✨ 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.
- 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.
- 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.
- Mini-meta Weighted Delta Fix 🧮
The weighted delta calculation in mini-meta plots has been updated for greater accuracy and consistency.
- 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.
Thank you for your continued support!
The DABEST Development Team