Data Analytics Fundamentals: Toolkit

Privacy-safe diagnostics, paths, and logging helpers for analytics projects.

What This Provides

• find_project_root() and safe_relpath_str() for robust, repo-relative paths
• get_logger() for consistent console and file logging (using a standard logging API)
• log_header() for a privacy-safe logging header (shows OS, shell, Python version, repo-relative cwd)

This toolkit is designed for reuse. It works the same locally and in GitHub Actions.

Install (Choose One)

uv add datafun-toolkit

pip install datafun-toolkit

Example

from datafun_toolkit import find_project_root, get_logger, log_header, safe_relpath_str
from pathlib import Path

def main() -> None:
    logger = get_logger("example")
    log_header(logger, "example")

    root = find_project_root()
    logger.info(f"project_root={root.name}")
    logger.info(f"cwd={safe_relpath_str(Path.cwd(), root)}")

if __name__ == "__main__":
    main()

Developer Setup

Install tools:

• git
• uv
• VS Code

One-time setup:

uv self update
uv python pin 3.12
uv sync --extra dev --extra docs --upgrade
uvx pre-commit install
uvx pre-commit run --all-files

Before starting work:

git pull

After working, run checks:

git add -A
uv run ruff format .
uv run ruff check . --fix
uv run pytest --cov=src --cov-report=term-missing
uv run deptry .
uv run bandit -c pyproject.toml -r src
uv run validate-pyproject pyproject.toml

Build and serve docs:

uv run mkdocs build --strict
uv run mkdocs serve

Save progress:

git add -A
git commit -m "update"
git push -u origin main

Annotations

ANNOTATIONS.md

Citation

CITATION.cff

License

MIT

SE Manifest

SE_MANIFEST.md