The Solar Challenge Dashboard is a professional, data-driven web application built with Streamlit for exploring climate patterns in Benin, Sierra Leone, and Togo. It provides comprehensive analysis of temperature and wind speed data to support renewable energy planning decisions.

• Multi-Country Comparison - Analyze climate data across three West African nations
• Interactive Visualizations - Dynamic time series, box plots, and statistical charts
• Flexible Filtering - Filter by country, date range, and metric
• Statistical Analysis - Summary statistics, top records, and distribution analysis
• Data Export - Download filtered datasets in CSV format
• Smart Data Loading - Automatic fallback to Google Drive if local files are missing

• Line charts showing temperature and wind speed trends over time
• Interactive hover details and zoom capabilities
• Country-wise color coding for easy comparison
• Key metrics dashboard (average, max, min values)

• Box plots for distribution analysis
• Summary statistics by country (mean, median, std dev, min/max)
• Color-coded tables with gradient highlighting
• Outlier detection and visualization

• Configurable top-N records display (5-50 records)
• Highest and lowest value comparisons
• Visual bar charts for extreme values
• Date and country attribution

• Complete dataset preview
• Data completeness metrics
• One-click CSV export functionality
• Real-time record counts

┌─────────────────────────────────────────────────────────┐
│  ☀️ Solar Challenge Dashboard                          │
│  Interactive Analysis of Climate Data                   │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  📈 Time Series  │  📊 Statistics  │  🏆 Top Records  │
│                                                         │
│  [Interactive Plotly Charts]                            │
│  [Real-time Filtering]                                  │
│  [Export Options]                                       │
└─────────────────────────────────────────────────────────┘

• 📍 Country Selection - Multi-select dropdown
• 📊 Metric Selection - Temperature or Wind Speed
• 📅 Date Range Picker - Custom date filtering
• 🔢 Top-N Slider - Adjustable record display

• Python 3.8 or higher
• pip package manager
• Git (for cloning)

1. Clone the Repository

   git clone https://github.com/Miftah-Ebrahim/solar-challenge-week1/
   cd SOLAR-CHALLENGE-WEEK1

2. Create Virtual Environment

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Run the Dashboard

   cd notebooks/app
   streamlit run main.py

The dashboard will automatically open in your browser at http://localhost:8501

SOLAR-CHALLENGE-WEEK1/
├── data/                          # CSV data files (auto-downloaded if missing)
│   ├── benin_clean.csv
│   ├── sierra_leone_clean.csv
│   └── togo_clean.csv
├── notebooks/
│   └── app/
│       ├── main.py               # Main Streamlit application
│       └── utils.py              # Data loading and preprocessing utilities
├── src/                          # Additional Python modules (optional)
├── .gitignore                    # Git ignore rules
├── requirements.txt              # Python dependencies
└── README.md                     # This file

# Navigate to app directory
cd notebooks/app

# Launch dashboard
streamlit run main.py

1. Select Countries: Choose one or more countries from the sidebar
2. Pick a Metric: Select Temperature (T2M) or Wind Speed (WS10M_MIN)
3. Set Date Range: Filter data by specific time period
4. Explore Tabs:
   • 📈 View time series trends
   • 📊 Compare statistical distributions
   • 🏆 Analyze extreme values
   • 📋 Export filtered data

Each CSV file must contain the following columns:

• YEAR - Year (integer)
• MO - Month (1-12)
• DY - Day (1-31)
• T2M - Temperature at 2 meters (°C)
• WS10M_MIN - Minimum wind speed at 10 meters (m/s)

1. Push to GitHub

   git add .
   git commit -m "Initial commit"
   git push origin main

2. Deploy on Streamlit

   • Visit share.streamlit.io
   • Click "New app"
   • Select your repository
   • Set main file path: notebooks/app/main.py
   • Click "Deploy"

3. Automatic Data Loading

   • If CSV files are not in the repository, the app automatically downloads them from Google Drive
   • First load may take 10-15 seconds for downloads
   • Subsequent loads use cached data

For production deployments, you can set custom configurations in Streamlit Cloud:

[server]
maxUploadSize = 200

[theme]
primaryColor = "#FF6B35"
backgroundColor = "#FFFFFF"
secondaryBackgroundColor = "#F0F2F6"

To use different data sources, update GDRIVE_URLS in utils.py:

GDRIVE_URLS = {
    'benin': 'YOUR_GOOGLE_DRIVE_LINK',
    'sierra_leone': 'YOUR_GOOGLE_DRIVE_LINK',
    'togo': 'YOUR_GOOGLE_DRIVE_LINK'
}

1. Add CSV file to data/ folder with naming convention: {country_name}_clean.csv
2. Update GDRIVE_URLS dictionary in utils.py
3. CSV will automatically appear in country selection dropdown

Adjust cache time-to-live in utils.py:

@st.cache_data(ttl=7200)  # Cache for 2 hours instead of 1

Loads CSV data for a specific country, with automatic Google Drive fallback.

Creates datetime index, handles missing values, and prepares data for analysis.

Filters dataset by selected countries and date range.

Computes statistical summaries grouped by country.

Issue: FileNotFoundError: Data file not found

• Solution: Ensure CSV files are in data/ folder or check internet connection for Google Drive downloads

Issue: ModuleNotFoundError: No module named 'streamlit'

• Solution: Activate virtual environment and run pip install -r requirements.txt

Issue: Port already in use

• Solution: Run streamlit run main.py --server.port 8502

Issue: Deprecated fillna warning

• Solution: Already fixed in latest version using .ffill() and .bfill()

• 📧 Open an issue on GitHub
• 💬 Check Streamlit Community Forum
• 📖 Review Streamlit Documentation

• Follow PEP 8 style guidelines
• Add docstrings to all functions
• Include type hints where applicable
• Update README for new features
• Test locally before pushing

This project is licensed under the MIT License - see the LICENSE file for details.

• Data Source: Solar Challenge Project - Week 1
• Framework: Built with Streamlit
• Visualizations: Powered by Plotly
• Region: West Africa Climate Data (Benin, Sierra Leone, Togo)

Project Maintainer: [Mifta Yibrahim]

• GitHub: [(https://github.com/Miftah-Ebrahim/)]
• Email: miftah6972@gmail.com
• LinkedIn: https://www.linkedin.com/in/miftah-ebrahim