循环:利用来源和可视化来支持笔记本中的探索性数据分析.
IEEE transactions on visualization and computer graphics
|September 23, 2024
概括
循环通过可视化笔记本的变化,提高代码质量,回忆和计算笔记本中的可重复性来增强探索性数据科学. 这种视觉支持有助于代数据分析和版本比较.
科学领域:
- 数据科学数据科学数据科学
- 人与计算机的交互
- 软件工程 软件工程 软件工程
背景情况:
- 探索性数据科学本质上是代的,涉及数据采集,清理,分析,分析和解释.
- 传统的线性计算笔记本对代工作流提出了挑战,影响了代码质量,回忆和可重现性.
- 现有的工具往往缺乏可视化数据分析笔记本中的变化演变和影响的有效机制.
研究的目的:
- 介绍Loops,这是一套新的视觉支持技术,旨在用于计算笔记本中的代和探索性数据分析.
- 在循环数据科学过程中解决代码质量,回忆和可重现性的挑战.
- 通过可视化变化的影响和促进版本比较,提高透明度和支持数据分析师.
主要方法:
- 循环利用来源信息来创建笔记本电脑随时间演变的可视化.
- 它特别可视化了笔记本版本的代码,标记,表格,可视化和图像的差异.
- 一个单独的视图允许详细探索这些发现的差异.
主要成果:
- 循环有效地可视化了计算笔记本中的变化影响,追踪其来源.
- 该系统突出显示各种版本的笔记本文物之间的差异,包括数据,代码和输出.
- 用户反和使用案例演示证实了循环在支持数据分析方面的实用性和潜在影响.
结论:
- 循环为计算笔记本中的代探索性数据分析提供了一个透明和支持的环境.
- 通过可视化来源和差异,Loops帮助分析师了解其变化的影响和比较版本.
- 该方法有可能显著提高数据科学工作流程的质量,回忆和可重复性.
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