How is this different from random pandas tutorials?

**Every problem is hand-picked and ordered.** You face exactly one new pandas pattern per slot — *no random tutorial jumping.* The 50-problem journey covers **every pattern** that appears in pandas data-science interviews at top companies.

Do I need to know SQL first?

**No. LDS Python 50 is self-contained.** If you also know SQL, you'll recognize parallels (**merge ≈ JOIN**, **groupby ≈ GROUP BY**, **qcut ≈ NTILE**) — *but they're explained from scratch.*

**pandas 2.x.** All reference solutions use modern idioms (named aggregation, `.dt` accessor, indicator merges). Solutions also run **in your browser via Pyodide** — *no local setup needed.*

How long does it take to complete all 50?

**Most users complete in 3-4 weeks** solving 2 problems per day. Total estimated time is **~22 hours** of focused practice — *slightly more than the SQL 50 because pandas syntax has more surface area.*

How does this compare to DataLemur, StrataScratch, or LeetCode Python?

LeetCode is built for algorithms. DataLemur and StrataScratch include Python, but Python is a smaller share of either catalog. **What LDS Python 50 gives you that they don't:** - **A 5-stage pandas curriculum** — every stage adds one new pattern, no random tutorial jumping - **Industry-grade multi-table schemas** (retail, social, fintech, healthcare) — the same shape pandas pipelines hit in production - **Full pandas + numpy + scipy + sklearn in your browser** via Pyodide — no install, no Conda, no virtualenv - **Built-in AI Mentor** for guided hints - **Stage 1 is free** — 10 problems, no signup *If you want algorithmic Python, go to LeetCode. If you want pandas the way it's actually used in data roles, this is the most cohesive path on the market.*

Curated Collection

Updated · May 2026

LDS Python 50

A 5-stage pandas progression from boolean indexing through groupby + rolling windows to staff-level multi-table scorecards, structured across 15 production-grade schemas.

Problems

Rounds

Datasets

~22h

Time

0/50Solved

About this collection

A 5-stage pandas progression from boolean indexing through groupby + rolling windows to staff-level multi-table scorecards, structured across 15 production-grade schemas — payments, lodging, streaming, social — so the same pattern repeats across different domains until it sticks. The Python companion to LDS SQL 50.

Your Learning Path

5 stages — each building on the last.

Stage 1: Foundation

Locked

Boolean indexing, .loc, sort_values, column selection — every pandas pipeline starts here.

10 problems ~3h

Stage 1: Foundation

Locked

Boolean indexing, .loc, sort_values, column selection — every pandas pipeline starts here.

10 problems ~3h

Stage 2: Merging & Joining

Locked

pd.merge — inner, left, multi-key, suffix collisions, and the anti-merge with indicator.

The heart of relational pandas.

10 problems ~4h

Stage 2: Merging & Joining

Locked

pd.merge — inner, left, multi-key, suffix collisions, and the anti-merge with indicator.

The heart of relational pandas.

10 problems ~4h

Stage 3: Aggregation & Window

Locked

groupby + named agg, rank, cumsum, shift, rolling, qcut, pivot_table. Where raw rows turn into business insights — and where naive pandas hits the for-loop trap.

12 problems ~6h

Stage 3: Aggregation & Window

Locked

groupby + named agg, rank, cumsum, shift, rolling, qcut, pivot_table. Where raw rows turn into business insights — and where naive pandas hits the for-loop trap.

12 problems ~6h

Stage 4: Real-World Transformations

Locked

np.select classification, .dt accessor, date arithmetic, multi-column derivation, and pivot reshapes. The production-grade transformations every real pandas pipeline ends up doing.

10 problems ~5h

Stage 4: Real-World Transformations

Locked

np.select classification, .dt accessor, date arithmetic, multi-column derivation, and pivot reshapes. The production-grade transformations every real pandas pipeline ends up doing.

10 problems ~5h

Stage 5: Interview Ready

Locked

Staff-level multi-table pandas pipelines. 4–5 tables stitched with merges + groupby aggregation + weighted composite scores + np.select tier classification — the shape of pandas code a senior IC actually ships.

8 problems ~4h

Stage 5: Interview Ready

Locked

8 problems ~4h

Start solving — Stage 1

Why These 50?

Hand-curated for maximum interview ROI.

A Pandas Curriculum, Not Random Tutorials

Five stages of ten problems, each adding exactly one new pandas pattern. From read_csv to multi-frame merges to rolling aggregations — every step builds on the last, no tutorial-jumping.

Industry-Grade Multi-Table Data

Every problem runs on the same kind of 8–15 table production schemas pandas pipelines actually hit — payments, lodging, streaming, social. You write the shape of code a real DS or analytics engineer writes.

Full Scientific Stack in Your Browser

pandas, numpy, scipy, sklearn — all run in your browser via Pyodide. No install, no Conda, no virtualenv. Hit Run and a t-test or a linear regression fits in under a second.

Skill Coverage

How the 50 problems distribute across pandas topics.

Boolean Filtering & Selection

Merging & Joining

Window Operations

Aggregation

Reshaping (Pivot)

Classification (np.select / apply)

Date & Time

Cleaning & Normalization

Multi-Table Scorecards

FAQ

The first 10 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.

Ready to Master Pandas?

Start with Stage 1 — graded instantly in your browser.

Start Problem 1 Browse All Problems

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LDS Python 50

About this collection

Your Learning Path

Why These 50?

A Pandas Curriculum, Not Random Tutorials

Industry-Grade Multi-Table Data

Full Scientific Stack in Your Browser

Skill Coverage

FAQ

Ready to Master Pandas?

LDS Python 50 — 50 Curated Python Problems

Problems included in LDS Python 50

LDS Python 50

About this collection

Your Learning Path