## What is it
Python package to optionnally compute statistical test and add statistical annotations on an existing boxplot/barplot generated by seaborn.
## Features
- 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.
## Installation
The latest stable release can be installed from PyPI:
```python
pip install statannot
```
You may instead want to use the development version from Github:
```python
pip install git+https://github.com/webermarcolivier/statannot.git
```
## Documentation
See example jupyter notebook `example/example.ipynb`.
## Usage
Here is a minimal example:
```python
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`.
## Examples
## Requirements
+ Python >= 3.5
+ numpy >= 1.12.1
+ seaborn >= 0.8.1
+ matplotlib >= 2.2.2
+ pandas >= 0.23.0
+ scipy >= 1.1.0
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"description": "## What is it\n\nPython package to optionnally compute statistical test and add statistical annotations on an existing boxplot/barplot generated by seaborn.\n\n## Features\n\n- Single function to add statistical annotations on an existing boxplot/barplot generated by seaborn boxplot.\n- Integrated statistical tests (binding to `scipy.stats` methods):\n - Mann-Whitney\n - t-test (independent and paired)\n - Welch's t-test\n - Levene test\n - Wilcoxon test\n - Kruskal-Wallis test\n- Smart layout of multiple annotations with correct y offsets.\n- Annotations can be located inside or outside the plot.\n- Format of the statistical test annotation can be customized: star annotation, simplified p-value, or explicit p-value.\n- Optionally, custom p-values can be given as input. In this case, no statistical test is performed.\n\n## Installation\n\nThe latest stable release can be installed from PyPI:\n\n```python\npip install statannot\n```\nYou may instead want to use the development version from Github:\n\n```python\npip install git+https://github.com/webermarcolivier/statannot.git\n```\n\n## Documentation\n\nSee example jupyter notebook `example/example.ipynb`.\n\n## Usage\n\nHere is a minimal example:\n\n```python\nimport seaborn as sns\nfrom statannot import add_stat_annotation\n\ndf = sns.load_dataset(\"tips\")\nx = \"day\"\ny = \"total_bill\"\norder = ['Sun', 'Thur', 'Fri', 'Sat']\nax = sns.boxplot(data=df, x=x, y=y, order=order)\ntest_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n box_pairs=[(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n test='Mann-Whitney', text_format='star',\n loc='outside', verbose=2)\ntest_results\n```\n\nMore examples are available in the jupyter notebook `example/example.ipynb`.\n\n\n## Examples\n\n\n\n\n\n## Requirements\n\n+ Python >= 3.5\n+ numpy >= 1.12.1\n+ seaborn >= 0.8.1\n+ matplotlib >= 2.2.2\n+ pandas >= 0.23.0\n+ scipy >= 1.1.0\n\n\n\n",
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