# mdipierro/nlib

The book "Annotated Algorithms in Python" and the nlib.py library

Repository: https://github.com/mdipierro/nlib
Canonical: https://ross.abutalabs.com/products/nlib
Language: Python
License Family: other
Last push: 2026-04-04T15:57:14+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 75, release rhythm 35, longevity 100
- inputs: {"age_days": 4867, "days_push": 151, "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 1338, forks 111 (observed 2026-08-28T04:04:25.792844+00:00)

## What it is
A pure-Python numerical computing library (nlib.py) accompanied by the open book 'Annotated Algorithms in Python', covering linear algebra, root finding, optimization, statistics, and finance without requiring numpy. It serves both as a teaching resource and a lightweight dependency-free toolkit for numerical algorithms.

## Use cases
- learn numerical algorithms in python from scratch
- do linear algebra without numpy
- fit curves with least squares in pure python
- solve equations with newton's method
- compute statistics like mean, correlation, covariance
- fetch and analyze stock price data
- persist a python dictionary to sqlite

## When to choose
- you want a dependency-free pure python numerical library
- you are teaching or learning how numerical algorithms work internally
- you need lightweight math utilities where numpy is unavailable or overkill

## When to avoid
- you need high-performance vectorized numerical computing
- you are building production scientific software - prefer numpy/scipy
- you need a maintained library with a clear license and active development

## Facets
- artifact type: library
- maturity: maintenance
- function: math, data-science, machine-learning
- domain: mathematics, data-science, education, tutorials
- platform: python, cross-platform
- tags: numerical-computing, pure-python, textbook, no-numpy, linear-algebra, statistics, finance, optimization

## Member repositories
- mdipierro/nlib (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:25.792844+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 2026-08-30T04:44:00.344658+00:00, confidence not recorded.
  - readme: https://github.com/mdipierro/nlib (fetched 2026-08-28T04:04:25.792844+00:00, sha 973bab1c25d7)
  - registry_pypi: https://pypi.org/pypi/nlib/json (fetched 2026-08-29T12:03:04.768634+00:00, sha 8963effedd90)
- Data as of 2026-08-30T08:39:29.467469+00:00.