Python Analytics Playground

Context

In product analytics, most tasks start the same way: load an export, clean it, look at distributions and correlations, show charts. This project turns that routine into reusable modules — so every new analysis starts not from scratch, but from a tested foundation.

Data & Method

Package layout (src/python_analytics/):

load.py (REQ-002) — CSV loading into a DataFrame with basic checks.
clean.py (REQ-003) — missing values, duplicates, type coercion.
eda.py (REQ-004) — descriptive stats, missingness, correlations.
viz.py (REQ-005) — histograms, correlation heatmaps.
pipeline.py (REQ-006) — end-to-end pipeline runnable as python -m python_analytics.

Each module covers a single requirement from the PRD (docs/prd.md), and the tests in tests/ check exactly those requirements — coverage ≥80%.

Run

uv sync --all-groups
uv run pytest                          # tests + coverage (≥80%)
uv run python -m python_analytics      # end-to-end pipeline

Findings

The key difference from one-off analysis scripts is structure and testability: modules are small and single-purpose, requirements are documented in a PRD, and tests keep coverage ≥80%. This makes the tool “product-grade”: you can hand it to teammates, extend it, and not fear breaking it.

Impact

Reusability — a starting point for every new analysis instead of a from-scratch script.
Coverage ≥80% — changes don’t silently break existing behavior.
PRD tracing — each module maps to a requirement, tests verify requirements.
uv tooling — reproducible environment with no dependency drift.

Documentation

GitHub → python