Getting Started with Python in VS Code

In this tutorial, you use Python 3 to create the simplest Python "Hello World" application in Visual Studio Code. By using the Python extension, you make VS Code into a great lightweight Python IDE (which you may find a productive alternative to PyCharm).

This tutorial is intended to introduce you to VS Code as a Python environment, primarily how to edit, run, and debug code. This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on within the context of VS Code for an introduction to the language.

If you have any problems, feel free to file an issue for this tutorial in the VS Code documentation repository.

Note: You can use VS Code with Python 2 with this tutorial, but you need to make appropriate changes to the code, which are not covered here.


To successfully complete this tutorial, complete the following requirements:

  1. Install the Python extension for VS Code. For details on installing extensions, see Extension Marketplace. The Python extension is named Python and published by Microsoft.

  2. Install a version of Python 3 (for which this tutorial is written). Options include:

    • (All operating systems) A download from; you can typically use the Download Python 3.7.4 button that appears first on the page (or whatever is the latest version).
    • (Linux) The built-in Python 3 installation works well, but to install other Python packages you must install pip with
    • (macOS) An installation through Homebrew on macOS using brew install python3 (the system install of Python on macOS is not supported).
    • (Windows) Install Python from the Windows Store.
    • (All operating systems) A download from Anaconda (for data science purposes).

    Note You can use the py -0 command in the integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

  3. On MacOS, make sure the location of your VS Code installation is included in your PATH environment variable. See the setup instructions for more information.

Verify the Python installation

VS Code and the Python extension don't require that the location of a Python interpreter be included in your PATH environment variable. However, some tools and libraries that VS Code might invoke won't work in the VS Code terminal unless Python is on the PATH. For best results, we recommend that you make sure you have Python on your PATH. (Exception: if you're using an Anaconda distro, make sure you start VS Code from an Anaconda command prompt when following the instructions in the next section.)

  • Linux/macOS: Python is typically added to the PATH when you install it. To verify, run the following command in a terminal:

    python3 --version
  • Windows: Python is included on the system PATH if you install it from the Windows Store or if you checked "Add Python 3.7 to PATH" during installation. Otherwise, you must add the install location to the PATH manually. To verify, open a command prompt and run the following command:

    py -3 --version

    If your Python installation is not found, open Windows Settings, search for "environment", select Edit environment variables for your account, then edit the Path variable to include the appropriate folder.

Start VS Code in a project (workspace) folder

At 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 .

When using an Anaconda distribution, be sure to use an Anaconda command prompt.

By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in .vscode/settings.json, which are separate from user settings that are stored globally.

Alternately, you can run VS Code through the operating system UI, then use File > Open Folder to open the project folder.

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 (⇧⌘P (Windows, Linux 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):

No interpreter selected

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.

Note: When using an Anaconda distribution, the correct interpreter should have the suffix ('base':conda), for example Python 3.7.3 64-bit ('base':conda).

Selecting an interpreter sets the python.pythonPath value in your workspace settings to the path of the interpreter. To see the setting, select File > Preferences > Settings (Code > Preferences > Settings on macOS), then select the Workspace Settings tab.

Note: If you select an interpreter without a workspace folder open, VS Code sets python.pythonPath in your user settings instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.

Create a Python Hello World source code file

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

File Explorer New File

Name the file, and it automatically opens in the editor:

File Explorer

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.

Next, start entering the following source code if using Python 3:

msg = "Hello World"

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

IntelliSense appearing for Python code

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.:

IntelliSense appearing for a variable whose type provides methods

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 (⌘S (Windows, Linux 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 with Python. Right-click anywhere in the editor window and select Run Python File in Terminal (which saves the file automatically):

Run Python File in Terminal command in the Python editor

The command opens a terminal panel in which your Python interpreter is automatically activated, then runs python3 (macOS/Linux) or python (Windows):

Program output in a Python terminal

There are two other ways you can run Python within VS Code:

  • Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.
  • From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Configure and run the debugger

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

First, set a breakpoint on line 2 of 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.

