绘制 EDA 图表:用混合方法表征计算笔记本中交互式可视化的使用情况

Dylan Wootton, Amy Rae Fox, Evan Peck, Arvind Satyanarayan
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引用次数: 0

摘要

交互式可视化是探索性数据分析(EDA)的强大工具,但交互式可视化如何影响分析师对数据的观察?通过对参与者的话语进行定性编码,我们引入了一种形式主义,将 EDA 描述为一系列分析状态,其中每个状态都由分析师构建的表述(如数据框架的输出、交互式可视化等)或分析师的观察(如关于缺失数据、变量之间的关系等)组成。通过将形式主义应用于数据集,我们发现,与静态可视化相比,交互式可视化平均能更早更复杂地洞察数据集属性之间的关系。此外,通过计算重访次数和表征多样性等指标,我们发现有些表征更像是 EDA 过程中的 "规划辅助工具",而非严格意义上的假设解答工具。我们展示了这些指标如何帮助识别分析行为的其他模式,例如 "80-20 规则",即一小部分表征驱动了大部分观察。基于这些发现,我们为交互式探索分析工具提供了设计指南,并思考了研究可视化在 EDA 中的作用的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism
Interactive visualizations are powerful tools for Exploratory Data Analysis (EDA), but how do they affect the observations analysts make about their data? We conducted a qualitative experiment with 13 professional data scientists analyzing two datasets with Jupyter notebooks, collecting a rich dataset of interaction traces and think-aloud utterances. By qualitatively coding participant utterances, we introduce a formalism that describes EDA as a sequence of analysis states, where each state is comprised of either a representation an analyst constructs (e.g., the output of a data frame, an interactive visualization, etc.) or an observation the analyst makes (e.g., about missing data, the relationship between variables, etc.). By applying our formalism to our dataset, we identify that interactive visualizations, on average, lead to earlier and more complex insights about relationships between dataset attributes compared to static visualizations. Moreover, by calculating metrics such as revisit count and representational diversity, we uncover that some representations serve more as "planning aids" during EDA rather than tools strictly for hypothesis-answering. We show how these measures help identify other patterns of analysis behavior, such as the "80-20 rule", where a small subset of representations drove the majority of observations. Based on these findings, we offer design guidelines for interactive exploratory analysis tooling and reflect on future directions for studying the role that visualizations play in EDA.
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