back to home

rougier / numpy-100

100 numpy exercises (with solutions)

13,816 stars
6,580 forks
57 issues
Python

AI Architecture Analysis

This repository is indexed by RepoMind. By analyzing rougier/numpy-100 in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.

Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.

Embed this Badge

Showcase RepoMind's analysis directly in your repository's README.

[![Analyzed by RepoMind](https://img.shields.io/badge/Analyzed%20by-RepoMind-4F46E5?style=for-the-badge)](https://repomind-ai.vercel.app/repo/rougier/numpy-100)
Preview:Analyzed by RepoMind

Repository Summary (README)

Preview

100 numpy exercises

Binder

This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation. I've also created some problems myself to reach the 100 limit. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. For extended exercises, make sure to read From Python to NumPy.

Test them on Binder
Read them on GitHub

Note: markdown and ipython notebook are created programmatically from the source data in source/exercises.ktx. To modify the content of these files, please change the text in the source and run the generators.py module with a python interpreter with the libraries under requirements.txt installed.

The keyed text format (ktx) is a minimal human readable key-values to store text (markdown or others) indexed by keys.

This work is licensed under the MIT license.
DOI

Variants in Other Languages