# jrjohansson/scientific-python-lectures

Lectures on scientific computing with python, as IPython notebooks.

Repository: https://github.com/jrjohansson/scientific-python-lectures
Canonical: https://ross.abutalabs.com/products/scientific-python-lectures
Language: Jupyter Notebook
License Family: other
Last push: 2026-06-02T22:19:39+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 85, release rhythm 35, longevity 100
- inputs: {"age_days": 5025, "days_push": 92, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3647, forks 1801 (observed 2026-08-28T04:08:12.974718+00:00)

## What it is
A collection of IPython/Jupyter notebook lectures on scientific computing with Python, covering Python programming, NumPy, SciPy, Matplotlib, SymPy, C/Fortran integration, HPC, and revision control. Read-only versions are available online and a compiled PDF of all lectures is provided.

## Use cases
- learn scientific computing with python
- numpy tutorial notebooks
- learn matplotlib plotting
- scipy lecture notes
- introduction to python programming course
- symbolic math with sympy tutorial

## When to choose
- you want a free, notebook-based introduction to the Python scientific stack
- you prefer runnable examples in Jupyter over static docs
- you need course material covering NumPy, SciPy, Matplotlib, and SymPy in one place

## When to avoid
- you need up-to-date material reflecting the latest library APIs
- you want interactive exercises or graded assignments
- you need a maintained software library rather than educational content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, data-visualization, math, developer-tools
- domain: data-science, education, tutorials, programming-languages
- platform: python, cross-platform
- tags: jupyter-notebooks, numpy, scipy, matplotlib, sympy, scientific-computing, lectures

## Member repositories
- jrjohansson/scientific-python-lectures (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:12.974718+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 2026-08-29T18:31:43.751018+00:00, confidence not recorded.
  - readme: https://github.com/jrjohansson/scientific-python-lectures (fetched 2026-08-28T04:08:12.974718+00:00, sha e9602fa0d2b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.