DataFrame Operations: A Framework-Agnostic Library
Dataframe operations form the foundation of modern data science workflows, yet researchers often face vendor lock-in when choosing between different dataframe frameworks. My work addresses this by developing a unified operations library that abstracts away framework-specific implementations. The challenge lies in handling the fundamental differences between these systems—some pandas-like dataframes use row-by-row operations, others employ lazy evaluation with native expressions, some require distributed execution patterns, while others focus on parallelization.
I’ve implemented operations like merge, reduce, replace, forward-fill, and mathematical transformations that work identically across all frameworks. Extensive benchmarking infrastructure—with synthetic datasets at small, medium, and large scales—ensures performance optimization while maintaining consistent behavior.
