Survey Analysis: Life Cycle Assessment of E-Cigarettes

This repository contains the analysis of survey data regarding the life cycle assessment (LCA) of e-cigarettes. The analysis is performed using a Jupyter notebook that processes survey responses and generates visualizations.

📊 Project Overview

The auswerting.ipynb notebook analyzes survey responses.

📁 Data Requirements

Survey Data

Important: The survey data file is not included in this public repository for privacy reasons.

You need to copy your survey data to:

./data/Umfrage_ Oekobilanz von E-Zigaretten.json

The expected JSON structure should contain:

• questions: Object with question definitions
• responses: Array of survey responses

Generated Outputs

The notebook generates PDF visualizations for specific questions:

• ./out/q2.pdf - E-cigarette usage frequency analysis
• ./out/q5.pdf - [Question 5 analysis]
• ./out/q13.pdf - [Question 13 analysis]

🚀 Setup and Installation

Prerequisites

• Python 3.12 or higher
• uv package manager
• VSCode with Jupyter extension

Installation Steps

1. Clone the repository

   git clone <repository-url>
   cd umfrage_projektarbeit
   

2. Install dependencies using uv

   uv sync
   

   This will create a virtual environment and install all required packages.

3. Activate the virtual environment

   source .venv/bin/activate  # On Linux/macOS
   # or
   .venv\Scripts\activate     # On Windows
   

📓 Running the Notebook in VSCode

Method 1: Using VSCode Jupyter Extension

1. Open VSCode and ensure you have the Jupyter extension installed
2. Open the project folder in VSCode
3. Select the Python interpreter:
   • Press Ctrl+Shift+P (or Cmd+Shift+P on macOS)
   • Type "Python: Select Interpreter"
   • Choose the interpreter from .venv/bin/python
4. Open the notebook: auswerting.ipynb
5. Run cells: Use the play button next to each cell or press Shift+Enter

Method 2: Using Jupyter Lab/Notebook

1. Activate the virtual environment

   source .venv/bin/activate
   

2. Launch Jupyter

   jupyter lab
   # or
   jupyter notebook
   

3. Navigate to the notebook and run the cells

📦 Dependencies

The project uses the following main dependencies:

• jupyter>=1.1.1 - Jupyter notebook environment
• matplotlib>=3.10.5 - Plotting library
• rich>=14.1.0 - Rich text and formatting
• seaborn>=0.13.2 - Statistical data visualization

📈 Analysis Output

The notebook generates:

• Interactive visualizations for survey questions
• PDF exports of key charts (stored in ./out/)
• Statistical summaries of responses
• Data filtering and processing for specific questions of interest