PyDOE
PyDOE is a Python package for design of experiments (DOE), enabling scientists, engineers, and statisticians to efficiently construct experimental designs.
A Python package for constructing factorial, fractional-factorial, response-surface, and other design-of-experiments matrices.
About the Project
Why use this?
PyDOE helps engineers construct experimental designs efficiently, including full-factorial, fractional-factorial, Plackett-Burman, response-surface, Latin-hypercube, and other DOE structures. It is useful for planning experiments that identify significant factors, interactions, and optimal operating conditions.
Lean and Six Sigma practitioners choose PyDOE for the Design and Improve phases of DMAIC, where structured experimentation is needed to optimize processes with limited trials. It pairs naturally with statsmodels for analyzing experimental results and with Python visualization libraries for interpreting factor effects and response surfaces. [413]
Key Features
- Design-of-experiments matrix generation in Python
- Full and fractional factorial designs
- Response-surface and Latin-hypercube designs
- Useful for factor screening, optimization, and process improvement
- Pairs well with statsmodels for DOE analysis
- Reference / Repo
- https://github.com/pydoe/pydoe
- Tool / Hosted
- https://github.com/pydoe/pydoe
- Replaces / Alternative to
- Minitab DOE, Design-Expert, JMP DOE
- Free layer
- open-source