I need a fully-packaged, Python 3.8-compatible library that streamlines data analysis workflows. The core modules must handle robust data cleaning routines, generate ready-to-use model input files, and perform light spatial analysis for GIS-style datasets. A clean, idiomatic API and clear separation of concerns are essential because this code will be reused across several internal projects. Here’s what counts as “done” for me: • Package structure ready for PyPI (setup.cfg or pyproject.toml, src layout, type hints where helpful). • Unit tests written with pytest (aiming for >90 % coverage) and an automated test matrix for Python 3.8. • Sphinx (or MkDocs) documentation: quick-start guide, API reference generated from docstrings, and examples that show a full workflow—data cleaning → input file creation → basic GIS analysis. • Continuous Integration script (GitHub Actions preferred) that installs dependencies, runs tests, and builds docs on every push. • One command to build the wheel and the HTML docs, plus a short README explaining how to publish the package. You’re free to choose well-maintained libraries such as pandas, NumPy, GeoPandas, or Shapely where appropriate, but keep external dependencies lean and clearly pinned. All code will be reviewed for clarity, comments, and adherence to PEP 8. Deliverables will be a Git repository containing source, tests, generated docs, and a compiled wheel. Once tests pass and the docs build cleanly, the project is complete.