Discovering Movie Magic: An SQL Analysis of IMDB's Top 250 with Python

Want to dive deep into the world of cinema? This project uses SQL analysis of IMDB movies to uncover fascinating insights about the top 250 highest-rated films. Learn how to explore movie data and display results in an understandable way using Python, Quarto, and DuckDB!

Why Analyze IMDB Movies with SQL and Python?

Data analysis can provide insight into trends otherwise missed. This project allows you to:

• Uncover hidden trends: Identify popular genres, directors, and countries featured in top-rated movies.
• Sharpen your SQL skills: Practice writing SQL queries to extract specific information from the movie database.
• Visualize your findings: Display your results as interactive HTML tables for easy exploration.

Setting Up Your Movie Analysis Environment

Ready to start your cinematic data adventure? Here’s how to set up your environment:

1. Clone the repository: Grab the project files from GitHub using:

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

   Copy

2. Create a virtual environment: Isolate project dependencies from your system packages:

   uv venv
   source .venv/bin/activate  # or .venv\Scripts\activate on Windows
   

   Copy

3. Install dependencies: Install the required Python packages using:

   uv pip install -r pyproject.toml
   

   Copy

Running the Movie SQL Analysis

Explore the Quarto notebook and generate the final analysis!

• Render as HTML: Convert the Quarto notebook into an interactive HTML report.

  quarto render notebook.qmd
  

  Copy

• Interactive exploration (Jupyter): Open the notebook in Jupyter for real-time experimentation.

  quarto preview notebook.qmd
  

  Copy

The Heart of the Project: The Movies Database

The analysis relies on a SQLite database named movies.db placed inside the data/ folder. Make sure you have a movies table with the following columns:

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

Features That Make This Project Shine

• SQL-powered analysis: Utilizes the power of SQL queries for efficient data extraction.
• Interactive HTML tables: Presents results in an accessible and engaging format using itables.
• DuckDB integration: Leverages DuckDB for fast and efficient database querying.
• Clear and concise: Easy-to-understand code and documentation for a seamless learning experience.

License

This project is licensed under the MIT License, meaning you're free to use, modify, and distribute it.

Ready to uncover the secrets behind IMDB's top-rated movies? Dive into this Python SQL analysis project and start exploring! Find out why certain movies stand out and get comfortable with interactive IMDB data analysis.