davecom/ClassicComputerScienceProblemsInPython resource
Source Code for the Book Classic Computer Science Problems in Python observed · 2026-08-28
Health v2 · maintenance only
70/100
- Activity 83
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3018
- days_rel: n/a
- days_push: 102
- n_releases_24m: 0
Adoption not part of the score
1121 stars · 408 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
learning-resource · maturity stable
developer-tools interpreter machine-learning search-engine graphics simulation education programming-languages computer-vision tutorials python cross-platform 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
4 sources
- readme: https://github.com/davecom/ClassicComputerScienceProblemsInPython · fetched 2026-08-28 · d6fdfdd8df61
- homepage: https://www.manning.com/books/classic-computer-science-problems-in-python?a_aid=oaksnow&a_bid=d326fe0b · fetched 2026-08-29 · f99c34997f6c
- site_page: https://www.manning.com/liveproject/how-to-think-about-scheduling-tasks · fetched 2026-08-29 · a3fc08779a93
- site_page: https://www.manning.com/faq · fetched 2026-08-29 · 9e27304d7757
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| davecom/ClassicComputerScienceProblemsInPython | main | 70 |
For agents
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Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem