Spreadsheet-heavy analytical work remains common in business analytics, operations reporting, and applied research, yet workbooks that grow through formulas, manual edits, and copy-paste refresh are difficult to audit, reproduce, and govern at scale. When tabular work requires repeatability, validation, version control, automated refresh, or integration with statistics and machine learning, analysts need a transformation layer that preserves familiar table concepts while making assumptions explicit. This paper treats the Python pandas library as that layer: a practical bridge between spreadsheet practice and research-grade workflows, not a wholesale replacement for Excel. The paper contributes an Excel-to-pandas migration mapping, a taxonomy of nine workflow categories, seven end-to-end examples drawn from business analytics and applied research, a failure-mode catalog, and reusable code recipes for governed tabular work. pandas is most useful when tabular analysis must be repeatable, auditable, and defensible, while Excel can remain a familiar input and output interface for stakeholders who need workbooks.

翻译：电子表格主导的分析工作仍常见于业务分析、运营报告和应用研究中，然而，通过公式、手动编辑和复制粘贴更新演进的工作簿，在规模化审计、复现和管控方面面临诸多困难。当表格工作需具备可重复性、可验证性、版本控制、自动刷新或需与统计及机器学习集成时，分析师需要一个转换层，在保留熟悉表格概念的同时使假设显式化。本文将 Python pandas 库视为这一转换层：一座连接电子表格实践与研究级工作流的实用桥梁，而非对 Excel 的完全替代。本文提供了从 Excel 到 Pandas 的迁移映射、涵盖九类工作流的分类体系、七个源自业务分析与应用研究的端到端示例、一份故障模式目录，以及面向受控表格工作的可复用代码方案。当表格分析须具备可重复性、可审计性和可辩护性时，Pandas 最为实用；同时，对于需要工作簿的利益相关方，Excel 仍可作为熟悉的输入输出界面。