2024/06/17 by Yue Yu, Leixian Shen, Fei Long +2 · 1 voice · 1 citation
Computer Science · #Data Visualization and Analytics #Video Analysis and Summarization #cs.HC
paper · pdf · doi:10.1109/vis55277.2024.00009
arxiv published 2024/06/17 · arxiv updated 2024/06/17 · openalex publication_date 2024/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.