# prakhar1989/Algorithms

:computer: Data Structures and Algorithms in Python

Repository: https://github.com/prakhar1989/Algorithms
Canonical: https://ross.abutalabs.com/products/prakhar1989-algorithms
Language: Python
License: WTFPL
License Family: permissive
Last push: 2021-08-03T04:59:43+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": 5013, "days_push": 1856, "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 3088, forks 819 (observed 2026-08-28T04:07:42.192691+00:00)

## What it is
A collection of classic data structure and algorithm implementations in Python, covering graphs, heaps, sorting, dynamic programming, and number theory. It serves as an educational reference with accompanying tests rather than a production library.

## Use cases
- learn data structures and algorithms in python
- study graph algorithms like dijkstra and kruskal with example code
- prepare for coding interviews with python algorithm implementations
- reference implementations of heaps, tries, and union-find
- understand dynamic programming problems like knapsack and longest common subsequence

## When to choose
- you want readable, educational Python implementations of classic algorithms
- you are studying for interviews or coursework and want worked examples
- you need a quick reference for graph, heap, or number theory algorithms

## When to avoid
- you need a production-grade, performance-optimized algorithm library
- you want a maintained package with releases and dependency support
- you need algorithms in a language other than Python

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages
- platform: python
- tags: data-structures, algorithms, interview-prep, educational

## Member repositories
- prakhar1989/Algorithms (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:42.192691+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 2026-08-30T07:27:30.968234+00:00, confidence not recorded.
  - readme: https://github.com/prakhar1989/Algorithms (fetched 2026-08-28T04:07:42.192691+00:00, sha c32ca478e9a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.