Code or JupyterLab, and we describe minimal workflows for using these images to follow along with this book in the sections below. Feel free to customize these images and/or workflows to suit your specific use cases. We will continue to maintain the Docker images via their GitHub repositories (py-pkgs/docker-vscode and py-pkgs/docker-jupyter) to support readers of this book into the future.

2.6.Docker with Visual Studio Code#

To develop with Docker inside Visual Studio Code, you can consult the Visual Studio Code official container tutorial, or try following the steps below:

Install Visual Studio Code from the official website.
Install and configure Docker Desktop for your operating system following the instructions on the official website.
Once docker is installed, open a command-line interface and pull the

pypkgs/vscode
docker image by running the following command:

$ docker pull pypkgs/vscode
From Visual Studio Code, open/create the working directory you want to develop in (this can be called anything and located wherever you like on your file system).
In Visual Studio Code, open the Extensions tab on the Visual Studio Code activity bar and search for the “Dev Containers” extension in the search bar. Install this extension if it is not already installed.
Create a file called

.devcontainer.json
in your current working directory (be sure to include the period at the beginning of the file name). This file will tell Visual Studio Code how to run in a Docker container. You can read more about this configuration in the official documentation, but for now, a minimal set up requires adding the following content to that file:

{ “name”: “poetry”, “image”: “pypkgs/vscode”, “extensions”: [“ms-python.python”], }
Now, open the Visual Studio Code Command Palette and search for and select the command “Dev Containers: Reopen in Container”. This command will open Visual Studio Code inside a container made using the

pypkgs/vscode
Docker image. After Visual Studio Code finishes opening in the container, test that you have access to the three pre-installed pieces of packaging software we need by opening the integrated terminal and trying the following commands:

$ poetry –version $ conda –version $ cookiecutter –version
Your development environment is now set up, and you can work with Visual Studio Code as if everything were running locally on your machine (except now your development environment exists inside a container). If you exit Visual Studio Code, your container will stop but will persist on your machine. It can be re-opened at a later time using the “Dev Containers: Reopen in Container” command we used in step 7.
If you want to completely remove your development container to free up memory on your machine, first find the container’s ID:

$ docker ps -a

CONTAINER ID IMAGE 762bca6eb51e pypkgs/vscode
Then use the

docker rm
command combined with the container’s ID. This will remove the container, including any packages or virtual environments installed in it. However, any files and directories you created will persist on your machine.

$ docker rm 762bca6eb51e

2.6.Docker with JupyterLab#

To develop with Docker in JupyterLab follow the instructions below. Helpful information and tutorials can also be found in the Jupyter Docker Stacks documentation.

Install and configure Docker Desktop for your operating system following the instructions on the official website.
Once Docker has been installed, open a command-line interface and pull the

pypkgs/jupyter
Docker image by running the
docker pull
command as follows:

$ docker pull pypkgs/jupyter
From the command line, navigate to the directory you want to develop in (this can be called anything and located wherever you like on your file system).
Start a new container from that directory by running the following command from the command line:

$ docker run -p 8888:8888 \ -v “${PWD}”:/home/jovyan/work \ pypkgs/jupyter

Tip

In the command above,

-p
binds port 8888 in the container to port 8888 on the host machine and
-v
mounts the current directory into the container at the location
/home/jovyan/work
. Windows users that run into issues with the command above may need to try double-slashes in the volume mount path, for example:
-v /$(pwd)://home//jovyan//work
. You can read more about the
docker run
command and its arguments in the Docker command-line interface documentation.
Copy the unique URL printed to screen (that looks something like this:

http://127.0.0.1:8888/lab?token=45d53a348580b3acfafa
) to your browser. This will open an instance of JupyterLab running inside a Docker container.
Navigate to the

work
directory in JupyterLab. This is where you can develop and create new files and directories that will persist in the directory from where you launched your container.
Test that you have access to the three pre-installed pieces of packaging software we need by opening a terminal in JupyterLab and trying the following commands:

$ poetry –version $ conda –version $ cookiecutter –version
When you’ve finished a working session, you can exit JupyterLab, and kill your terminal, and your container will persist. You can restart the container and launch JupyterLab again by first finding its ID:

$ docker ps -a

CONTAINER ID IMAGE 653daa2cd48e pypkgs/jupyter
Then, to restart the container and launch JupyterLab, use the

docker start -a
command combined with the container’s ID:

$ docker start -a 653daa2cd48e
If you want to completely remove the container you can use the

docker rm
command. This will remove the container, including any packages or virtual environments installed in it. However, all files and directories added to the
work
directory will persist on your machine.