Setting a breakpoint in

Next, select the Debug View in the sidebar:

Debug icon

Then select the settings icon on the debug toolbar (or use the Debug > Open configurations menu command):

Debug toolbar settings command

Selecting the settings icon automatically opens a configuration menu from the Command Palette, allowing you to select the type of debug configuration you would like for the opened file.

Debug configurations after launch.json is created

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. Once selected the Python extension creates and opens a launch.json file that contains a pre-defined configuration based on your selection.

To automatically stop the debugger on the first line when the program starts, add a "stopOnEntry": true setting to the "Python: Current File" configuration in the generated launch.json file, so that the whole configuration appears as follows:

    "name": "Python: Current File",
    "type": "python",
    "request": "launch",
    "program": "${file}",
    "console": "integratedTerminal",
    "stopOnEntry": true

Save launch.json after making changes.

Tip: If you need to specify the exact folder containing the interpreter to use for debugging, include an entry for pythonPath in the configuration, such as "pythonPath": "${workspaceFolder}" or "pythonPath": "${workspaceFolder}/.venv".

Tip: To specify command-line arguments for the Python program, add a line "args": [] to the configuration, and place each argument as elements inside the [] list. For examples, see Debugging - args.

Switch to in the editor, then run the debugger by selecting the arrow in the Debug toolbar or pressing F5. The debugger stops at the first line of the file breakpoint (or the first line if stopOnEntry is set to true). The current line is indicated with a yellow arrow in the left margin. If you're stopped on the first line and examine the Local variables window at this point, you see that only automatic dunder variables are defined:

Debugging step 1 - stop on entry

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)).

Debugging toolbar

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 next breakpoint. The now-defined msg variable appears in the Local pane.

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.

Debugging step 2 - variable defined

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:


Debugging step 3 - using the debug console

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 (or the first line is stopOnEntry is set to true, in which case the debugger stops before any code is run.)

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 Debug > 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 print statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.


If for some reason VS Code doesn't generate launch.json for you, create the .vscode/launch.json file within the project folder (creating the .vscode folder if you need to), then paste the following contents into launch.json:

    "version": "0.2.0",
    "configurations": [
            "name": "Python: Current File",
            "type": "python",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal"

If you see "SyntaxError: invalid syntax" as shown below, you may have attempted to start debugging when launch.json was showing in the editor. The error occurs because launch.json is not Python code like

    // Use IntelliSense to learn about possible attributes.
SyntaxError: invalid syntax

Select and try again. Alternately, create a debug configuration specifically for the file by adding the following lines in launch.json within the configuration array. Then select this configuration in the debugger drop-down and start the debugger again.

            "name": "Python:",
            "type": "python",
            "request": "launch",
            "program": "${workspaceFolder}/",
            "console": "integratedTerminal"

If you see the message, "Python interpreter is not found because python.exe is not in the workspace directory," or "You need to install a Python interpreter before you start debugging," then you may have pythonPath: ${workspaceFolder} in your launch.json file, but your Python interpreter actually exists in a different path. Check the value, or remove the pythonPath property altogether.

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, and paste in the following source code:

import matplotlib.pyplot as plt
import numpy as np

x = 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                   # 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. (If you still have "stopOnEntry": true in that configuration, you need to select the run command again to continue.)

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 Integrated Terminal (⌃⇧` (Windows, Linux 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.

  1. Create and activate the virtual environment

    For windows

    py -3 -m venv .venv

    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

    For macOS/Linux

    python3 -m venv .venv
    source .venv/bin/activate
  2. Install the packages

    # Don't use with Anaconda distributions because they include matplotlib already.
    # macOS
    python3 -m pip install matplotlib
    # Windows (may require elevation)
    python -m pip install matplotlib
    # Linux (Debian)
    apt-get install python3-tk
    python3 -m pip install matplotlib
  3. Select your new environment by using the Python: Select Interpreter command from the Command Palette.

  4. Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:

    matplotlib output

  5. Once you are finished, type deactivate in the terminal window to deactivate the virtual environment.

For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.

Next steps

You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.

To learn more about the Python language, follow any of the programming tutorials listed on within the context of VS Code.

To learn to build web apps with the Django and Flask frameworks, see the following tutorials:

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