https://github.com/dmarks84/ind_project_movie-database-sqlite

Independent Project - I joined and manipulated data from disparate tables of movie information using Python & SQLite; defined schema, created tables/views, queried data, etc. Utilized CTE's, Window Functions, and other DDL, DQL, DML, and DCL scripts.
https://github.com/dmarks84/ind_project_movie-database-sqlite

advanced-sql cte databases dcl ddl dml dql group-by joins python query sql sqlite tables views window-functions

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Independent Project - I joined and manipulated data from disparate tables of movie information using Python & SQLite; defined schema, created tables/views, queried data, etc. Utilized CTE's, Window Functions, and other DDL, DQL, DML, and DCL scripts.

• Host: GitHub
• URL: https://github.com/dmarks84/ind_project_movie-database-sqlite
• Owner: dmarks84
• License: bsd-3-clause
• Created: 2024-02-27T21:52:25.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-02-27T22:13:56.000Z (8 months ago)
• Last Synced: 2024-02-28T22:49:07.551Z (8 months ago)
• Topics: advanced-sql, cte, databases, dcl, ddl, dml, dql, group-by, joins, python, query, sql, sqlite, tables, views, window-functions
• Language: Python
• Homepage:
• Size: 2.67 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Advanced SQL Movie Database

## Screenshot

![Example_TopDirectors](https://github.com/dmarks84/Ind_Project_Movie-Database-SQLite/blob/main/screenshot.png?raw=true)

## Summary

I 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.

Using 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).

## Skills (Developed & Applied)

Programming, Python, SQL, SQLite, queries, commands, DDL, DML, DCL, DQL, Window Functions, Aggregate Functions, GROUP BY, CTEs