DABEST-Python

Recent Version Update

We are proud to announce DABEST Version Dadar (v2025.03.27) This new version of the DABEST Python library includes several new features and performance improvements. It’s a big one!

1. Python 3.13 Support: DABEST now supports Python 3.10—3.13.

2. Horizontal Plots: Users can now create horizontal layout plots, providing compact data visualization. This can be achieved by setting horizontal=True in the .plot() method. See the Horizontal Plots tutorial for more details.

3. Forest Plots: Forest plots provide a simple and intuitive way to visualize many delta-delta (or delta g), mini-meta, or regular delta effect sizes at once from multiple different dabest objects without presenting the raw data. See the Forest Plots tutorial for more details.

4. Gridkey: Users can now represent experimental labels in a ‘gridkey’ table. This can be accessed with the gridkey parameter in the .plot() method. See the gridkey section in the Plot Aesthetics tutorial for more details.

5. Other Visualization Improvements:

- **Comparing means and effect sizes**: The estimation plots now
  include three types of customizable visual features to enhance
  contextualization and comparison of means and effect sizes:

  - **Bars for the mean of the observed values (`raw_bars`)**:
    Colored rectangles that extend from the zero line to the mean of
    each group’s raw data. These bars visually highlight the central
    tendency of the raw data.

  - **Bars for effect size/s (`contrast_bars`)**: Similar to raw
    bars, these highlight the effect-size difference between two
    groups (typically test and control) in the contrast axis. They
    provide a visual representation of the differences between
    groups.

  - **Summary bands (`reference_band`)**: An optional band or ribbon
    that can be added to emphasize a specific effect size’s
    confidence interval that is used as a reference range across the
    entire contrast axis. Unlike raw and contrast bars, these span
    horizontally (or vertically if `horizontal=True`) and are not
    displayed by default.

    Raw and contrast bars are shown by default. Users can customize
    these bars and add summary bands as needed. For detailed
    customization instructions, please refer to the [Plot Aesthetics
    tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html).

- **Tighter spacing in delta-delta and mini-meta plots**: We have
  adjusted the spacing of delta-delta and mini-meta plots to reduce
  whitespace. The new format brings the overall effect size closer
  to the two-groups effect sizes. In addition, delta-delta plots now
  have a gap in the zero line to separate the delta-delta from the ∆
  effect sizes.

- **Delta-delta effect sizes for proportion plots**: In addition to
  continuous data, delta-delta plots now support binary data
  (proportions). This means that 2-way designs for binary outcomes
  can be analyzed with DABEST.

- **Proportion plots sample sizes**: The sample size of each binary
  option for each group can now be displayed. These can be toggled
  on/off via the `prop_sample_counts` parameter.

- **Effect size lines for paired plots**: Along with lines
  connecting paired observed values, the paired plots now also
  display lines linking the effect sizes within a group in the
  contrast axes. These lines can be toggled on/off via the
  `contrast_paired_lines` parameter.

- **Baseline error curves**: To represent the baseline/control group
  in the contrast axes, it is now possible to plot the baseline dot
  and the baseline error curve. The dot is shown by default, while
  the curve can be toggled on/off via the `show_baseline_ec`
  parameter. This dot helps make it clear where the baseline comes
  from i.e. the control minus itself. The baseline error curve can
  be used to show that the baseline itself is an estimate inferred
  from the observed values of the control data.

- **Delta text**: Effect-size deltas (e.g. mean differences) are now
  displayed as numerals next to their respective effect size. This
  can be toggled on/off via the `delta_text` parameter.

- **Empty circle color palette**: A new swarmplot color palette
  modification is available for unpaired plots via the
  `empty_circle` parameter in the `.plot()` method. This option
  modifies the two-group swarmplots to have empty circles for the
  control group and filled circles for the experimental group.

1. Miscellaneous Improvements & Adjustments

- **Numba for speed improvements**: We have added
  [Numba](https://numba.pydata.org/) to speed up the various
  calculations in DABEST. Precalculations will be performed during
  import, which will help speed up the subsequent loading and
  plotting of data.

- **Terminology/naming updates**: During the refactoring of the
  code, we have made several updates to the documentation and
  terminology to improve clarity and consistency. For example:

  - Plot arguments have been adjusted to bring more clarity and
    consistency in naming. Arguments relating to the rawdata plot
    axis will now be typically referred to with `raw` while
    arguments relating to the contrast axis will be referred to with
    `contrast`. For example, `raw_label` replaces `swarm_label` and
    `bar_label`. The various kwargs relating to each different type
    of plot (e.g., `swarmplot_kwargs`) remain unchanged.

  - The method to utilise the Delta *g* effect size is now via the
    .hedges_g.plot() method rather than creating a whole new Delta_g
    object as before. The functionality remains the same, it plots
    hedges_g effect sizes and then the Delta *g* effect size
    alongside these (if a delta-delta experiment was loaded
    correctly).

- **Updated tutorial pages**: We have updated the tutorial pages to
  reflect the new features and changes. The tutorial pages are now
  more comprehensive and (hopefully!) more intuitive!

- **Results dataframe for delta-delta and mini-meta plots**: A
  results dataframe can now be extracted for both the delta-delta
  and mini-meta effect size data (similar to the results dataframe
  for the regular effect sizes). These can be found via the
  `.results` attribute of the `.delta_delta` or `.mini_meta` object.

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

shell pip install dabest

You can also clone this repo locally.

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

shell pip install .

Usage

``` python3 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.

irisdabest = dabest.load(data=iris, x="species", y="petalwidth", idx=("setosa", "versicolor", "virginica"))

Produce a Cumming estimation plot.

irisdabest.meandiff.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).