{"id":31988449,"url":"https://github.com/tomaslink/algorithmic","last_synced_at":"2025-10-15T09:43:13.223Z","repository":{"id":224989383,"uuid":"764701948","full_name":"tomaslink/algorithmic","owner":"tomaslink","description":"Collection of documented, well tested, clean and efficient pure python algorithms implementations to solve computational problems.","archived":false,"fork":false,"pushed_at":"2024-03-12T02:48:53.000Z","size":56,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-03-12T19:26:00.197Z","etag":null,"topics":["algorithmic-efficiency","algorithms","big-o","clean-code","complexity-analysis","cracking-the-coding-interview","data-structures","interview-practice","interview-questions","problem-solving","python","recursion"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tomaslink.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-02-28T15:02:19.000Z","updated_at":"2024-03-12T19:26:00.197Z","dependencies_parsed_at":"2024-03-10T19:25:57.374Z","dependency_job_id":"63d0c881-3fa3-4e05-befd-acda60fd62fd","html_url":"https://github.com/tomaslink/algorithmic","commit_stats":null,"previous_names":["tomaslink/algorithmic"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tomaslink/algorithmic","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tomaslink%2Falgorithmic","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tomaslink%2Falgorithmic/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tomaslink%2Falgorithmic/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tomaslink%2Falgorithmic/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tomaslink","download_url":"https://codeload.github.com/tomaslink/algorithmic/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tomaslink%2Falgorithmic/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279067580,"owners_count":26096347,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-15T02:00:07.814Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithmic-efficiency","algorithms","big-o","clean-code","complexity-analysis","cracking-the-coding-interview","data-structures","interview-practice","interview-questions","problem-solving","python","recursion"],"created_at":"2025-10-15T09:43:08.762Z","updated_at":"2025-10-15T09:43:13.212Z","avatar_url":"https://github.com/tomaslink.png","language":"Python","readme":"\u003ch1 align=\"center\" style=\"border-bottom: none;\"\u003e algorithmic \u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca\u003e\n    \u003cimg alt=\"Coverage\" src=\"https://codecov.io/gh/tomaslink/algorithmic/graph/badge.svg?token=U8IXA9B0VD\"\u003e\n    \u003cimg alt=\"Python\" src=\"https://img.shields.io/badge/python-3.8 | 3.9 | 3.10 | 3.11 | 3.12 -blue.svg\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n[Cracking the Coding Interview, 6th Edition]: https://www.amazon.com/gp/product/0984782850/ref=as_li_ss_tl?ie=UTF8\u0026tag=care01-20\u0026linkCode=as2\u0026camp=217145\u0026creative098478285050tiveASIN=0984782850\n[Zen of Python]: https://peps.python.org/pep-0020/\n\n[Arrays and Strings]: docs/arrays-and-strings.md\n[Linked Lists]: docs/linked-lists.md\n[Stacks and Queues]: docs/stacks-and-queues.md\n\nCollection of pure python algorithm implementations to solve computational problems.\n\nThe goal is for these implementations to be:\n- Elegant (clean, short, easy to understand).\n- Theoretically efficient.\n- Well tested.\n- Well documented.\n\n## List of problems\n   - [Arrays and Strings]\n   - [Linked Lists]\n   - [Stacks and Queues]\n\n**References**:\n- The book [Cracking the Coding Interview, 6th Edition].\n\n\n## Design principles\n\nSome design principles taken into account (from the [Zen of Python]):\n  - Beautiful is better than ugly.\n  - Explicit is better than implicit.\n  - Simple is better than complex.\n  - Complex is better than complicated.\n  - Flat is better than nested.\n  - Sparse is better than dense.\n  - Readability counts.\n  - Errors should never pass silently.\n  - Unless explicitly silenced.\n  - There should be one-- and preferably only one --obvious way to do it.\n  - If the implementation is hard to explain, it's a bad idea.\n  - If the implementation is easy to explain, it may be a good idea.","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftomaslink%2Falgorithmic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftomaslink%2Falgorithmic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftomaslink%2Falgorithmic/lists"}