metadata

license: mit
task_categories:
  - table-question-answering
  - text-generation
  - summarization
language:
  - en
pretty_name: DA-Code
size_categories:
  - 1B<n<10B
tags:
  - code
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.csv
    sep: ','

[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: