Pandas for Reproducible Data Analysis: From Spreadsheets to Research-Grade Python Workflows

2025-01-05 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

The paper "Pandas for Reproducible Data Analysis" positions the Python pandas library as a practical bridge between spreadsheet-heavy analytical work and research-grade workflows, rather than a full Excel replacement. It addresses the challenges of auditing, reproducing, and governing spreadsheet-based work by providing a transformation layer that preserves familiar table concepts. The contribution includes an Excel-to-pandas migration mapping, a taxonomy of nine workflow categories, seven end-to-end examples from business analytics and applied research, a failure-mode catalog, and reusable code recipes. pandas is highlighted for its utility in ensuring tabular analysis is repeatable, auditable, and defensible, while Excel can remain an interface for stakeholders.

Key takeaway

For data analysts and research scientists transitioning from spreadsheet-centric workflows, adopting pandas for data transformation is crucial for improving reproducibility and governance. Focus on encoding business rules in version-controlled scripts, validating joins with explicit cardinality checks, and reconciling totals before exporting reports. This approach ensures auditability and reduces manual errors, making your analytical outputs more defensible and reliable for recurring tasks.

Key insights

Pandas enables auditable, repeatable data transformation, bridging spreadsheet familiarity with programmatic rigor for governed analytics.

Principles

• Raw data should be immutable.
• Scripts beat notebooks for repetition.
• Validate assumptions with assertions.

Method

The paper proposes a maturity path from exploratory notebooks to reproducible project workflows, emphasizing promoting stable logic into named functions and scripts, using immutable raw data, and logging intermediate outputs.

In practice

• Declare ID columns as strings.
• Validate merge cardinality.
• Compare detail and summary totals.

Topics

• Pandas
• Reproducible Research
• Data Governance
• Excel Migration
• Data Validation
• Business Analytics Workflows

Best for: AI Scientist, Data Scientist, Data Analyst, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.