Analyze and visualize your Apple Health data and custom workout logs using Python.
This project provides tools to load, clean, analyze, and visualize health and fitness data exported from Apple Health (XML) and custom workout logs (CSV). It is designed to help users gain actionable insights into their health trends, workout routines, and overall fitness progress.
- Apple Health Data Analysis:
- Load and parse Apple Health XML exports
- Clean and preprocess health records
- Visualize trends (steps, heart rate, sleep, etc.)
- Identify patterns and anomalies
- Workout Log Analysis:
- Import and clean custom workout CSV logs
- Aggregate and summarize workout routines
- Visualize exercise frequency, volume, and progress
- Modern Data Science Stack:
- Uses pandas, matplotlib, seaborn, plotly, and numpy for robust analysis and visualization
- Python 3.8+
- Install required packages:
pip install pandas matplotlib seaborn plotly numpy
- Apple Health Data:
- Export your data from the Apple Health app (XML format)
- Place the
export.xmlfile in a folder namedapple_health_exportin the project root
- Workout Logs:
- Place your workout CSV files in a folder named
workout_logsin the project root - Example filenames:
1 - PLP Training Split - 2024 - Spring 2024.csv, etc.
- Place your workout CSV files in a folder named
- Analyze Apple Health Data:
python Health_Data.py
- Analyze Workout Logs:
python Workout_Logs.py
Both scripts will output visualizations and summary statistics to help you understand your health and fitness data.
- Track your daily step count and activity trends
- Visualize heart rate or sleep patterns over time
- Summarize your most frequent exercises and progress in workout routines
Contributions are welcome! Please open issues or submit pull requests for improvements, bug fixes, or new features.
This project is for educational and personal use.
Created by Connor Hardin for COMSC-225 Final Project