TableMage is a Python package for low-code/conversational clinical data science. TableMage can help you quickly explore tabular datasets, easily perform regression analyses, and effortlessly benchmark machine learning models.
We recommend installing TableMage in a new virtual environment. TableMage supports Python version 3.12.
To install TableMage:
git clone https://github.com/YMa-lab/TableMage.git
cd TableMage
pip install .
cd ..
Please read the demo available on readthedocs.
Note
For MacOS users: You might run into an error involving XGBoost, one of TableMage's dependencies, when using TableMage for the first time.
To resolve this error, you'll need to install libomp: brew install libomp. This requries Homebrew.
- February 2026: Our paper on ChatDA has been published in npj Artificial Intelligence! We are working on TableMage's v0.1.0 release. Help us out by reporting bugs in Issues.
- December 2025: We have released a preprint on TableMage's ChatDA agent on medRxiv!
- February 2025: We have released an alpha version of TableMage on PyPI!
If this software was beneficial in your work, please consider citing it as follows:
@article{Yang2026,
author = {Yang, Andrew and Woo, Joshua and Zhang, Ryan and Mach, Alan and Ramkumar, Prem and Ma, Ying},
title = {Tool-wielding language model-based agent offers conversational exploration of clinical tabular data},
journal = {npj Artificial Intelligence},
year = {2026},
volume = {2},
number = {1},
pages = {22},
month = {feb},
doi = {10.1038/s44387-025-00070-2},
url = {https://doi.org/10.1038/s44387-025-00070-2},
issn = {3005-1460},
abstract = {Advancing evidence-based medicine requires integrating clinical expertise with data analysis. While clinicians contribute essential domain knowledge, applying modern data science methods often requires specialized training, creating a barrier to adoption. To bridge this gap, we developed ChatDA, an artificial intelligence agent enabling large language model-mediated conversational analysis of de-identified clinical tabular datasets. ChatDA empowers clinicians to extract meaningful insights efficiently and accurately, making data-driven clinical research more accessible and effective.}
}