This paper presents our system for SemEval-2025 Task 8: DataBench, Question-Answeringover Tabular Data. The primary objective ofthis task is to perform question answering ongiven tabular datasets from diverse domains;under two subtasks: DataBench QA (SubtaskI) and DataBench Lite QA (Subtask II). Totackle both subtasks, we developed a zero-shotsolution with a particular emphasis on lever-aging Large Language Model (LLM)-basedcode generation. Specifically, we proposeda Python code generation framework, utiliz-ing state-of-the-art open-source LLMs to gen-erate executable Pandas code via optimizedprompting strategies. Our experiments revealthat different LLMs exhibit varying levels ofeffectiveness in Python code generation. Addi-tionaly, results show that Python code genera-tion achieves superior performance in tabularquestion answering compared to alternative ap-proaches. Although our ranking among zero-shot systems is unknown at the time of this pa-per’s submission, our system achieved eighthplace in Subtask I and sixth place in Subtask IIamong the 30 systems that outperformed thebaseline in the open-source models category.