🌀🌦️ AtmosPulse (Python)

A modular Python project for analyzing and visualizing real-world weather data using API integration, time-series analysis, and statistical techniques.

This project demonstrates an end-to-end data pipeline:

API → Data Processing → Analysis → Visualization → Insights

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✨ Features

• 🌍 Fetch real-time weather data (Vienna, AT) via API

• 🐼 Structured data handling using Pandas

• 📊 Compute key metrics:

  • Average temperature
  • Moving averages (trend smoothing)
  • Temperature anomalies (heat waves / cold drops)

• 🌧️ Precipitation analysis and visualization

• 📈 Time-series visualizations (temperature trends)

• 🔥 Anomaly detection and highlighting

• 💾 Export analysis results to CSV

• 🧠 Clean, modular project structure

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🧱 Project Structure

atmospulse/
│
├── src/               # Core logic
│   ├── data_loader.py
│   ├── analysis.py
│   ├── visualization.py
│
├── outputs/           # Generated files (plots, exports)
├── main.py            # Entry point
├── README.md

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📸 Example Output

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⚙️ Tech Stack

• Python 3.14
• Pandas
• NumPy
• Matplotlib
• Requests (API integration)

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▶️ How to Run

pip install -r requirements.txt
python main.py

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📊 Example Insights Generated

• Identification of hottest and coldest days
• Smoothed temperature trends using moving averages
• Detection of unusual temperature deviations (anomalies)
• Daily precipitation patterns

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🎯 Purpose

This project demonstrates:

• Working with real-world APIs
• Structuring and analyzing time-series data
• Applying rolling statistics and anomaly detection
• Building modular and scalable data workflows
• Visualizing insights effectively using Python

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🔮 Future Improvements

• CLI interface (argparse / Typer)
• Interactive dashboards (Plotly / Streamlit)
• Forecasting models (time-series / ML)

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📜 License

CC0-1.0