# subbarayudu-j/TheAlgorithms-Python

TheAlgorithms/Python

Repository: https://github.com/subbarayudu-j/TheAlgorithms-Python
Canonical: https://ross.abutalabs.com/products/thealgorithms-python
Language: Python
License: MIT
License Family: permissive
Last push: 2024-06-18T14:25:52+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": 2712, "days_push": 806, "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 3549, forks 655 (observed 2026-08-28T04:08:09.489358+00:00)

## What it is
A collection of algorithms implemented in Python for educational purposes, covering sorting, searching, and other classic algorithms with complexity explanations. It is a fork of TheAlgorithms/Python intended for learning and demonstration, not production use.

## Use cases
- learn how sorting algorithms work in python
- study algorithm implementations for coding interviews
- find example code for bubble sort and merge sort
- understand time complexity of classic algorithms
- reference python implementations of data structures and algorithms

## When to choose
- you are learning algorithms and want readable python examples
- you are preparing for technical interviews and want to study implementations
- you want annotated code with complexity analysis

## When to avoid
- you need performant, production-ready algorithm implementations
- you should use the python standard library or optimized packages instead

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: education, tutorials
- platform: python
- tags: algorithms, sorting, data-structures, educational, interview-preparation

## Member repositories
- subbarayudu-j/TheAlgorithms-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.489358+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 2026-08-29T18:34:22.717994+00:00, confidence not recorded.
  - readme: https://github.com/subbarayudu-j/TheAlgorithms-Python (fetched 2026-08-28T04:08:09.489358+00:00, sha d8febfe86ead)
- Data as of 2026-08-30T08:39:29.467469+00:00.