DAANA

Data Analysis & Automatic Notification Application

Overview

DAANA is a Python-based framework for automated data analysis and reporting. It is structured for interactive exploration via Jupyter notebooks and reproducible execution via uv (Python package and environment manager).

This repository includes:

• Case study: Fallstudie_Rettungswesen
• Exercises: Uebungen
• Custom example: own_case
• Demo notebook: test.ipynb
• Project configuration: pyproject.toml and uv.lock

Features

• Structured modular folder layout for case studies, exercises, and personal projects
• Reproducible environments powered by uv
• Interactive Jupyter notebooks for analysis and experimentation
• Simple to extend for new datasets or business cases

Getting Started

Prerequisites

• Python ≥ 3.11 (as defined in .python-version)
• uv installed globally

Install uv (if not already installed):

pip install uv

Installation & Setup

# Clone the repository
git clone https://github.com/macnch33s3/DAANA.git
cd DAANA

# Create and sync environment using uv
uv sync

uv sync will automatically:

• Create a virtual environment in .venv
• Install all dependencies from pyproject.toml and uv.lock

Running the Demo Notebook

# Activate environment
source .venv/bin/activate     # on Windows: .venv\Scripts\activate

# Launch Jupyter Notebook
uv run jupyter notebook

Open test.ipynb, execute the cells, and verify that all modules load correctly.

Repository Structure

DAANA/
├── Fallstudie_Rettungswesen/    # Case study: rescue services
├── Uebungen/                    # Exercises
├── own_case/                    # Custom case study
├── test.ipynb                   # Demo notebook
├── pyproject.toml               # Project configuration
├── uv.lock                      # Dependency lock file
├── .python-version              # Python version indicator
└── README.md                    # This file

Usage

• Use the provided notebooks to analyse sample datasets.
• Extend the own_case directory with your own data and logic.
• Automate or schedule analysis by wrapping logic into scripts and running via uv run <script>.

Example:

uv run python scripts/analyse.py

Configuration & Customisation

• Add or update dependencies:

  uv add pandas numpy jupyter

• Remove dependencies:

  uv remove <package>

• Update all dependencies:

  uv lock --upgrade

• Modify pyproject.toml to define metadata, dependencies, and scripts.

Recommended Workflow

1. Clone repository
2. Run uv sync
3. Test environment via uv run jupyter notebook
4. Build your own case under own_case
5. Extend or automate via scripts or scheduled jobs

Future Enhancements

• CLI for non-interactive execution
• Integration with API from open.data
• Reporting dashboards (Streamlit / Dash)
• Continuous integration workflows

License

GNU GENERAL PUBLIC LICENSE.

Contributions are welcome: fork, branch, and open a pull request.