Discovering Movie Magic: A SQL Analysis of Top IMDB Films Using Python and DuckDB

Want to dive deep into the cinematic world's best movies? This project offers a fun and insightful SQL analysis of top-rated IMDB films, using Python and DuckDB. We will explore the secrets hidden within movie data, and you can follow along!

Unveiling Movie Trends using SQL: What You’ll Discover

This project is all about using SQL to analyze IMDB movie data. Here's what makes it engaging:

• SQL-Powered Insights: Extract valuable information about directors, genres, scores, and more with efficient SQL queries.
• Interactive Results: View your analysis in dynamic, sortable, and filterable HTML tables.
• Reproducible Workflow: The entire project is built with Quarto, ensuring easy replication and modification of the analysis.

Think of it as becoming a data-savvy film critic!

Project Highlights: DuckDB, Python, and Interactive Tables

This project leverages powerful tools to bring movie data to life:

• DuckDB: A fast and efficient in-process SQL database, perfect for analytical queries on the movies.db database.
• Python: Used to connect to DuckDB, execute SQL queries, and display results.
• Quarto: Combines code, output, and narrative into a single, easy-to-read document.
• Itables: Display dataframes as interactive HTML tables within the Quarto report.

Get Started: Installation Guide

Ready to start your own IMDB movie analysis with SQL? Here's how to set up the project:

1. Clone the Repository:

   git clone https://github.com/gpenessot/imdb-sql-analysis.git
   cd imdb-sql-analysis
   

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2. Create a Virtual Environment: Using uv package manager for faster performance.

   uv venv
   source .venv/bin/activate # or .venv\Scripts\activate on Windows
   uv pip install -r pyproject.toml
   

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This isolates the project dependencies.

Usage: Running the Analysis

Transform the Quarto notebook into an interactive HTML report:

• Render to HTML:

  quarto render notebook.qmd
  

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• Interactive Preview:

  quarto preview notebook.qmd
  

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This will open the report in your browser.

Dataset Details

The core of this project is the movies.db SQLite database. Put it inside the data/ folder and ensure it contains a movies table with these columns:

• title
• director
• year
• rating
• genres
• runtime
• country
• language
• imdb_score
• imdb_votes
• metacritic_score

License

This project is open-source under the MIT License. Feel free to use, modify, and share it!