Getting Started with Python

Let's get started by creating the simplest "Hello World" Python application.

Note: This tutorial uses Python 3; if you're using Python 2, you need to make appropriate changes to the code.


To successfully complete this tutorial, you must do the following:

  1. Install the Python extension.

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

    • The built-in Python installation on Linux.
    • An installation through Homebrew on macOS using brew install python3 (the system install of Python on macOS is not supported).
    • A download from
    • A download from Anaconda (for data science purposes).
  3. (Windows) Make sure the location of your Python interpreter is included in your PATH environment variable. You can check this by running path at the command prompt. If the Python interpreter's folder isn't included, open Windows Settings, search for "environment", select Edit environment variables for your account, then edit the Path variable to include that folder.

Create a folder and source code file

Create an empty folder called "hello", navigate into it, and open VS Code (code) in that folder (.):

mkdir hello
cd hello
code .

From the File Explorer toolbar, press the New File button:

File Explorer New File

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

File Explorer

By using the .py file extension, VS Code interprets this file as Python and evaluates the contents with the Python extension.

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 then you type msg.:

IntelliSense appearing for a variable whose type provides methods

Feel free to experiment with IntelliSense some more, but then revert 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.

Select a Python interpreter

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

From within VS Code, select a Python 3 interpreter using the Python: Select Interpreter command on the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), or by using 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. If you don't see the desired interpreter, see Configuring Python environments.

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, 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.

Run Hello World

It's simple to run with Python. Right-click in the editor 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 (Mac/Linux) or python

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 Ctrl+Enter or right-click and select Run Selection/Line in Python Terminal. This command is very convenient for testing just a part of a file.
  • Use the Python: Start REPL command to opens 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 in by placing the cursor on the print call and pressing F9. Alternately, just click in the editor left gutter next to the line numbers. 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

The command opens a menu of available debuggers, which shows Python and Python Experimental. Select Python. The Python extension then creates a launch.json file that contains a number of configurations, which appear in the configurations drop-down:

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; for now, just select "Python: Current File," which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

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 launch.json, so that the whole configuration appears as follows:

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

Save launch.json, switch to in the editor, then run the debugger by selecting the green arrow in the Debug toolbar or pressing F5. Because stopOnEntry is set to true, the debugger stops on the first line of the file. The current line is indicated with a yellow arrow in the left margin. If you 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 commands to run, step, restart, and stop the program. The Status Bar also changes color (orange in many themes) to indicate debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run along with program output.

Select the green arrow on the debug toolbar to continue running the program (F5), and the debugger stops on the breakpoint. The now-defined msg variable appears in the Local pane and you can work with the variable in the Debug Console (select "Debug Console" in the lower right area of VS Code, or select it from the ... menu if you don't see it):

Debugging step 2 - variable defined

Select the green arrow again 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, remember that you set stopOnEntry in the configuration so that the debugger stops before running any code. To run all the way to the first breakpoint, remove that entry from the configuration.

For full details, see Debugging.


If for some reason VS Code doesn't generate launch.json for you, create a file by that name within the folder named .vscode folder (creating it 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}"

If you see the error below, it means that you attempted to start to debugger when launch.json is selected in the editor rather than

    // 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": ""

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 commonly done with data science.

Return to Explorer, create a new file called, and paste in the following source code:

import matplotlib.pyplot as plt
import matplotlib as mpl
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

Try running the file in the debugger as described in the last section, without any breakpoints. The message "ModuleNotFoundError: No module named 'matplotlib'" indicates that matplotlib and numpy are not installed in the current interpreter's environment, which is expected (unless you're using an Anaconda installation).

To install those packages, switch to the Python Debug Console that is already be open. (The Python Debug Console already has the selected interpreter's environment activated. You can also use Python: Create Terminal command from the Command Palette.) Enter pip3 install matplotlib (macOS/Linux) or pip install matplotlib (Windows) to install the matplotlib package (and its dependencies, such as numpy) into that environment.

Note: On Linux, you may need to install tkinter by running sudo apt-get install python3-tk. Also note that if you don't want to install matplotlib and its dependencies globally then use a virtual environment.

Rerun the program now and after a few moments a plot window appears with the output:

matplotlib output

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.

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

  • Python environments - Control which Python interpreter is used for editing and debugging.
  • 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.
  • Unit testing - Configure unit test environments and discover, run, and debug tests.
  • Settings reference - Explore the full range of Python-related settings in VS Code.