Statistical analysis is a crucial part of data visualization, and the Statannotations package makes it simple to add statistical tests directly to your Seaborn plots. This guide will walk you through the installation and usage of the package, so you can enhance your data visualizations with statistical significance annotations.

What is Statannotations?

Statannotations is a Python package designed to compute statistical tests and add annotations on plots created with Seaborn. It works seamlessly with various types of plots including box plots, bar plots, and violin plots. With its easy-to-use interface and integrated statistical testing capabilities, it can greatly improve your data visualization work.

Features

• Single function to add statistical annotations on plots.
• Integrated statistical tests (e.g., Mann-Whitney, t-tests, Kruskal-Wallis, etc.).
• Smart layout of multiple annotations with correct y offsets.
• Customizable annotation formats.
• Support for corrections for multiple testing.

Installation

To get started, you’ll need to install the Statannotations package. The latest stable release (v0.6.0) can be installed from PyPi using the following command:

pip install statannotations

You can also install it with conda:

conda install -c conda-forge statannotations

If you want optional dependencies for multiple comparisons testing, use:

pip install -r requirements.txt

How to Use Statannotations

Once installed, you can start using Statannotations in just a few steps. Here’s a minimal example:

import seaborn as sns
from statannotations.Annotator import Annotator

# Load a sample dataset
df = sns.load_dataset('tips')
x = 'day'
y = 'total_bill'
order = ['Sun', 'Thur', 'Fri', 'Sat']

# Create a box plot
ax = sns.boxplot(data=df, x=x, y=y, order=order)

# Define the pairs of groups you want to compare
pairs = [('Thur', 'Fri'), ('Thur', 'Sat'), ('Fri', 'Sun')]

# Create an annotator and configure the statistical test
annotator = Annotator(ax, pairs, data=df, x=x, y=y, order=order)
annotator.configure(test='Mann-Whitney', text_format='star', loc='outside')

# Apply the annotations
annotator.apply_and_annotate()

In this example, we use a box plot of ‘total_bill’ across different days of the week from the ‘tips’ dataset. The Mann-Whitney test is then configured to compare statistical significance between specific pairs of days.

Troubleshooting

If you encounter any issues while using the Statannotations package, consider these tips:

• Ensure you are using compatible versions of Seaborn (version ≥ 0.12) and pandas (version 2) as they are currently not officially supported.
• If you experience bugs, check the discussion board for similar issues or report your own.
• Refer to the documentation for a comprehensive guide through functionalities.
• For integration with other statistical functions, make sure you provide the correct specifications as noted in the examples.

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Conclusion

Statannotations is a powerful tool designed to enrich your data visualizations with statistical significance, making it easier for you to communicate data-driven findings. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.