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.