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Why These 25?

Hand-curated for maximum interview ROI.

Realistically Dirty Data

Mixed NULLs, type-coerced strings, duplicate rows, mismatched casing, half-formed timestamps, currency-mixed amounts. The shape of dirty data real data engineers and analysts encounter every day — not toy already-clean tables.

Decision-Making, Not Just Syntax

You don’t just learn fillna — you learn when to use forward-fill vs interpolation vs domain-default, when dropna is correct vs catastrophic. The judgment that separates juniors from seniors.

Full Pandas Stack in Your Browser

pandas, numpy, scipy — all run in your browser via Pyodide. No install, no Conda, no virtualenv. The same stack a real analyst uses, available instantly.

Skill Coverage

How the 25 problems distribute across pandas topics.

fillna for Missing Data
5
Numeric Normalization & Unit Conversion
5
Category Standardization (replace/map/np.select)
5
pivot_table Wide Reshape
5
d4 Complex Cleaning Pipelines
3
d4 Feature Matrix Engineering
2

FAQ

Data cleaning consumes 60-80% of real data work.

Every analytics platform that teaches pandas focuses on the fun parts (groupby, merge) and skips the part that fills your actual workdays.

This collection drills the cleaning + reshape patterns that show up in every ETL pipeline.

Ready to Master Pandas?

Start with Stage 1 — graded instantly in your browser.

Start Problem 1Browse All Problems

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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