$ docker rm 653daa2cd48e

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!

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

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.

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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.

Feedback

Submit and view feedback for

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.

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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.

Conclusion

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

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

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15 min

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.

Configure and run the debugger

Next, set a breakpoint on line 2 of
hello.py
by placing the cursor on the

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.

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.

msg msg.capitalize() msg.split()

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

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

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

Python in Visual Studio Code

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.

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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!

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.

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Install and use packages

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

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.

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!

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.

371: Python in a Crate - Python Bytes — 371: Python in a Crate – Python Bytes

2.Set up Git and GitHub#

If you’re not using a version control system, we highly recommend you get into the habit! A version control system tracks changes to the file(s) of your project in a clear and organized way (no more “document_1.doc”, “document_1_new.doc”, “document_final.doc”, etc.). As a result, a version control system contains a full history of all the revisions made to your project, which you can view and retrieve at any time. You don’t need to use or be familiar with version control to read this book, but if you’re serious about creating Python packages, version control will become an invaluable part of your workflow, so now is a good time to learn!

There are many version control systems available, but the most common is Git and we’ll be using it throughout this book. You can download Git by following the instructions in the Git documentation. Git helps track changes to a project on a local computer, but what if we want to collaborate with others? Or, what happens if your computer crashes and you lose all your work? That’s where GitHub comes in. GitHub is one of many online services for hosting Git-managed projects. GitHub helps you create an online copy of your local Git repository, which acts as a backup of your local work and allows others to easily and transparently collaborate on your project. You can sign up for a free GitHub account on the GitHub website. Once signed up, you should also set up SSH authentication to help push and pull files from GitHub by following the steps in the official GitHub documentation.

We assume that those who choose to follow the optional version control sections of this book have basic familiarity with Git and GitHub (or equivalent). Two excellent learning resources are Happy Git and GitHub for the useR2 and Research Software Engineering with Python3.

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.

Python #106: Install python and VScode on macOS | Programming python on macOS

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.

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

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 (*).

Setup Python For Visual Studio Code - macOS (2020) — Setup Python For Visual Studio Code – macOS (2020)

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.

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.

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Create a virtual 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.

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.

How To Setup A Virtual Environment For Python In Visual Studio Code In 2023

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.

2.Register for a PyPI account and get an authentication token#

The Python Package Index (PyPI) is the official online software repository for Python. A software repository is a storage location for downloadable software, like Python packages. In this book we’ll be publishing a package to PyPI. Before publishing packages to PyPI, it is typical to “test drive” their publication on TestPyPI, which is a test version of PyPI. To follow along with this book, you should register for a TestPyPI account on the TestPyPI website and a PyPI account on the PyPI website.

Both TestPyPI and PyPI will require an authentication token for publishing your Python packages using Poetry. You should obtain one from each of these repositories and store them in a safe place, such as some password management software (e.g., LastPass or 1Password). You can find the location of where to generate an authentication token by logging into the repository, and choosing “Account Settings” from the drop down menu under your username. Then scroll down to “API tokens” and click “Add API token”. You will then be prompted to give the token a name and to set its scope. For scope choose “Entire account (all projects)”.

2.Python integrated development environments#

A Python integrated development environment (IDE) will make the process of creating Python packages significantly easier. An IDE is a piece of software that provides advanced functionality for code development, such as directory and file creation and navigation, autocomplete, debugging, and syntax highlighting, to name a few. An IDE will save you time and help you write better code. Commonly used free Python IDEs include Visual Studio Code, Atom, Sublime Text, Spyder, and PyCharm Community Edition. For those more familiar with the Jupyter ecosystem, JupyterLab is a suitable browser-based IDE. Finally, for the R community, the RStudio IDE also supports Python.

You’ll be able to follow along with the examples presented in this book regardless of what IDE you choose to develop your Python code in. If you don’t know which IDE to use, we recommend starting with Visual Studio Code. Below we briefly describe how to set up Visual Studio Code, JupyterLab, and RStudio as Python IDEs (these are the IDEs we personally use in our day-to-day work). If you’d like to use Docker to help develop Python packages and follow along with this book, we’ll describe how to do so with Visual Studio Code or JupyterLab in Section 2.6.

2.5.Visual Studio Code#

You can download Visual Studio Code (VS Code) from the Visual Studio Code website. Once you’ve installed VS Code, you should install the “Python” extension from the VS Code Marketplace. To do this, follow the steps listed below and illustrated in Fig. 2.1:

Open the Marketplace by clicking the Extensions tab on the VS Code activity bar.
Search for “Python” in the search bar.
Select the extension named “Python” and then click Install.

Fig. 2.1 Installing the Python extension in Visual Studio Code.#

Once this is done, you have everything you need to start creating packages! For example, you can create files and directories from the File Explorer tab on the VS Code activity bar, and you can open up an integrated CLI by selecting Terminal from the View menu. Fig. 2.2 shows an example of executing a Python .py file from the command line in VS Code.

Fig. 2.2 Executing a simple Python file called hello-world.py from the integrated terminal in Visual Studio Code.#

We recommend you take a look at the VS Code Getting Started Guide to learn more about using VS Code. While you don’t need to install any additional extensions to start creating packages in VS Code, there are many extensions available that can support and streamline your programming workflows in VS Code. Below are a few we recommend installing to support the workflows we use in this book (you can search for and install these from the “Marketplace” as we did earlier):

Python Docstring Generator: an extension to quickly generate documentation strings (docstrings) for Python functions.
Markdown All in One: an extension that provides keyboard shortcuts, automatic table of contents, and preview functionality for Markdown files. Markdown is a plain-text markup language that we’ll use and learn about in this book.

2.5.JupyterLab#

For those comfortable in the Jupyter ecosystem feel free to stay there to create your Python packages! JupyterLab is a browser-based IDE that supports all of the core functionality we need to create packages. As per the JupyterLab installation instructions, you can install JupyterLab with:

$ conda install -c conda-forge jupyterlab

Once installed, you can launch JupyterLab from your current directory by typing the following command in your terminal:

$ jupyter lab

In JupyterLab, you can create files and directories from the File Browser and can open up an integrated terminal from the File menu. Fig. 2.3 shows an example of executing a Python .py file from the command line in JupyterLab.

Fig. 2.3 Executing a simple Python file called hello-world.py from a terminal in JupyterLab.#

We recommend you take a look at the JupyterLab documentation to learn more about how to use Jupyterlab. In particular, we’ll note that, like VS Code, JupyterLab supports an ecosystem of extensions that can add additional functionality to the IDE. We won’t install any here, but you can browse them in the JupyterLab Extension Manager if you’re interested.

2.5.RStudio#

Users with an R background may prefer to stay in the RStudio IDE. We recommend installing the most recent version of the IDE from the RStudio website (we recommend installing at least version ^1.4) and then installing the most recent version of R from CRAN. To use Python in RStudio, you will need to install the reticulate R package by typing the following in the R console inside RStudio:

install.packages(“reticulate”)

When installing reticulate, you may be prompted to install the Anaconda distribution. We already installed the Miniconda distribution of Python in Section 2.2.1, so answer “no” to this prompt. Before being able to use Python in RStudio, you will need to configure
reticulate
. We will briefly describe how to do this for different operating systems below, but we encourage you to look at the
reticulate
documentation for more help.

