Question 1

Why a dedicated cleaning collection?

Accepted Answer

**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.

Question 2

What about melt and stack?

Accepted Answer

**Honest answer:** our reference solution pool has *zero `melt` examples* and only **3 `unstack`** examples.

Per our 100/100 quality bar, **we never claim a pattern we don't teach**.

*The `pivot_table` coverage in Stage 4 covers 90%+ of real reshape interview questions.*

Question 3

Is this only fillna and pivot?

Accepted Answer

**No.**

- **Stage 2** covers numeric normalization (cents→dollars, unit conversion)
- **Stage 3** covers categorical standardization with `replace`/`map`/`np.select`
- **Stage 5** covers d4 feature-matrix pipelines combining all of the above with merge and classification

Question 4

How is this different from Python 50's cleaning stage?

Accepted Answer

**Python 50** introduces cleaning across 3 problems in Stage 4.

This collection drills **25 problems exclusively on cleaning + reshape**, including 5 d4 capstones that integrate cleaning into multi-merge feature pipelines — *the shape of real production ETL code.*

Question 5

How long to complete?

Accepted Answer

**~10 hours of focused work.** Most users finish in 1-2 weeks at 2 problems per day.

**Stage 5 is the densest** — give yourself 25-30 minutes per problem there.

Question 6

What pandas version?

Accepted Answer

**pandas 2.x.** All solutions use modern idioms — `.fillna`, `.replace`, `pd.pivot_table`, `np.select`.

Question 7

How does this compare to DataLemur, StrataScratch, or LeetCode for pandas cleaning?

Accepted Answer

Cleaning data is taught in tutorials — usually on cleanly-formatted toy data.

**What LDS Pandas Cleaning & Reshape 25 gives you that they don't:**

- **A 5-stage curriculum** built on **realistically dirty data** — mixed NULLs, type-coerced strings, duplicate rows, mismatched casing, half-formed timestamps, currency-mixed amounts
- **The decision-making, not just the syntax** — when to use forward-fill vs interpolation vs domain-default; when `dropna` is correct vs catastrophic
- **Full pandas + numpy + scipy in your browser** via Pyodide — no install, no Conda, no virtualenv
- **Industry-grade multi-table schemas** — the kind of dirty tables real data engineers and analysts encounter every day
- **Built-in AI Mentor** for guided hints

*Tutorials teach you `fillna`. This collection teaches you when `fillna` is the wrong call.*

LDS Pandas Cleaning & Reshape 25

About this collection

Your Learning Path

Why These 25?

Realistically Dirty Data

Decision-Making, Not Just Syntax

Full Pandas Stack in Your Browser

Skill Coverage

FAQ

Ready to Master Pandas?

LDS Pandas Cleaning & Reshape 25

About this collection

Your Learning Path