{"id":16507596,"url":"https://github.com/chmp/pyconde23","last_synced_at":"2025-06-22T09:37:32.375Z","repository":{"id":192132975,"uuid":"622641570","full_name":"chmp/PyConDE23","owner":"chmp","description":"Material for \"Pragmatic ways of using Rust in your data project\" @ PyCon.DE 2023","archived":false,"fork":false,"pushed_at":"2023-04-16T12:00:38.000Z","size":557,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T14:25:01.432Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","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/chmp.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":"License.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-04-02T17:51:21.000Z","updated_at":"2023-11-25T18:24:20.000Z","dependencies_parsed_at":"2023-09-02T20:15:01.333Z","dependency_job_id":null,"html_url":"https://github.com/chmp/PyConDE23","commit_stats":null,"previous_names":["chmp/pyconde23"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/chmp/PyConDE23","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chmp%2FPyConDE23","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chmp%2FPyConDE23/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chmp%2FPyConDE23/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chmp%2FPyConDE23/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chmp","download_url":"https://codeload.github.com/chmp/PyConDE23/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chmp%2FPyConDE23/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261272208,"owners_count":23133787,"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","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":[],"created_at":"2024-10-11T15:29:15.481Z","updated_at":"2025-06-22T09:37:27.364Z","avatar_url":"https://github.com/chmp.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pragmatic ways of using Rust in your data project @ PyCon.DE 2023\n\nWriting efficient data pipelines in Python can be tricky. The standard\nrecommendation is to use vectorized functions implemented in Numpy, Pandas, or\nthe like. However, what to do, when the processing task does not fit these\nlibraries? Using plain Python for processing can result in lacking performance,\nin particular when handling large data sets.\n\nRust is a modern, performance-oriented programming language that is already\nwidely used by the Python community. Augmenting data processing steps with Rust\ncan result in substantial speed ups. In this talk will present strategies of\nusing Rust in a larger Python data processing pipeline with a particular focus\non pragmatism and minimizing integration efforts.\n\nContents:\n\n1. [Slides](Slides.pdf)\n2. IO Patterns\n   - [Rust](01_io_patterns)\n   - [Python](01_io_pattern.ipynb)\n3. PDF Parser\n   - [Rust](02_pdf_parser)\n   - [Python](02_pdf_parser.ipynb)\n4. JSON to Arrow\n   - [Rust](03_json_to_arrow)\n   - [Python](03_json_to_arrow.ipynb)\n5. [Summary of results](04_summary.ipynb)\n\n## Datasets\n\nThe \"Spotify Million Playlist Dataset\" was obtained [here][spotify-dataset]. In\naccordance with the [license][spotify-license] the data processing code is\nlicensed under the MIT license.\n\n[spotify-dataset]: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge\n[spotify-license]: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/challenge_rules\n\n## License\n\n```\nThe MIT License (MIT)\nCopyright (c) 2023 Christopher Prohm\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the\nSoftware is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\nFROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\nDEALINGS IN THE SOFTWARE.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchmp%2Fpyconde23","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchmp%2Fpyconde23","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchmp%2Fpyconde23/lists"}