Boxplots and related visualization methods are widely used exploratory tools for taking a first look at collections of univariate variables. In this note an extension is provided that is specifically designed to detect and display bimodality and multimodality when the data warrant it. For this purpose a univariate clustering method is constructed that ensures contiguous clusters, meaning that no cluster has members inside another cluster, and such that each cluster contains at least a given number of unique members. The resulting bixplot display facilitates the identification and interpretation of potentially meaningful subgroups underlying the data. The bixplot also displays the individual data values, which can draw attention to isolated points. Implementations of the bixplot are available in both Python and R, and their many options are illustrated on several real datasets. For instance, an external variable can be visualized by color gradations inside the display.

翻译：箱线图及相关可视化方法是用于初步探索单变量数据集的常用工具。本文提出一种专为检测并展示数据中双峰性及多峰性而设计的扩展方法。为此，我们构建了一种确保簇连续性的单变量聚类方法，即每个簇内部不包含其他簇的成员，且每个簇至少包含指定数量的独立成员。由此生成的bixplot图有助于识别和解释数据中潜在的有意义子群体。该图还能展示个体数据值，从而引起对孤立数据点的注意。bixplot图已在Python和R中实现，并通过多个真实数据集展示了其丰富的选项功能。例如，可在图中通过颜色渐变来可视化外部变量。