Python Data Analytics

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Use when computing complex analytics, KPIs, cohort/funnel/retention metrics, financial math (IRR/NPV/amortization), statistical tests, anomaly detection, or geospatial analytics in Python — for cases where SQL alone gets unwieldy.
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to python-data-analytics or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.