  Exploratory visual data analysis tools empower data analysts to efficiently
and intuitively explore data insights throughout the entire analysis cycle.
However, the gap between common programmatic analysis (e.g., within
computational notebooks) and exploratory visual analysis leads to a disjointed
and inefficient data analysis experience. To bridge this gap, we developed
PyGWalker, a Python library that offers on-the-fly assistance for exploratory
visual data analysis. It features a lightweight and intuitive GUI with a shelf
builder modality. Its loosely coupled architecture supports multiple
computational environments to accommodate varying data sizes. Since its release
in February 2023, PyGWalker has gained much attention, with 612k downloads on
PyPI and over 10.5k stars on GitHub as of June 2024. This demonstrates its
value to the data science and visualization community, with researchers and
developers integrating it into their own applications and studies.
