Last updated: May 25, 2026

Using article:write to guide the polish pass. The brief and reviewer notes are fully provided, so I’ll write the expanded article directly.

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Image: Microsoft Visual Studio Code Documentation

How long does it actually take to go from zero to running Python code in a professional editor? If your answer involves downloading separate IDEs, wrestling with PATH variables, or spending an afternoon reading documentation, you probably have not tried the Python VS Code setup workflow recently.

By the end of this guide you will have Python installed, the Microsoft Python extension configured, a virtual environment running, and your first script executing – all inside Visual Studio Code. You will also understand what each step does, so you can adapt it for real projects rather than follow instructions blindly.

Prerequisites

Image: Microsoft Visual Studio Code Documentation

Before diving in, confirm you have the following:

• Visual Studio Code – download from code.visualstudio.com (see our coverage of Visual Studio Code 1.120 for recent feature highlights)
• Python 3.8 or later – download from python.org; during installation on Windows, tick “Add Python to PATH”
• Basic familiarity with file systems and opening a terminal
• No prior VS Code extension experience required

Why VS Code Works Well for Python

The Microsoft Python extension (ms-python.python) is the foundation of the VS Code Python experience. Once installed, it adds IntelliSense (context-aware autocomplete), inline linting, an integrated debugger, environment management, and test runner support. That is a single, actively maintained extension – not a loosely connected collection of plugins – that understands the Python runtime you have selected.

VS Code’s approach is particularly strong for polyglot projects. A developer building a web application that mixes Python on the back-end with JavaScript or TypeScript on the front-end gets a consistent editing experience in one tool. If you are weighing up whether Python or JavaScript suits your project, our Node.js vs Python – A Practical Guide for Developers comparison covers the decision in detail.

How to Complete Your Python VS Code Setup: Step by Step

Step 1 – Install the Python Extension

Why this step matters: Without the Python extension, VS Code treats .py files as plain text. The extension connects VS Code to your Python installation and enables every feature in this guide.

Open VS Code. Press Ctrl+Shift+X (Windows/Linux) or Cmd+Shift+X (macOS) to open the Extensions panel.

Search for Python and select the extension published by Microsoft (identifier: ms-python.python). Click Install.

Expected result: After installation, VS Code displays a notification and prompts you to also install Pylance (for language intelligence) and Python Debugger (for breakpoints). Accept both – they work as a unit with the core extension.

Common mistake: Installing a third-party Python extension rather than Microsoft’s official one. Always check the publisher name in the detail panel before clicking Install.

Step 2 – Select Your Python Interpreter

Why this step matters: VS Code can see multiple Python versions on your machine. You need to tell it explicitly which one to use – this affects IntelliSense suggestions, linting results, and which packages are available when you run scripts.

Open the Command Palette with Ctrl+Shift+P (Cmd+Shift+P on macOS) and type:

Python: Select Interpreter

VS Code scans your system and lists every Python installation it finds, including Conda environments and virtual environments.

Expected result: You see a list similar to:

Python 3.12.3 64-bit
Python 3.11.0 ('venv': venv)
~/anaconda3/envs/myenv/bin/python

Select the version you want to use for this project. The selected interpreter then appears in the status bar at the bottom left of the window.

Before selection: The status bar shows “Select Python Interpreter” and IntelliSense does not activate.
After selection: The status bar shows your Python version (for example 3.12.3), and autocomplete activates immediately.

If you see “No interpreters found”: Python is not on your PATH. On Windows, re-run the Python installer and tick “Add Python to PATH”. On macOS, run brew install python3 or download directly from python.org. Then reload VS Code and run Python: Select Interpreter again.

Step 3 – Create a Project Folder and Open It

Why this step matters: VS Code’s environment detection, linting, and integrated terminal all behave correctly only when you open a workspace folder – not an individual file. Opening a folder is what tells VS Code where to look for your virtual environment and settings.

Create a folder for your project, then open it in VS Code:

mkdir my-python-project
cd my-python-project
code .

The code . command opens VS Code with the current directory as the workspace root.

