Python package to optionnally compute statistical test and add statistical annotations on an existing boxplot/barplot generated by seaborn.

• Single function to add statistical annotations on an existing boxplot/barplot generated by seaborn boxplot.
• Integrated statistical tests (binding to scipy.stats methods):
  • Mann-Whitney
  • t-test (independent and paired)
  • Welch's t-test
  • Levene test
  • Wilcoxon test
  • Kruskal-Wallis test
• Smart layout of multiple annotations with correct y offsets.
• Annotations can be located inside or outside the plot.
• Format of the statistical test annotation can be customized: star annotation, simplified p-value, or explicit p-value.
• Optionally, custom p-values can be given as input. In this case, no statistical test is performed.

The latest stable release can be installed from PyPI:

pip install statannot

You may instead want to use the development version from Github:

pip install git+https://github.com/webermarcolivier/statannot.git

See example jupyter notebook example/example.ipynb.

Here is a minimal example:

import seaborn as sns
from statannot import add_stat_annotation

df = sns.load_dataset("tips")
x = "day"
y = "total_bill"
order = ['Sun', 'Thur', 'Fri', 'Sat']
ax = sns.boxplot(data=df, x=x, y=y, order=order)
test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,
                                   box_pairs=[("Thur", "Fri"), ("Thur", "Sat"), ("Fri", "Sun")],
                                   test='Mann-Whitney', text_format='star',
                                   loc='outside', verbose=2)
test_results

More examples are available in the jupyter notebook example/example.ipynb.

• Python >= 3.5
• numpy >= 1.12.1
• seaborn >= 0.8.1
• matplotlib >= 2.2.2
• pandas >= 0.23.0
• scipy >= 1.1.0