https://github.com/mementomorri/habits-visualizer
Visualization and analysis for data from open source project Loop Habits Tracker app.
https://github.com/mementomorri/habits-visualizer
csv matplotlib pandas python visualization
Last synced: about 2 months ago
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Visualization and analysis for data from open source project Loop Habits Tracker app.
- Host: GitHub
- URL: https://github.com/mementomorri/habits-visualizer
- Owner: mementomorri
- License: mit
- Created: 2025-05-28T09:36:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-01T17:55:57.000Z (about 1 year ago)
- Last Synced: 2025-06-02T03:38:25.410Z (about 1 year ago)
- Topics: csv, matplotlib, pandas, python, visualization
- Language: Python
- Homepage:
- Size: 34.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# habits-visualizer
Visualization and analysis for data from open source project [Loop Habits Tracker app](https://github.com/iSoron/uhabits).
## Features
- 📊 Overall progress heatmap
- 📈 Habit strength trend visualization
- 📅 Monthly comparison reports
- 🔍 Individual habit trend analysis
- 📝 Summary statistics and streaks
- 💾 CSV exports for further analysis
## Quick Start
1. Place your Loop Habits export files in `data/` directory:
- `Checkmarks.csv`
- `Habits.csv`
- `Scores.csv`
2. Install dependencies:
```bash
pip install pandas matplotlib numpy
```
3. Run the analysis:
```bash
python run.py
```
## Output Files
All outputs will be saved in the `output/` directory.
File Pattern and Description:
- `habit_completion_history.png` Heatmap of all habits over time
- `habit_score_trends.png` Trend lines for habit scores
- `monthly_report_YYYY_MM.png` Visual monthly comparison
- `monthly_habit_report_YYYY_MM.csv` Tabular monthly completion rates
- `habit_trend_.png` Detailed trend for each habit
- `habit_summary.csv` Overall statistics and streaks
### Example Output
Habit Heatmap
Overall progress visualization
Monthly Report
Monthly habit performance comparison
### Customization
Modify run_analysis.py to:
- Change date ranges for monthly reports
- Adjust visualization styles
- Add custom analysis metrics
## Requirements
- Python 3.7+
- pandas
- matplotlib
- numpy
## License
MIT License - use freely, credit appreciated.