If code is not recognised in your terminal: Open VS Code manually and use File > Open Folder. On macOS you can also enable the command by opening the Command Palette and searching for Shell Command: Install ‘code’ command in PATH.

Step 4 – Set Up a Virtual Environment

Why this step matters: Without a virtual environment, every pip install goes into your global Python installation, which causes version conflicts when different projects need different versions of the same library. A virtual environment gives each project its own isolated package directory, making your project reproducible on any other machine.

Open the integrated terminal in VS Code with Ctrl+` and run:

python -m venv .venv

This creates a .venv folder inside your project directory containing a self-contained Python installation.

Expected result: VS Code detects the new environment and displays a notification: “We noticed a new virtual environment has been created. Do you want to select it for the workspace folder?” Click Yes. VS Code now uses this environment for all subsequent operations in this workspace.

To activate the environment manually if needed:

# Windows
.venv\Scripts\activate

# macOS / Linux
source .venv/bin/activate

Before activation: Your terminal shows your normal shell prompt. Any pip install command affects your global Python.
After activation: Your prompt changes to (.venv) $. Packages installed now stay inside this project only.

To confirm which Python is active after activation, run which python (macOS/Linux) or where python (Windows). The path should point inside your .venv folder, not to a system-level Python.

Common mistake: Running pip install before activating the virtual environment. Always check your prompt shows (.venv) before installing anything.

Step 5 – Write and Run Your First Script

Why this step matters: Confirming the entire setup works end-to-end – interpreter, virtual environment, and terminal integration – before writing real code saves troubleshooting time later.

Create a new file called hello.py in your project folder. VS Code recognises the .py extension and applies syntax highlighting immediately.

name = input("What is your name? ")
print(f"Hello, {name}! Your Python VS Code setup is working.")

This script uses an f-string to embed the variable name directly in the printed output. It is a quick test that both Python and your terminal integration are functioning correctly.

Run the script by pressing F5 or by right-clicking in the editor and selecting Run Python File in Terminal. VS Code opens an integrated terminal, activates your virtual environment, and executes the script.

Expected output:

What is your name? Alex
Hello, Alex! Your Python VS Code setup is working.

If the script runs but your terminal prompt does not show (.venv): Your virtual environment is not active. Open a new terminal inside VS Code (Ctrl+``), which should auto-activate the workspace environment. If it does not, run the activation command from Step 4 manually before running the script again.

Step 6 – Add Linting

Why this step matters: Linting catches code-style errors and potential bugs as you type, before you run the script. It flags undefined variables, unused imports, and inconsistent indentation – issues that would cause runtime errors in production.

Install pylint inside your virtual environment:

pip install pylint

This installs pylint into your .venv directory only, not globally. Then open the Command Palette and search for Python: Select Linter. Choose pylint from the list.

Expected result: Pylint begins analysing your code automatically. Red underlines indicate errors; yellow underlines indicate warnings. Hover over any underline to see the message, or press Ctrl+. to open the quick-fix menu with suggested corrections.

If you prefer a stricter style checker, flake8 is a widely used alternative:

pip install flake8

Select it via the same Command Palette command.

Troubleshooting: If linting is not showing results after installation, check that your virtual environment is active and that the interpreter selected in Step 2 matches the environment where you installed pylint. Run which pylint (macOS/Linux) or where pylint (Windows) – the path should point inside .venv. If it does not, activate the environment and reinstall.

What Most Developers Miss in Their VS Code Python Workflow

The six steps above get you running. These features separate a basic configuration from a genuinely productive environment.

Automatic code formatting. The Black formatter imposes a consistent style on your code without requiring configuration decisions. Install it with pip install black, then add "editor.formatOnSave": true and "editor.defaultFormatter": "ms-python.black-formatter" to your VS Code settings. Every time you save a file, Black reformats it automatically. This eliminates style debates on team projects and keeps version-control diffs clean.

Keyboard shortcuts worth learning. Ctrl+. opens the quick-fix and suggestion menu at the cursor – useful for importing missing modules or applying lint fixes with a single keypress. Ctrl+Click on any function or class name navigates to its definition, whether in your own code or inside an installed library. These two shortcuts alone significantly reduce time spent hunting through files.

Interactive mode and notebook-style execution. VS Code supports Python Interactive mode, which lets you run sections of a script as cells – similar to Jupyter notebooks – directly in the editor. Add a # %% comment above any block of code to turn it into an executable cell. This is particularly useful for data exploration and prototyping without switching to a separate Jupyter interface.

Framework-specific tutorials. If you are building a web application, VS Code includes dedicated tutorials for Django, FastAPI, and Flask. Each covers project structure, debugging configuration, and running a development server within VS Code’s integrated terminal. These address framework-specific tooling such as Django’s manage.py commands and Flask’s debug mode – not just generic Python advice. For context on how Python web frameworks differ structurally from PHP-based platforms, our article on what WordPress is and how it works provides a useful contrast.

Debugging beyond print statements. Press F9 on any line to set a breakpoint. Press F5 to start the debugger. VS Code pauses execution at the breakpoint and shows the value of every variable in scope in the sidebar. This is the single largest productivity gain for developers who currently debug by inserting print() calls throughout their code.

Containerised and deployed Python. The Dev Containers extension lets you run your entire development environment inside a Docker container, ensuring consistency across machines and CI pipelines. This is particularly valuable on teams where “works on my machine” is otherwise a recurring problem.

Next Steps

Once your environment is stable, consider these areas next:

• Testing – install pytest and use VS Code’s Testing panel (Ctrl+Shift+T) to run and visualise test results
• Formatting – install black and configure VS Code to format on save via "editor.formatOnSave": true in your settings
• Source control – VS Code’s built-in Git integration handles commits, diffs, and branches without leaving the editor
• Containers – install the Dev Containers extension if you need a reproducible environment across multiple machines or team members
• Web frameworks – follow VS Code’s official Django or Flask tutorials to build your first web application

Getting a professional Python environment running in VS Code takes less than fifteen minutes when you follow these steps in order. The real payoff is not the first “Hello, World” – it is the linting that catches bugs before they run, the debugger that replaces hours of guesswork, and the virtual environment that keeps your projects cleanly separated.

If you are working on a more complex Python project and need professional development support, the team at DRS Web Solutions is happy to help with architecture, code review, and deployment.

Frequently Asked Questions

Q: Do I need to install Python before installing the VS Code Python extension?
A: Yes. The Python extension in VS Code requires a Python interpreter to be installed on your system. Install Python from python.org first, then install the extension – VS Code will detect your Python installation automatically when you open the interpreter selector.

Q: What is the difference between the Python extension and Pylance?
A: The Python extension (ms-python.python) provides core runtime integration – running scripts, selecting interpreters, and debugging. Pylance is a separate language server that adds faster IntelliSense and advanced type checking. The Python extension prompts you to install Pylance automatically, and the two work together.

Q: Why should I use a virtual environment instead of installing packages globally?
A: Global package installation causes version conflicts when different projects require different versions of the same library. A virtual environment gives each project its own isolated package directory, so installing or upgrading a package for one project cannot break another.

Q: Can I use VS Code for Python web development, not just scripts?
A: Yes. VS Code includes dedicated tutorials for Django, FastAPI, and Flask. It supports running development servers in the integrated terminal, debugging web applications with breakpoints, and deploying to cloud platforms – making it suitable for full production web development workflows.

Q: What does “Python: Select Interpreter” do and why does it matter?
A: This command tells VS Code which Python installation to use for the current workspace. It affects IntelliSense suggestions, linting results, and which packages are available when you run scripts. If you have multiple Python versions or virtual environments, selecting the correct interpreter ensures VS Code reflects the right environment.

Q: My linter is installed but VS Code is not showing any warnings – what is wrong?
A: The most common cause is a mismatch between the selected interpreter and the environment where the linter is installed. Open the Command Palette, run Python: Select Interpreter, and confirm it points to the .venv folder in your project. Then verify the linter is installed in that environment by activating it in the terminal and running pip show pylint (or pip show flake8). If the package is missing, install it again with the environment active.

Source: https://code.visualstudio.com/docs/python/python-quick-start