This Streamlit-based dashboard provides comprehensive analytics and insights into California's cannabis retail market. The application combines dispensary data with social media sentiment analysis to offer a multi-faceted view of the market landscape.

• Key market metrics and trends
• Overall market health indicators
• Quick navigation to detailed analysis sections

• Year-over-year market growth analysis
• Regional distribution of dispensaries
• License type breakdown by county
• Interactive filters for custom analysis

• Interactive choropleth map of California counties
• Population-adjusted retailer density metrics
• Regional market concentration analysis
• County-level license distribution

• Social media sentiment analysis by region
• Temporal sentiment trends
• Correlation between market density and public sentiment
• County-level sentiment distribution

The dashboard utilizes three main data sources:

• Dispensaries.csv: Contains detailed information about cannabis retailers
• Tweet_Sentiment.csv: Social media sentiment data related to cannabis
• California_County_Boundaries.geojson: Geographic boundaries for visualization

• Python: >= 3.10
• Key Dependencies:
  • Streamlit: Web application framework
  • Pandas: Data manipulation and analysis
  • Plotly: Interactive visualizations
  • Transformers: Sentiment analysis models

1. Clone the repository:

git clone https://github.com/your-username/california-cannabis-analytics.git
cd california-cannabis-analytics

2. Create and activate a virtual environment:

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

3. Install dependencies using Poetry:

poetry install

4. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

5. Run the application:

streamlit run app/Home.py

├── app/
│   ├── Home.py              # Main dashboard entry point
│   ├── pages/               # Dashboard pages
│   ├── plots/               # Visualization components
│   ├── utils/               # Utility functions
│   └── style.css           # Custom styling
├── data/                    # Data files
├── config/                  # Configuration files
└── public/                  # Static assets

The project uses Poetry for dependency management and includes development tools:

• Black: Code formatting
• isort: Import sorting
• flake8: Code linting
• pytest: Testing

Run tests:

poetry run pytest

Format code:

poetry run black .
poetry run isort .

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request