# Kyubyong/numpy_exercises

Numpy exercises.

Repository: https://github.com/Kyubyong/numpy_exercises
Canonical: https://ross.abutalabs.com/products/numpy_exercises
Language: Python
License: MIT
License Family: permissive
Topics: numpy, numpy-exercises
Last push: 2023-05-21T08:47:30+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3574, "days_push": 1200, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1740, forks 578 (observed 2026-08-28T04:05:30.138362+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: data-science, tutorials, education
- platform: python
- tags: numpy, exercises, practice-problems, numerical-computing, jupyter-notebooks

## Member repositories
- Kyubyong/numpy_exercises (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:30.138362+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:30:04.514290+00:00, confidence not recorded.
  - readme: https://github.com/Kyubyong/numpy_exercises (fetched 2026-08-28T04:05:30.138362+00:00, sha 32bc8653280e)
- Data as of 2026-08-30T08:39:29.467469+00:00.