Once Python is installed on your machine, the next decision is where you'll actually write your code. This guide walks through setting up VS Code for Python and setting up PyCharm for Python, so you can get either environment configured correctly the first time — interpreter, extensions, linting, and debugging included.
Introduction: VS Code vs PyCharm — Which Should You Choose?
Both are excellent choices, and the "right" one depends more on what you're building than on any objective quality gap.
VS Code is a lightweight, general-purpose editor. On its own, it doesn't know anything about Python — it becomes a Python environment through extensions. That makes it fast, flexible, and a natural fit if you work across multiple languages (say, Python plus JavaScript, or Python plus Go).
PyCharm is the opposite philosophy: a Python-first, batteries-included IDE. Interpreter management, a test runner, refactoring tools, and a debugger are all built in from the moment you open it. Less configuration, but a heavier, more opinionated tool.
Here's the thing worth knowing before you install either one: your interpreter setup matters more than which editor you pick. A misconfigured interpreter will cause the same headaches — wrong package versions, broken imports, mysterious errors — regardless of which tool you're using. Get that part right, and either editor works well.
Who should pick which:
VS Code — polyglot developers, people building web apps or working across several languages, anyone who wants a fast, customizable editor.
PyCharm — developers doing dedicated Python or data science work who want a guided, all-in-one experience with less setup friction.
Let's set up both, starting with VS Code.
Setting Up VS Code for Python
Install VS Code and the Python extension
Download VS Code from its official site, then open the Extensions panel (Ctrl+Shift+X / Cmd+Shift+X) and search for Python — the official Microsoft extension. Installing it also pulls in a few essentials automatically: Pylance (the language server), and depending on your setup, prompts for Jupyter and import-sorting support.
Selecting the Python interpreter
This is the step people skip and then regret. VS Code needs to know which Python installation — and which virtual environment — to use for a given project.
Open the Command Palette (
Ctrl+Shift+P/Cmd+Shift+P).Type and select Python: Select Interpreter.
Choose the interpreter you want — ideally the one inside your project's virtual environment.
You'll see the active interpreter listed in the bottom status bar. If it's ever wrong, this is the first place to check.
Essential extensions
Beyond the core Python extension, a few others round out a solid python interpreter setup:
Pylance — powers IntelliSense, type checking, and fast autocomplete. It's installed automatically alongside the Python extension now, so most people already have it without realizing it.
Python Debugger (debugpy) — handles breakpoints, step-through debugging, and variable inspection. This used to be bundled directly into the Python extension; it's now a separate install.
Jupyter — lets you run
.ipynbnotebooks directly inside VS Code, complete with a variable explorer.Ruff — a fast linter and formatter written in Rust. It replaces the older combination of Flake8, Black, and isort with a single extension, and it's become the default recommendation for new setups in 2026 because of how much faster it runs.
If you're only installing three things, make it Python, Pylance, and Ruff. That combination alone gives you type checking, linting, and formatting with almost no extra configuration.
Setting up a virtual environment VS Code can detect
Create your virtual environment the normal way from a terminal inside your project folder:
# Create a virtual environment named .venv
python -m venv .venv
VS Code automatically scans for a .venv folder in your project root and will usually offer to select it as your interpreter without you doing anything else. If it doesn't pick it up within a few seconds, run Python: Select Interpreter again and choose it manually.
Running and debugging a script
Click the Run (▶) button in the top-right corner to execute the current file.
Click in the gutter next to a line number to set a breakpoint, then run with Run and Debug (
F5) instead of the plain Run button — execution will pause there so you can inspect variables.The integrated terminal doubles as a REPL — you can drop into
pythondirectly inside it to test snippets without leaving the editor.
Setting Up PyCharm for Python
PyCharm's setup process is more guided, since environment configuration happens right at project creation.
