{"id":22085315,"url":"https://github.com/antrikshy/personalmovieanalysis","last_synced_at":"2026-05-07T11:35:10.378Z","repository":{"id":79880564,"uuid":"497427834","full_name":"Antrikshy/PersonalMovieAnalysis","owner":"Antrikshy","description":"Finds interesting patterns in an IMDb ratings export; written as a Jupyter notebook, viz using Seaborn","archived":false,"fork":false,"pushed_at":"2022-07-31T22:13:52.000Z","size":4,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-23T21:47:05.656Z","etag":null,"topics":["data-visualization","imdb","jupyter-notebook","movie-ratings","pandas","python","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Antrikshy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"publiccode":null,"codemeta":null}},"created_at":"2022-05-28T21:06:42.000Z","updated_at":"2022-05-28T21:08:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"aea5addf-214c-45ce-9a4a-0430363fda8b","html_url":"https://github.com/Antrikshy/PersonalMovieAnalysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Antrikshy/PersonalMovieAnalysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antrikshy%2FPersonalMovieAnalysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antrikshy%2FPersonalMovieAnalysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antrikshy%2FPersonalMovieAnalysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antrikshy%2FPersonalMovieAnalysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Antrikshy","download_url":"https://codeload.github.com/Antrikshy/PersonalMovieAnalysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antrikshy%2FPersonalMovieAnalysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32735331,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T02:14:30.463Z","status":"ssl_error","status_checked_at":"2026-05-07T02:14:29.405Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["data-visualization","imdb","jupyter-notebook","movie-ratings","pandas","python","seaborn"],"created_at":"2024-12-01T01:13:17.715Z","updated_at":"2026-05-07T11:35:10.373Z","avatar_url":"https://github.com/Antrikshy.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"PersonalMovieAnalysis\n=====================\n\nI have a habit of rating every single movie I watch on IMDb. This repo helps me analyze my IMDb ratings exports while cross-referencing [IMDb's datasets](http://imdb.com/interfaces). You can run it too!\n\nSee my detailed write up, with more beginner-friendly instructions for running it on your own data at [film.Antrikshy](http://antrikshy.com/film/analyzing-decades-worth-my-imdb-movie-ratings).\n\n## Dependencies\n\n### Packages\n\nAt this time, running this notebook requires:\n1. Jupyter\n2. Pandas\n3. Seaborn\n\n(and all transitive dependencies)\n\n### Others\n\nIn addition to packages, the notebook assumes the presence of:\n1. An export of a user's ratings.csv, as [generated](https://help.imdb.com/article/imdb/track-movies-tv/ratings-faq/G67Y87TFYYP6TWAV) by IMDb.\n2. rackfocus_out.db, as generated by [Rackfocus](https://github.com/Antrikshy/Rackfocus) using [IMDb datasets](http://imdb.com/interfaces).\n\nBoth files should be placed in the top-level directory as cloned from this repo.\n\n## Executing\n\nMy favorite way to run Jupyter notebooks is using VS Code's [built-in notebook support](https://code.visualstudio.com/docs/datascience/jupyter-notebooks).\n\nCurrently, this repo contains one notebook - Analysis.ipynb. In the presence of dependencies listed above, it can be run top-to-bottom as is.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantrikshy%2Fpersonalmovieanalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fantrikshy%2Fpersonalmovieanalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantrikshy%2Fpersonalmovieanalysis/lists"}