LDS Pandas Cleaning & Reshape 25 — 25 Curated Python Problems

A 5-stage progression on the pandas work most courses skip: filling missing data, normalizing units and free-text categories, pivoting long DataFrames into wide cross-tabulations, and assembling production-grade feature matrices from messy multi-table pipelines. Twenty-five problems on 15 industry schemas — the practice nobody gives you before your first dirty-data interview.

Problems included in LDS Pandas Cleaning & Reshape 25

1. Fill Missing User Income Data
2. Fill Missing Patient Contact Info
3. Fill Missing HOA Fees
4. Fill Missing Phone Numbers
5. Fill Missing User Bios
6. Normalize Impression Costs to USD
7. Normalize Payment Amounts
8. Normalize Freight Costs
9. Normalize Charge Amounts to Dollars
10. Normalize Bill Amounts
11. Standardize Merchant Categories
12. Standardize Payment Methods
13. Standardize Payment Methods
14. Standardize Ticket Priority Levels
15. Standardize Device OS into Platform Categories
16. Pivot Impressions by Country and Device
17. Reservation Count by Channel and Status
18. Balance Transaction Pivot by Source Type
19. Listing Metrics Pivot by Property Type
20. Moderation Reports by Reason and Status
21. Insurance Plan Health Classification
22. Portfolio Feature Matrix
23. Restaurant Feature Matrix
24. Driver Feature Matrix
25. Subscriber Health Classification