LogisticsPY Applied — Standalone

## The scenario Network Performance at CubedNet Logistics suspects that two of the eight depots are running below the network-average first-attempt success rate by **more than the spread you'd expect from postcode mix alone**. Operations want this confirmed or ruled out before they reorganise the depot management structure. You receive twelve months of attempt-level data, depot and route reference tables, postcode-area difficulty scores (a propensity-to-fail score from the operations team), public-holiday calendars, and the rolling SLA-rule history. ## Deliverables 1. `route_kpi_monthly.csv` — per-route monthly stats (first-attempt success rate, breach rate, average attempts-to-deliver, compensation paid). 2. `depot_postcode_adjusted.csv` — depot-level performance with and without postcode-mix adjustment (use direct standardisation against the network postcode distribution). 3. `route_anomalies.csv` — routes whose adjusted first-attempt-success-rate is materially below the network adjusted rate (define your threshold, justify it). 4. `findings.md` — 500-700 words: are the suspect depots genuinely underperforming, or is the spread inside what postcode mix explains? Include one concrete operational recommendation. ## Acceptance criteria (summary) pandas/numpy allowed · timezone handling correct (DST transition weeks appear in the dataset) · holiday weeks treated explicitly · direct-standardisation arithmetic correct · ≥3 charts · findings.md substantive · reproducible · ≥10 conventional commits. Full brief, dataset orientation, and starter notebook appear inside the lesson once enrolled.