---
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:

```

```