Kyubyong/numpy_exercises resource
Numpy exercises. observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3574
- days_rel: n/a
- days_push: 1200
- n_releases_24m: 0
Adoption not part of the score
1740 stars · 578 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of simple Python exercises covering NumPy's API, with one or a few lines of solution per problem. It is organized by NumPy documentation sections such as array creation, linear algebra, random sampling, and statistics.
Use cases
- practice numpy exercises
- learn numpy by solving problems
- prepare for a python data science interview
- find example usage of numpy functions
- teach a numpy workshop
- review numpy array manipulation and linear algebra
When to choose
- you want hands-on practice problems for NumPy
- you are learning numerical computing in Python and want short exercises per function
- you need ready-made exercise material for teaching NumPy
When to avoid
- you need a NumPy tutorial with explanations rather than exercises
- you want exercises for pandas, PyTorch, or other libraries
- you need production code or a software tool
Facets
learning-resource · maturity maintenance
developer-tools data-science tutorials education python numpy exercises practice-problems numerical-computing jupyter-notebooks
1 source
- readme: https://github.com/Kyubyong/numpy_exercises · fetched 2026-08-28 · 32bc8653280e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Kyubyong/numpy_exercises | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="Kyubyong/numpy_exercises")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem