Python Ecosystem

vx provides comprehensive Python support through both standalone Python runtime and the uv package manager.

Supported Tools

Tool	Description
python	Python interpreter (via python-build-standalone)
uv	Fast Python package manager
uvx	Python tool runner (uv tool run)

Python Runtime

vx uses python-build-standalone from Astral for portable Python distributions. Supports Python 3.7 to 3.13+.

Version Support Status

Version	Status	Notes
Python 3.13+	Active	Latest features
Python 3.12	Active	Recommended for production
Python 3.11	Active	Stable
Python 3.10	Active	Stable
Python 3.9	EOL	Last build: 20251120
Python 3.8	EOL	Limited availability
Python 3.7	EOL	Legacy support only

Note: Python versions that have reached End-of-Life (EOL) may have limited availability in python-build-standalone releases.

Installation

# Install latest Python
vx install `python@latest

# Install specific version
vx install python 3.12.8
vx install python 3.11.11
vx install python 3.10.16
vx install python 3.9.21
vx install python 3.8.20
vx install python 3.7.17

# List available versions
vx list python

Running Python

vx python --version
vx python script.py
vx python -m pytest

Recommendation: For pure Python development, we recommend using uv instead of managing Python directly. uv provides faster package installation, built-in virtual environment management, and automatic Python version management.

uv (Recommended)

uv is an extremely fast Python package and project manager. We strongly recommend using uv for Python development as it provides:

• 10-100x faster package installation than pip
• Built-in virtual environment management
• Automatic Python version management
• Modern project management with pyproject.toml

Installation

vx install `uv@latest

Package Management

vx uv pip install requests
vx uv pip install -r requirements.txt
vx uv pip list

Virtual Environments

vx uv venv .venv
vx uv venv .venv --python 3.11

Project Management

vx uv init
vx uv add requests
vx uv sync
vx uv run python script.py

uvx

uvx runs Python tools without installing them globally.

vx uvx ruff check .
vx uvx black .
vx uvx mypy src/
vx uvx pytest
vx uvx jupyter notebook

Project Configuration

[tools]
uv = "latest"

[python]
version = "3.11"
venv = ".venv"

[python.dependencies]
requirements = ["requirements.txt"]
packages = ["pytest", "black", "ruff"]
git = [
    "https://github.com/user/repo.git",
]
dev = ["pytest", "mypy"]

[scripts]
test = "pytest"
lint = "uvx ruff check ."
format = "uvx black ."
typecheck = "uvx mypy src/"

Common Workflows

New Python Project (Recommended)

# Initialize project with uv
vx uv init my-project
cd my-project

# Add dependencies
vx uv add requests pandas

# Run code
vx uv run python main.py

Using Standalone Python

# Install Python directly
vx install python 3.12.8

# Run Python
vx python --version
vx python script.py

Data Science

# Start Jupyter
vx uvx jupyter notebook

# Or JupyterLab
vx uvx jupyter lab

Code Quality

# Lint with ruff
vx uvx ruff check .

# Format with black
vx uvx black .

# Type check with mypy
vx uvx mypy src/

Testing

# Run pytest
vx uvx pytest

# With coverage
vx uvx pytest --cov=src

Virtual Environment Setup

When [python] is configured in vx.toml, vx setup will:

1. Create the virtual environment
2. Install from requirements files
3. Install listed packages
4. Install git dependencies

vx setup
# Creates .venv, installs dependencies

Tips

1. Use uv for development: uv provides the best Python development experience with automatic version management and fast package installation
2. Use uvx for tools: Run linters, formatters, etc. with uvx instead of installing globally
3. Pin Python version: Specify version in vx.toml for reproducibility
4. Use standalone Python for specific needs: When you need a specific Python version without uv's management