How to Configure Python with Visual Studio Code (VSCode)

10 min readOct 20, 2022

In this article, you will learn how to configure and use Python 3 and how to code the simplest Python “Hello World” in Visual Studio Code. By using the Python extension, you make Visual Studio Code into a great lightweight Python IDE with faster computation.

You will learn how to edit, run, and debug code through the following tasks:

Write, run, and debug a Python “Hello World” Application
Learn how to install packages by creating Python virtual environments
Write a simple Python script to plot figures within VS Code

Requirements

To successfully complete this task, you will need to first download Python 3 from Here and then install it.

Now Install Visual Studio Code and the Python Extension

If you have not already done so, install VS Code.
Next, install the Python extension for VS Code from the Visual Studio Marketplace or from the Extension which is in provided inside Visual Studio. For additional details on installing extensions, see Extension Marketplace.

Now Install a Python interpreter

You need to install a Python interpreter, so follow this guide which is provided below, if you have already installed then you don’t need to install it again.

Windows Installation

Install Interpreter Python from the official website python.org. You can typically 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 Python 3.7, Python 3.8, Python 3.9, and Python 3.10.

macOS Installation

However, Python on macOS is not supported, so Instead of python, you will need 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 Installation

In Linux Python 3 is already built-in so you don’t need to install it, but to install other Python packages you must install pip with get-pip.py

Verify the Python installation

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

Windows/Linux/macOS: open a Terminal or Command Prompt and type the following command:
python3 --version or
python --version or
py -3 --version

If the installation was successful, the output window should show the version of Python that you installed in your system.

Note 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 (*).

Start Visual Studio Code in a project (workspace) folder by clicking right click from the mouse and choosing the folder

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 using Terminal or Command Prompt:

mkdir hello
cd hellocode .

Select a Python interpreter

Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.

From within VS Code, select a Python 3 interpreter by opening the Command Palette (Ctrl+Shift+P), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):

Visual Studio Down Status Bar

The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don’t see the desired interpreter, see Configuring Python environments.

Create a Python Hello World source code file#

From the File Explorer toolbar, select the New File button on the hello folder:

Name the file hello.py, and it automatically opens 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 = "Hello World"
print(msg)

When you start typing print, notice how IntelliSense presents auto-completion options.

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.:

Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the msg variable and the print call, and save the file (Ctrl+S).

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

Run Hello World

It’s simple to run hello.py with Python. Just 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 (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.

Configure and run the debugger

Let’s now try debugging our simple Hello World program.

First, set a breakpoint on line 2 of hello.py by placing the cursor on the print call and pressing F9. Alternately, just click in the editor's left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.

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.

These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

You can also start the debugger by clicking on the down-arrow next to the run button on the editor, and selecting Debug Python File in Terminal.

The debugger will 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 will see now defined 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 (Shift+F11), restart (Ctrl+Shift+F5), and stop (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 Hello world 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. “Hello World” 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 (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.

Install and use packages

Let’s now run an example that’s a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the matplotlib and numpy packages to create a graphical plot as is commonly done with data science. (Note that matplotlib cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)

Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called standardplot.py, and paste in the following source code:

import matplotlib.pyplot as plt
import numpy as npx = np.linspace(0, 20, 100)  # Create a list of evenly-spaced numbers over the range
plt.plot(x, np.sin(x))       # Plot the sine of each x point
plt.show()                   # Display the plot

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

Unless you’re using an Anaconda distribution or have previously installed the matplotlib package, you should see the message, "ModuleNotFoundError: No module named 'matplotlib'". Such a message indicates that the required package isn't available in your system.

To install the matplotlib package (which also installs numpy as a dependency), stop the debugger and use the Command Palette to run Terminal: Create New Terminal (Ctrl+Shift+`). This command opens a command prompt for your selected interpreter.

A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific virtual environment that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:

Note: For additional information about virtual environments, see Environments.

Create and activate the virtual environment
Virtual environment creation for Windows

py -3 -m venv .venv .venv\scripts\activate

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

Virtual environment creation for macOS/Linux

python3 -m venv .venv source .venv/bin/activate
Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.