Mac and Linux

Find the path to the Python interpreter installed with Miniconda by typing

which python
at the command line.
Open (or create) an

.Rprofile
file in your HOME directory and add the line
Sys.setenv(RETICULATE_PYTHON = "path_to_python")
, where
"path_to_python"
is the path identified in step 1.
Open (or create) a

.bash_profile
file in your HOME directory and add the line
export PATH="/opt/miniconda3/bin:$PATH"
, replacing
/opt/miniconda3/bin
with the path you identified in step 1 but without the
python
at the end.
Restart R.
Try using Python in RStudio by running the following in the R console:

library(reticulate) repl_python()

Windows

Find the path to the Python interpreter installed with Miniconda by opening an Anaconda Prompt from the Start Menu and typing

where python
in a terminal.
Open (or create) an

.Rprofile
file in your HOME directory and add the line
Sys.setenv(RETICULATE_PYTHON = "path_to_python")
, where
"path_to_python"
is the path identified in step 1. Note that in Windows, you need
\\
instead ofto separate the directories; for example your path might look like:
C:\\Users\\miniconda3\\python.exe
.
Open (or create) a

.bash_profile
file in your HOME directory and add the line
export PATH="/opt/miniconda3/bin:$PATH"
, replacing
/opt/miniconda3/bin
with the path you identified in step 1 but without the
python
at the end.
Restart R.
Try using Python in RStudio by running the following in the R console:

library(reticulate) repl_python()

Fig. 2.4 shows an example of executing Python code interactively within the RStudio console.

Fig. 2.4 Executing Python code in RStudio.#

Getting Started with Python in VS Code

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.

Learn How to Run Python Code using Visual Studio Code - for Mac OS — Learn How to Run Python Code using Visual Studio Code – for Mac OS

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

2.Installing software#

Section 2.2.1 and Section 2.2.2 describe how to install the software you’ll need to develop a Python package and follow along with the text and examples in this book. However, we also support an alternative setup with Docker that has everything you need already installed to get started. The Docker approach is recommended for anyone that runs into issues installing or using any of the software below on their specific operating system, or anyone who would simply prefer to use Docker — if that’s you, skip to Section 2.3 for now, and we’ll describe the Docker setup later in Section 2.6.

2.2.Installing Python#

We recommend installing the latest version of Python via the Miniconda distribution by following the instructions in the Miniconda documentation. Miniconda is a lightweight version of the popular Anaconda distribution. If you have previously installed the Anaconda or Miniconda distribution feel free to skip to Section 2.2.2.

If you are unfamiliar with Miniconda and Anaconda, they are distributions of Python that also include the
conda
package and environment manager, and a number of other useful packages. The difference between Anaconda and Miniconda is that Anaconda installs over 250 additional packages (many of which you might never use), while Miniconda is a much smaller distribution that comes bundled with just a few key packages; you can then install additional packages as you need them using the command
conda install
.

conda
is a piece of software that supports the process of installing and updating software (like Python packages). It is also an environment manager, which is the key function we’ll be using it for in this book. An environment manager helps you create “virtual environments” on your machine, where you can safely install different packages and their dependencies in an isolated location. Installing all the packages you need in the same place (i.e., the system default location) can be problematic because different packages often depend on different versions of the same dependencies; as you install more packages, you’ll inevitably get conflicts between dependencies, and your code will start to break. Virtual environments help you compartmentalize and isolate the packages you are using for different projects to avoid this issue. You can read more about virtual environments in the
conda
documentation. While alternative package and environment managers exist, we choose to use
conda
in this book because of its popularity, ease-of-use, and ability to handle any software stack (not just Python).

2.2.Install packaging software#

Once you’ve installed the Miniconda distribution, ensure that Python and
conda
are up to date by running the following command at the command line:

$ conda update –all

Now we’ll install the two main pieces of software we’ll be using to help us create Python packages in this book:

poetry
: software that will help us build our own Python packages.
poetry
is under active development, thus we recommend installing Poetry using their official installer while referring to their official
poetry
documentation for detailed installation instructions and support.
cookiecutter
: software that will help us create packages from pre-made templates. It can be installed with
conda
as follows:

$ conda install -c conda-forge cookiecutter

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