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NBA Analysis Project (DuckDB + Quary w/ Interactive Python Notebook)

This project analyzes NBA data using DuckDB and Quary to transform raw data from .csv files into a structured database ready for analysis. The project also includes an interactive Python Notebook (analysis.ipynb) that provides visual insights into team and player performance metrics.

๐Ÿ“‚ What's in this repo?

This repo contains .csv data, including raw data on NBA players, teams, salaries, and draft information. The raw data is transformed using DuckDB and DBT into a structured database with the following views:

  • stg_players: Player information and statistics
  • team_performance: Team performance metrics
  • player_performance: Player performance metrics
  • player_salary_info: Player salary information
  • team_salary_info: Team salary information
  • player_draft_info: Player draft information
  • player_combine_stats: Player measurements from the draft combine

๐Ÿš€ Opening this project

To explore this project, follow these steps:

  1. Clone the repository
git clone https://github.com/quarylabs/quary_basketball_analysis_duckdb.git
  1. Open the project in Visual Studio Code and install Quary from the extension marketplace

  2. Install the Quary CLI

brew install quarylabs/quary/quary

(Optional) 4. Explore the SQL file documentation (CMD/CTRL+D)

(Optional) 5. Build the database (deploy the models to DuckDB) using the Quary: RUN VSCode command or quary build in the CLI

(Optional) 6. Run the tests against the DuckDB database using Quary: Test VSCode command or quary test in the CLI.

Feel free to fork this project and make your own analysis!

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