# davecom/ClassicComputerScienceProblemsInPython

Source Code for the Book Classic Computer Science Problems in Python

Repository: https://github.com/davecom/ClassicComputerScienceProblemsInPython
Canonical: https://ross.abutalabs.com/products/classiccomputerscienceproblemsinpython
Homepage: https://www.manning.com/books/classic-computer-science-problems-in-python?a_aid=oaksnow&a_bid=d326fe0b
Language: Python
License: Apache-2.0
License Family: permissive
Topics: manning, computer-science, python, python37, algorithms, book
Last push: 2026-05-23T18:41:29+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 83, release rhythm 35, longevity 100
- inputs: {"age_days": 3018, "days_push": 102, "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 1121, forks 408 (observed 2026-08-28T04:03:39.945619+00:00)

## What it is
Source code repository accompanying the book 'Classic Computer Computer Science Problems in Python' by David Kopec, containing chapter-by-chapter Python 3.7 implementations of classic computer science problems such as search, constraint-satisfaction, genetic algorithms, neural networks, and adversarial game-playing. It is a learning companion rather than a production library, with each code listing in the book mapped to a corresponding file in the repository.

## Use cases
- learning classic computer science algorithms in Python
- study companion for the Classic Computer Science Problems book
- example implementations of constraint-satisfaction problems
- reference code for genetic algorithms and neural networks from scratch
- teaching materials for introductory algorithms and data structures
- practicing Python 3.7 features like data classes and type hints

## When to choose
- you are reading the book and want the matching source code organized by chapter
- you want small, readable, from-scratch implementations of classic algorithms for learning
- you are an educator looking for clean example code for teaching CS concepts
- you want to see Python 3.7 features like data classes and advanced type hints applied

## When to avoid
- you need production-ready, optimized, or maintained algorithm libraries
- you want a pip-installable package with a stable API
- you are not following the book and need standalone, documented algorithm implementations
- you require support for Python versions earlier than 3.7

## Facets
- artifact type: learning-resource
- maturity: stable
- function: developer-tools, interpreter, machine-learning, search-engine, graphics, simulation
- domain: education, programming-languages, computer-vision, tutorials
- platform: python, cross-platform
- tags: book-source-code, computer-science, algorithms, python37, manning, educational, data-structures, constraint-satisfaction, genetic-algorithms, neural-networks, adversarial-search, k-means, tsp, classic-problems, natural-language-processing

## Member repositories
- davecom/ClassicComputerScienceProblemsInPython (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:39.945619+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 2026-08-30T06:41:00.041674+00:00, confidence not recorded.
  - readme: https://github.com/davecom/ClassicComputerScienceProblemsInPython (fetched 2026-08-28T04:03:39.945619+00:00, sha d6fdfdd8df61)
  - homepage: https://www.manning.com/books/classic-computer-science-problems-in-python?a_aid=oaksnow&a_bid=d326fe0b (fetched 2026-08-29T12:44:57.800776+00:00, sha f99c34997f6c)
  - site_page: https://www.manning.com/liveproject/how-to-think-about-scheduling-tasks (fetched 2026-08-29T12:44:57.803527+00:00, sha a3fc08779a93)
  - site_page: https://www.manning.com/faq (fetched 2026-08-29T12:44:57.805530+00:00, sha 9e27304d7757)
- Data as of 2026-08-30T08:39:29.467469+00:00.