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LDS Pandas Window & Aggregation 25 — 25 Curated Python Problems
A 5-stage progression through every pandas window/aggregation pattern — groupby with named aggregation, within-group rank, qcut quartile bucketing, IQR transforms, cumulative running totals, shift period-over-period, rolling 7-day means (both flat-daily and per-entity), and 2D pivot capstones. Twenty-five problems on 15 production-grade schemas — the pandas analog of SQL window functions, where naive solutions hit the for-loop trap.
Problems included in LDS Pandas Window & Aggregation 25
- Count Campaigns by Status
- Sessions by Status
- Total Billed Amount by Provider
- Average Driver Rating by City
- Total Revenue by Merchant
- Creative Asset Approval Summary
- Total Revenue by Property
- Claim Stats by Place of Service and Network
- Trip Stats by City and Service Level
- Portfolio Holdings Summary
- Rank Users by Post Count
- Rank Listings by Price Within City
- Trade Amount Quartile Bucketing
- Anomalous Transaction Detection (IQR)
- Message Activity Feature Engineering
- Running Total of Loan Payments
- Daily Revenue Change vs Previous Day
- 7-Day Moving Average Freight Cost
- 7-Day Rolling Average Usage per Org
- 7-Day Moving Average Order Value
- Pivot Tickets by Channel
- Pivot Order Counts by Cuisine and Fulfillment Type
- Pivot Rating Distribution by Genre
- Carrier Performance Feature Matrix
- Customer Feature Matrix