🏀 NBA Player Directory

A modern, interactive web application built with Streamlit for exploring NBA player data. Browse through hundreds of players with advanced filtering, sorting, and search capabilities.

✨ Features

🔍 Advanced Search & Filtering

• Text Search: Search across player names, positions, schools, and countries
• Position Filter: Multi-select dropdown for specific positions (Guard, Forward, Center, etc.)
• Country Filter: Filter players by their country of origin
• Draft Year Range: Interactive slider to filter by draft year (2003-2022)

📊 Data Visualization

• Card Layout: Beautiful player cards with photos and detailed information
• Responsive Grid: Clean 3-column layout that adapts to your screen
• Player Images: Individual player photos with fallback placeholders
• Comprehensive Stats: Position, height, weight, age, country, school, and draft information

⚙️ Customization Options

• Flexible Sorting: Sort by name, position, country, draft year, height, weight, or age
• Pagination Controls: Adjustable page sizes (6, 9, 12, 24, or 48 players per page)
• Raw Data View: Expandable table view for detailed data analysis
• Real-time Updates: Instant filtering and search results

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository

   git clone https://github.com/Geronimo-Basso/github-coding-agent-demo.git
   cd github-coding-agent-demo

2. Create and activate virtual environment

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   streamlit run app.py

5. Open your browser The app will automatically open at http://localhost:8501

📁 Project Structure

github-coding-agent-demo/
├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── README.md                # This file
└── nba-mock-data/           # Data directory
    ├── players.csv          # Player dataset (548 players)
    └── img/                 # Player images directory
        ├── 1630173.png      # Individual player photos
        ├── 203500.png
        └── ...

📊 Data Schema

The application uses a CSV dataset with the following fields:

Field	Description	Example
playerid	Unique player identifier	1630173
fname	First name	Precious
lname	Last name	Achiuwa
position	Playing position	Forward
height	Player height	6-8
weight	Player weight (lbs)	225
birthday	Birth date	1999-09-19
country	Country of origin	Nigeria
school	College/University	Memphis
draft_year	NBA draft year	2020
draft_round	Draft round	1
draft_number	Draft pick number	20

🛠️ Technical Details

Dependencies

• Streamlit >=1.38.0 - Web app framework
• Pandas >=2.2.0 - Data manipulation and analysis
• Python >=3.8 - Programming language

Key Features Implementation

• Caching: Uses @st.cache_data decorator for optimal performance
• Error Handling: Graceful fallbacks for missing images and data
• Responsive Design: Mobile-friendly layout with flexible columns
• Data Validation: Automatic data cleaning and normalization

🎯 Usage Examples

Search for Specific Players

• Type "LeBron" in the search box to find LeBron James
• Search "Duke" to find all players from Duke University
• Search "Canada" to find all Canadian players

Filter by Multiple Criteria

1. Select "Guard" from the Position filter
2. Choose "USA" from the Country filter
3. Adjust the Draft Year slider to 2015-2020
4. Sort by "height" to see the tallest guards

Browse and Explore

• Use pagination to browse through all 548 players
• Click the "Raw table" expander to see the complete dataset
• Experiment with different sort orders to discover patterns

🔄 Future Enhancements

• Team Filtering: Add NBA team-based filters
• Advanced Statistics: Include performance metrics and career stats
• Data Export: Download filtered datasets as CSV
• Comparison Tool: Side-by-side player comparisons
• Data Caching: Implement advanced caching strategies
• API Integration: Real-time NBA data updates
• Unit Tests: Comprehensive test coverage for data utilities

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is open source and available under the MIT License.

🙋‍♂️ Support

If you have any questions or run into issues:

• Check the Issues page
• Create a new issue with detailed information about your problem
• Include your Python version and operating system

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Built with ❤️ using Streamlit and Python

Generated with help from GitHub Copilot Coding Agent