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A persistent challenge to table question answering (TableQA) by generatingexecutable programs has been adapting to varied table structures, typicallyrequiring domain-specific logical forms. In response, this paper introduces aunified TableQA framework that: (1) provides a unified representation forstructured tables as multi-index Pandas data frames, (2) uses Python as apowerful querying language, and (3) uses few-shot prompting to translate NLquestions into Python programs, which are executable on Pandas data frames.Furthermore, to answer complex relational questions with extended programfunctionality and external knowledge, our framework allows customized APIs thatPython programs can call. We experiment with four TableQA datasets that involvetables of different structures -- relational, multi-table, and hierarchicalmatrix shapes -- and achieve prominent improvements over past state-of-the-artsystems. In ablation studies, we (1) show benefits from our multi-indexrepresentation and APIs over baselines that use only an LLM, and (2)demonstrate that our approach is modular and can incorporate additional APIs.

API-Assisted Code Generation for Question Answering on Varied Table Structures