[EMNLP2024] DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models

DA-Code is a comprehensive evaluation dataset designed to assess the data analysis and code generation capabilities of LLM in agent-based data science tasks. Our papers and experiment reports have been published on Arxiv.

Dataset Overview

• 500 complex real-world data analysis tasks across Data Wrangling (DW), Machine Learning (ML), and Exploratory Data Analysis (EDA).
• Tasks cover the entire data analysis pipeline, from raw data handling to gaining insights using SQL and Python.
• Each example is meticulously designed to ensure high complexity and quality, with robust evaluation suites.
• An interactive sandbox environment allows LLMs/Agents to autonomously explore, reason, and complete tasks.

Usage

This dataset can be used to:

• Evaluate LLMs’ data analysis and code generation capabilities
• Benchmark autonomous reasoning in real-world tasks
• Develop and test multi-step data analysis strategies

Citation

If you use this dataset in your research, please cite our paper: