dabest 2025.10.20

Data Analysis and Visualization using Bootstrap-Coupled Estimation.

DABEST-Python

Recent Version Update

✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨

Dear DABEST users, The latest version of the DABEST Python library brings new visualizations, refined plots, and improved accuracy.

1. Whorlmap 🌀: Compact visualization for multi-dimensional effects

   Introducing Whorlmap, a new way to visualize effect sizes from multiple comparisons in a compact, grid-based format.

   Whorlmaps condense information from the full bootstrap distributions of many contrast objects into a 2D heatmap-style grid of “whorled” cells. This provides an overview of the entire dataset while preserving the underlying distributional detail.

   They are especially useful for large-scale or multi-condition experiments, serving as a space-efficient alternative to stacked forest plots.

   You can generate a Whorlmap directly from multi-dimensional DABEST objects using the .whorlmap() method. See the Whorlmap tutorial for more details.

2. Slopegraphs 📈: Enhanced summaries for paired data

   Slopegraphs for paired continuous data now display group summary statistics.

   • By default, a thick trend line connects group means, with vertical bars showing standard deviation.

   • Choose the summary type via the group_summaries argument in .plot() — options include 'mean_sd', 'median_quartiles', or None.

   • Customize appearance with group_summaries_kwargs.

   See the Group Summaries section in the Plot Aesthetics tutorial for more details.

3. Mini-meta Weighted Delta Fix 🧮

   The weighted delta calculation in mini-meta plots has been updated for greater accuracy and consistency.

4. Expanded custom_palette functionality 🎨

   • Barplots (unpaired, proportional): custom_palette can now take 1 and 0 as dictionary keys to color the filled and unfilled portions of the plot.

   • Slopegraphs (paired, non-proportional): custom_palette can now color contrast bars and effect-size curves.

See the Custom Palette section in the Plot Aesthetics tutorial for examples.

Thank you for your continued support!

The DABEST Development Team

Contents

• About
• Installation
• Usage
• How to cite
• Bugs
• Contributing
• Acknowledgements
• Testing
• DABEST in other languages

About

DABEST is a package for Data Analysis using Bootstrap-Coupled ESTimation.

Estimation statistics are a simple framework that avoids the pitfalls of significance testing. It employs familiar statistical concepts such as means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment or intervention, rather than succumbing to a false dichotomy engendered by P values.

An estimation plot comprises two key features.

1. It presents all data points as a swarm plot, ordering each point to display the underlying distribution.

2. It illustrates the effect size as a bootstrap 95% confidence interval on a separate but aligned axis.

DABEST powers estimationstats.com, allowing everyone access to high-quality estimation plots.

Installation

This package is tested on Python 3.11 and onwards. It is highly recommended to download the Anaconda distribution of Python in order to obtain the dependencies easily.

You can install this package via pip.

To install, at the command line run

pip install dabest

You can also clone this repo locally.

Then, navigate to the cloned repo in the command line and run

pip install .

Usage

import pandas as pd
import dabest

# Load the iris dataset. This step requires internet access.
iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")

# Load the above data into `dabest`.
iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
                          idx=("setosa", "versicolor", "virginica"))

# Produce a Cumming estimation plot.
iris_dabest.mean_diff.plot();

Please refer to the official tutorial for more useful code snippets.

How to cite

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3

Paywalled publisher site; Free-to-view PDF

Bugs

Please report any bugs on the issue page.

Contributing

All contributions are welcome; please read the Guidelines for contributing first.

We also have a Code of Conduct to foster an inclusive and productive space.

A wish list for new features

If you have any specific comments and ideas for new features that you would like to share with us, please read the Guidelines for contributing, create a new issue using Feature request template or create a new post in our Google Group.

Acknowledgements

We would like to thank alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, and Stanislav Ott.

Testing

To test DABEST, you need to install pytest and nbdev.

• Run pytest in the root directory of the source distribution. This runs the test suite in the folder dabest/tests/mpl_image_tests.
• Run nbdev_test in the root directory of the source distribution. This runs the value assertion tests in the folder dabest/tests

The test suite ensures that the bootstrapping functions and the plotting functions perform as expected.

For detailed information, please refer to the test folder

DABEST in other languages

DABEST is also available in R (dabestr) and Matlab (DABEST-Matlab).