Question 1

Is this free?

Accepted Answer

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

Stages 2-5 require a **Premium subscription**, which gives you access to all **1,625 problems** across 15 production-grade datasets.

Question 2

Why a dedicated window-and-aggregation collection?

Accepted Answer

After pandas merge, **"groupby + rank + cumsum + rolling + qcut + pivot"** is the densest pattern cluster in pandas interviews and the **#1 source of interview failure** for SQL developers moving to pandas.

`df.groupby.agg.transform` does *not* map cleanly to SQL's `GROUP BY... OVER...` mental model.

*This collection isolates the cluster across 5 progressive stages on real production schemas.*

Question 3

Should I do LDS Pandas Merge 25 first?

Accepted Answer

**Yes** if you have not used pandas before.

Stages 2 and 5 include problems where the groupby key is created via `.merge` first, so basic merge fluency helps.

*If you already write merges comfortably, jump straight into Stage 1 here.*

Question 4

What does Stage 3 mean by "post-aggregation rank" vs "within-group rank"?

Accepted Answer

**Two distinct patterns.**

- **Position #11** first aggregates with groupby to get a per-user count, then applies `.rank` **globally** on the resulting Series.
- **Position #12** keeps row-level data and partitions the rank within each city using `df.groupby('city')['price'].rank`.

*Different mechanics, different output shapes. Real interviews test both.*

Question 5

Why do you teach BOTH flat-daily rolling AND per-entity rolling in Stage 4?

Accepted Answer

**Because both come up in interviews and they are not the same operation.**

- **Positions #18 and #20** aggregate to a single daily series first, then apply `.rolling(7).mean` globally — useful for *platform-wide trend analysis*.
- **Position #19** keeps the per-entity dimension and applies `df.groupby('org_id').transform(lambda x: x.rolling(7).mean)` to compute a *7-day average within each org*.

*Conflating these is a common interview failure.*

Question 6

Do I need to know NumPy for this collection?

Accepted Answer

**Lightly.** Stage 5 capstones (positions #24 and #25) use `np.where`, `np.log1p`, and min-max normalization — *explained inline in the solution*.

If you have not seen these, the solution explainers walk through each step.

Question 7

How long does it take to complete all 25?

Accepted Answer

**Most users complete in 7-10 days** solving 3-4 problems per day.

Total estimated time is **~10 hours** of focused practice.

Question 8

How does this compare to DataLemur, StrataScratch, or LeetCode for pandas window/aggregation?

Accepted Answer

Groupby is taught everywhere — usually as flat single-column aggregation.

**What LDS Pandas Window & Aggregation 25 gives you that they don't:**

- **A 5-stage curriculum** covering every window/aggregation pattern actually tested: `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()` in both flat-daily and per-entity flavors
- **The flat-daily vs per-entity rolling distinction** — the trap that bites real production analytics queries; we drill it explicitly
- **The SQL-to-pandas mental model** — when `WINDOW PARTITION BY ... ORDER BY` corresponds to `groupby().rolling()` and when it doesn't
- **Real time-series data** on industry schemas — payments, sessions, ad events, support tickets — not synthetic 10-row tables
- **Full pandas + numpy in your browser** via Pyodide — no install

*Tutorials teach `groupby`. This collection teaches the multi-window composition real DS pipelines actually demand.*

LDS Pandas Window & Aggregation 25

About this collection

Your Learning Path

Why These 25?

Every Window/Aggregation Pattern

Flat-Daily vs Per-Entity Rolling

SQL-to-Pandas Mental Model

Skill Coverage

FAQ

Ready to Master Pandas?

LDS Pandas Window & Aggregation 25

About this collection

Your Learning Path