NumPy Practice Problems

NumPy is one of the most frequently tested skills in data analyst, data engineer and data science interviews — Vectorised array computing and broadcasting. Whether you're preparing for a timed technical screen or a take-home assignment, being able to write correct, efficient numpy under pressure is often what separates candidates who move forward from those who get filtered out early. That's exactly what this page is built for: a permanent, focused practice ground for numpy, not just another filter on a mixed list.

DataVix's NumPy problem set is organised into three difficulty tiers — Easy, Medium and Hard — so you can follow a structured practice roadmap instead of solving questions at random. New numpy problems across Easy, Medium and Hard difficulty are being added to this roadmap. Every problem runs entirely in your browser with instant feedback, hints you can reveal one at a time, and — for enrolled students — a fully worked official solution, so you always know not just whether your answer was right, but why.

Solving NumPy problems repeatedly, rather than only reading about numpy, is what actually moves the needle on interview performance: interviewers are evaluating how you think under pressure, not just whether you can recite syntax. Our questions are modeled on real interview patterns reported at companies including Amazon, Flipkart, Google, Meta, Microsoft, Swiggy, so the practice you put in here transfers directly to the kind of prompts you'll actually be asked. Each problem also lists the topics and companies it's tagged with, so you can drill a specific weak spot — joins, window functions, string manipulation, whatever it may be — instead of solving everything in order.

Use the search, difficulty, topic and sort controls below to build your own numpy practice session, or work straight down the list from Easy to Hard. New problems are added regularly, and this page updates automatically the moment they go live — no need to keep checking a separate list.