Creating a new project and choosing an environment
When you create a new project, PyCharm prompts you to choose an interpreter type immediately — venv, Conda, or Poetry, among others. Pick venv if you're not sure; it's the standard choice and matches what most tutorials assume. PyCharm creates and activates the environment for you as part of project setup, so there's no separate manual step like there is in VS Code.
Configuring the interpreter later
If you need to change it after the fact, or you're opening an existing project:
Go to File → Settings → Project → Python Interpreter (on macOS: PyCharm → Preferences → Project → Python Interpreter).
Select an existing interpreter, or click the gear icon to add a new one.
What comes built in
This is where PyCharm's "batteries-included" reputation earns itself:
Run/debug configurations are created automatically when you run a file — no separate extension needed.
A built-in test runner detects and runs pytest or unittest tests from a dedicated panel.
Refactoring tools — rename a variable or function across an entire project safely, extract methods, and more, all without extensions.
Installing packages
You can install packages two ways:
Through the UI — Settings → Project → Python Interpreter → the "+" button, then search and install by name.
Through PyCharm's built-in terminal, using pip exactly as you would anywhere else:
# Install a package inside PyCharm's terminal
pip install requests
Either method installs into the interpreter currently selected for the project, so there's no risk of accidentally installing into the wrong environment.
Key Configuration Tips for Either IDE
Workspace-level vs. user-level settings
In VS Code, settings can live at the user level (apply everywhere) or the workspace level, stored in a .vscode/settings.json file inside the project. Workspace settings are worth using for anything project-specific — like which formatter to run on save — since they travel with the project if you share it with teammates via version control. PyCharm has an equivalent split between IDE-wide settings and per-project settings under the same Settings menu.
Recommended linting and formatting setup
Ruff has become the practical default for python linting ruff pylint discussions in 2026 — it's fast enough to run on every save without noticeable lag, and it covers both linting and formatting in one tool. If you're working in an established codebase that already standardized on Pylint or Black, there's no urgent need to migrate, but for new projects, Ruff is the simpler starting point.
A typical VS Code setting to format automatically on save:
{
"[python]": {
"editor.defaultFormatter": "charliermarsh.ruff",
"editor.formatOnSave": true,
"editor.codeActionsOnSave": {
"source.fixAll.ruff": "explicit",
"source.organizeImports.ruff": "explicit"
}
}
}
PyCharm supports Ruff as well, either through its built-in plugin marketplace or by configuring it as an external tool.
Jupyter notebook support
Both editors support notebooks, but the common trap is a kernel that doesn't match your project's interpreter. If you install a package into your virtual environment but a notebook cell can't find it, check which kernel the notebook is actually running — it's easy to end up on a global or default kernel instead of the one tied to your project's .venv.
Common pitfall: mismatched environments
This is worth repeating because it causes so much confusion: your terminal, your notebook kernel, and your editor's selected interpreter can all silently point to different Python environments at the same time. If code runs fine in one place but throws ModuleNotFoundError in another, this mismatch is almost always the cause. Check all three whenever something behaves inconsistently.
Which One Should You Stick With?
Quick decision guide: if you value flexibility, work across multiple languages, or want a lighter tool you can shape to your workflow, go with VS Code. If you want a guided, all-in-one experience specifically built around Python — especially for larger or data-science-heavy projects — PyCharm's built-in tooling will save you setup time.
One more thing shaping both workflows in 2026: AI-assisted coding tools are now a standard part of the picture, not an add-on. GitHub Copilot integrates directly into VS Code, and agentic tools like Claude Code plug into both editors' terminals to handle multi-step coding tasks. Neither replaces understanding your setup — if anything, a correctly configured interpreter and linter make AI-generated code easier to trust, since your tooling will actually catch its mistakes.
Honestly, the best way to decide is to try both on a small project. Set up VS Code for python one afternoon, PyCharm python setup the next, and see which one gets out of your way faster. You can't go wrong with either — just don't skip the interpreter setup, whichever you land on.