{"id":18330102,"url":"https://github.com/dmarks84/ind_project_movie-database-sqlite","last_synced_at":"2025-10-21T07:19:32.334Z","repository":{"id":224823767,"uuid":"764325053","full_name":"dmarks84/Ind_Project_Movie-Database-SQLite","owner":"dmarks84","description":"Independent Project - I joined and manipulated data from disparate tables of movie information using Python \u0026 SQLite; defined schema, created tables/views, queried data, etc. Utilized CTE's, Window Functions, and other DDL, DQL, DML, and DCL scripts.","archived":false,"fork":false,"pushed_at":"2024-02-27T22:13:56.000Z","size":2801,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T10:32:07.376Z","etag":null,"topics":["advanced-sql","cte","databases","dcl","ddl","dml","dql","group-by","joins","python","query","sql","sqlite","tables","views","window-functions"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dmarks84.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":"2024-02-27T21:52:25.000Z","updated_at":"2024-02-27T21:57:50.000Z","dependencies_parsed_at":"2024-02-27T22:59:08.241Z","dependency_job_id":null,"html_url":"https://github.com/dmarks84/Ind_Project_Movie-Database-SQLite","commit_stats":null,"previous_names":["dmarks84/ind_project_movie-database-sqlite"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FInd_Project_Movie-Database-SQLite","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FInd_Project_Movie-Database-SQLite/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FInd_Project_Movie-Database-SQLite/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FInd_Project_Movie-Database-SQLite/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dmarks84","download_url":"https://codeload.github.com/dmarks84/Ind_Project_Movie-Database-SQLite/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248078944,"owners_count":21044205,"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":["advanced-sql","cte","databases","dcl","ddl","dml","dql","group-by","joins","python","query","sql","sqlite","tables","views","window-functions"],"created_at":"2024-11-05T19:20:27.545Z","updated_at":"2025-10-21T07:19:27.273Z","avatar_url":"https://github.com/dmarks84.png","language":"Python","readme":"# Advanced SQL Movie Database\n\n## Screenshot\n![Example_TopDirectors](https://github.com/dmarks84/Ind_Project_Movie-Database-SQLite/blob/main/screenshot.png?raw=true)\n\n## Summary\nI utilized two datasets on movies from Kaggle, one on Netflix's shows and the other on the top 1000 movies from IMDB.  I initially loaded these tables from CSV as they were received into a SQLite Database. I wrote several scripts, utilizing sqlite3 in python, to create new tables that better formatted the data (changing the datatypes) and creating primary keys.  I also create other tables to contain repetitive instances of films' directors, ratings, and genres.  The genre attribute represented a many-to-many relationship, so I created a linking table with to foreign keys.  Most actions for querying and inserting data into the tables was accomplished with custom functions I wrote and imported/called when needed.  The initial result in terms of the core data was a new, sleek table with id reference to related tables.\n\nUsing this core table, I created a number of queries and saved them as views related to meaningful questions.  The questions I investigated related to the most successful directors (again, this is limited to the most successful directors whose movies made it into Netflix at the time the data was collected).  I developed queries utilizing GROUP BY, CTEs, WINDOW FUNCTIONS, and other aggregate functions to answer questions like, \"What is the average gross at the box office for each director as a running average/total for each successive movie they made?\" or \"What is the average IMDB score for each director?\"  The main answer I sought was to see which director, who had at least two films in the database, had the highest average gross at the box office for their films (A: Peter Jackson, as highlighted above in the screenshot of the SQLite database).\n\n## Skills (Developed \u0026 Applied)\nProgramming, Python, SQL, SQLite, queries, commands, DDL, DML, DCL, DQL, Window Functions, Aggregate Functions, GROUP BY, CTEs\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Find_project_movie-database-sqlite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmarks84%2Find_project_movie-database-sqlite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Find_project_movie-database-sqlite/lists"}