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Why These 25?

Hand-curated for maximum interview ROI.

Every Window/Aggregation Pattern

groupby + agg with named outputs, groupby + transform to broadcast back, within-group .rank(), pd.qcut bucketing, .cumsum() running totals, .shift() previous-period diffs, and .rolling(7).mean() for moving averages. The complete pandas window surface — drilled, not glanced at.

Flat-Daily vs Per-Entity Rolling

The trap that bites real production analytics queries — when a 7-day rolling average should partition by user_id and when it shouldn’t. We drill it explicitly so you don’t learn it the hard way.

SQL-to-Pandas Mental Model

Each pattern is taught alongside its SQL equivalent — WINDOW PARTITION BY ... ORDER BY ↔ groupby().rolling() — so candidates fluent in one language pick up the other in days, not weeks.

Skill Coverage

How the 25 problems distribute across pandas topics.

Single-Key GroupBy + Basic Agg
5
Multi-Key GroupBy + Named Agg
5
Within-Group .rank() and pd.qcut
3
groupby().transform() for Group-Relative Metrics
2
GroupBy + .cumsum() Running Totals
1
GroupBy + .shift() Previous-Period Diff
1
Rolling Mean (flat-daily + per-entity)
3
pd.pivot_table 2D Cross-Tab
3
d4 Feature Matrix Capstones
2

FAQ

The first 5 problems (Stage 1) are completely free.

Stages 2-5 require a Premium subscription, which gives you access to all 1,500+ problems across 15 production-grade datasets.

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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

  1. Count Campaigns by Status
  2. Sessions by Status
  3. Total Billed Amount by Provider
  4. Average Driver Rating by City
  5. Total Revenue by Merchant
  6. Creative Asset Approval Summary
  7. Total Revenue by Property
  8. Claim Stats by Place of Service and Network
  9. Trip Stats by City and Service Level
  10. Portfolio Holdings Summary
  11. Rank Users by Post Count
  12. Rank Listings by Price Within City
  13. Trade Amount Quartile Bucketing
  14. Anomalous Transaction Detection (IQR)
  15. Message Activity Feature Engineering
  16. Running Total of Loan Payments
  17. Daily Revenue Change vs Previous Day
  18. 7-Day Moving Average Freight Cost
  19. 7-Day Rolling Average Usage per Org
  20. 7-Day Moving Average Order Value
  21. Pivot Tickets by Channel
  22. Pivot Order Counts by Cuisine and Fulfillment Type
  23. Pivot Rating Distribution by Genre
  24. Carrier Performance Feature Matrix
  25. Customer Feature Matrix