This project analyzes energy usage data 💡 for the United States and New England from 2001-2022. It uses Python 🐍 for data visualization to understand trends and seasonal patterns.
The data comes from a CSV file 📄 containing monthly energy usage for:
- United States - All Sectors 🏭
- United States - Electric Utility ⚡️
- United States - Independent Power Producers 🔋
- United States - Commercial 🏢
- United States - Industrial 🏭
- New England - All Sectors 🌆
The project performs:
- Data wrangling 🧹 using Pandas
- Timeseries manipulation with datetime 🕰
- Plotting trends and seasonality 📉 using Matplotlib and Seaborn
- Resampling and rolling averages 📏
Various plots are generated to visualize:
- Daily 🌞, weekly 🗓, and monthly 🌜 seasonality
- Timeseries decomposition 📉
- Trend analysis using rolling averages 📈
To run the analysis:
- Clone this repo 👯
- Install requirements 📥 (
pip install pandas matplotlib seaborn) - Run Jupyter notebook 📓
- Modify and customize as needed ✏️
📊 I've created interactive data visualizations to help you explore the energy usage trends. Check out my website for more such projects at :aishik-dasgupta.super.site
🤝 Contributions are welcome! If you have ideas for improving this project, feel free to submit issues or pull requests.
📜 This project is licensed under the MIT License - see the LICENSE file for details.