This work outlines the AlphaPro team’s solution to SemEval-2025 Task 8: Question Answering on Tabular Data. Our system utilizes a three-stage pipeline that uses natural language questions along with the table’s structural information to generate executable Python code, which is subsequently used to query the table and produce answers. The method achieves up to 67% accuracy in task data, demonstrating the feasibility of code generation for tabular question answering. The strengths and limitations of the approach are outlined and suggestions for further research are provided. The code has been made available in a public code repository to promote reproducibility and research in this area.