data2boxplot is a lightweight, powerful web app that instantly transforms CSV data into publication-quality boxplots — complete with ANOVA and Tukey HSD analysis. No coding required. 🧪 Built with Python, Streamlit, pandas, seaborn, and statsmodels.
➡️ Try it live: https://data2boxplot.com
- 🔼 Upload CSV or Excel files directly
- 📊 Generate clean, customizable boxplots instantly
- 📈 Add violin plots, scatter points, and group means
- 🧠 One-way ANOVA + post hoc Tukey HSD
- ✅ Clear natural-language interpretations of results
- 📤 Downloadable Tukey tables
- 📦 No installation or coding needed — just drop your file
| Upload your file |
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| Boxplot Output & Violin |
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| Tukey HSD & Signifigance |
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- Upload a CSV or Excel file.
- Choose a numerical column to plot, and a grouping column.
- Customize options — like adding scatter points or violin overlays.
- Analyze using ANOVA (auto-detected p-values), and if significant...
- Interpret clear summaries with post hoc Tukey HSD.
- Download your results in one click.
As a neuroscience undergrad doing biomedical research, I constantly needed fast, clean visuals for small group comparisons — especially with boxplots and ANOVA. Most tools were clunky, overkill, or required code.
data2boxplot was built for speed, clarity, and accessibility — for students, researchers, and educators alike.
- Streamlit
- pandas
- matplotlib
- seaborn
- statsmodels
- Python 3.10+
git clone https://github.com/rsmith3rd/data2boxplot.git
cd data2boxplot
pip install -r requirements.txt
streamlit run data2boxplot.py