This skill should be used when the user asks to "set up pixi", "add pixi dependencies", "create pixi environment", "migrate from conda to pixi", "configure pixi.toml", "add pixi tasks", "set up...
Master pixi, the modern package manager that unifies conda and PyPI ecosystems for fast, reproducible scientific Python development. Learn how to manage complex scientific dependencies, create isolated environments, and build reproducible workflows using pyproject.toml integration.
Official Documentation: https://pixi.sh GitHub: https://github.com/prefix-dev/pixi
# Install pixi (macOS/Linux)
curl -fsSL https://pixi.sh/install.sh | bash
# Install pixi (Windows)
iwr -useb https://pixi.sh/install.ps1 | iex
# Initialize new project with pyproject.toml
pixi init --format pyproject
# Initialize existing Python project
pixi init --format pyproject --import-environment
# Add dependencies
pixi add numpy scipy pandas # conda packages
pixi add --pypi pytest-cov # PyPI-only packages
pixi add --feature dev pytest ruff # dev environment
# Install all dependencies
pixi install
# Run commands in environment
pixi run python script.py
pixi run pytest
# Shell with environment activated
pixi shell
# Add tasks
pixi task add test "pytest tests/"
pixi task add docs "sphinx-build docs/ docs/_build"
# Run tasks
pixi run test
pixi run docs
# Update dependencies
pixi update numpy # update specific
pixi update # update all
# List packages
pixi list
pixi tree numpy # show dependency tree
Need compiled scientific libraries (NumPy, SciPy, GDAL)?
├─ YES → Use pixi (conda-forge has pre-built binaries)
└─ NO → Consider uv for pure Python projects
Need multi-language support (Python + R, Julia, C++)?
├─ YES → Use pixi (supports conda ecosystem)
└─ NO → uv sufficient for Python-only
Need multiple environments (dev, test, prod, GPU, CPU)?
├─ YES → Use pixi features for environment management
└─ NO → Single environment projects work with either
Need reproducible environments across platforms?
├─ CRITICAL → Use pixi (lockfiles include all platforms)
└─ LESS CRITICAL → uv also provides lockfiles
Want to use both conda-forge AND PyPI packages?
├─ YES → Use pixi (seamless integration)
└─ ONLY PYPI → uv is simpler and faster
Legacy conda environment files (environment.yml)?
├─ YES → pixi can import and modernize
└─ NO → Start fresh with pixi or uv
Pixi resolves dependencies from both conda-forge and PyPI in a single unified graph, ensuring compatibility:
[project]
name = "my-science-project"
dependencies = [
"numpy>=1.24", # from conda-forge (optimized builds)
"pandas>=2.0", # from conda-forge
]
[tool.pixi.pypi-dependencies]
my-custom-pkg = ">=1.0" # PyPI-only package
Why this matters for scientific Python:
Pixi generates pixi.lock with dependency specifications for all platforms (Linux, macOS, Windows, different architectures):
# pixi.lock includes:
# - linux-64
# - osx-64, osx-arm64
# - win-64
Benefits:
Create multiple environments using features without duplicating dependencies:
[tool.pixi.feature.test.dependencies]
pytest = ">=7.0"
pytest-cov = ">=4.0"
[tool.pixi.feature.gpu.dependencies]
pytorch-cuda = "11.8.*"
[tool.pixi.environments]
test = ["test"]
gpu = ["gpu"]
gpu-test = ["gpu", "test"] # combines features
Define reusable commands as tasks:
[tool.pixi.tasks]
test = "pytest tests/ -v"
format = "ruff format src/ tests/"
lint = "ruff check src/ tests/"
docs = "sphinx-build docs/ docs/_build"
analyse = { cmd = "python scripts/analyze.py", depends-on = ["test"] }
Pixi uses rattler (Rust-based conda resolver) for 10-100x faster resolution than conda:
Pixi reads standard Python project metadata from pyproject.toml, enabling:
# Create new project
mkdir climate-analysis && cd climate-analysis
pixi init --format pyproject
# Add scientific stack
pixi add python=3.11 numpy pandas matplotlib xarray
# Add development tools
pixi add --feature dev pytest ipython ruff
# Create analysis script
cat > analyze.py << 'EOF'
import pandas as pd
import matplotlib.pyplot as plt
# Your analysis code
data = pd.read_csv("data.csv")
data.plot()
plt.savefig("output.png")
EOF
# Run in pixi environment
pixi run python analyze.py
# Or activate shell
pixi shell
python analyze.py
See references/patterns.md for detailed patterns including:
Ready-to-use templates are available in the assets/ directory:
See references/common-issues.md for solutions to:
pixi init --format pyproject for new projectspython>=3.11,<3.13).gitignore to exclude .pixi/ directorypixi update regularly to get security patchespixi.lock to version controlprefix-dev/setup-pixi action)pixi clean cachepixi shell for interactive developmentpixi run for automated scripts and CIPixi revolutionizes scientific Python development by unifying conda and PyPI ecosystems with blazing-fast dependency resolution, reproducible multi-platform lockfiles, and seamless environment management. By leveraging pyproject.toml integration, pixi provides a modern, standards-compliant approach to managing complex scientific dependencies while maintaining compatibility with the broader Python ecosystem.
Key advantages for scientific computing:
Whether you're conducting reproducible research, developing scientific software, or managing complex data analysis pipelines, pixi provides the robust foundation for modern scientific Python development. By replacing conda/mamba with pixi, you gain speed, reliability, and modern workflows while maintaining full access to the scientific Python ecosystem.
Ready to get started? Install pixi, initialize your project with pixi init --format pyproject, and experience the future of scientific Python package management.