IEEE VIS 2024 Content: PyGWalker: On-the-fly Assistant for Exploratory Visual Data Analysis

Best Paper Award

PyGWalker: On-the-fly Assistant for Exploratory Visual Data Analysis

Yue Yu - The Hong Kong University of Science and Technology, Hong Kong, China. Kanaries Data Inc., Hangzhou, China

Leixian Shen - The Hong Kong University of Science and Technology, Hong Kong, China

Fei Long - Kanaries Data Inc., Hangzhou, China

Huamin Qu - The Hong Kong University of Science and Technology, Hong Kong, China

Hao Chen - Kanaries Data Inc., Hangzhou, China

Room: Bayshore I + II + III

2024-10-15T15:21:00Z GMT-0600 Change your timezone on the schedule page
2024-10-15T15:21:00Z
Exemplar figure, described by caption below
The image shows the interface of PyGWalker integrated into a Jupyter Notebook. PyGWalker is invoked with a single line of code, allowing users to seamlessly explore and visualize data using drag-and-drop functionality. Its user-friendly interface supports flexible data transformation and interactive visualization, making it popular among the data science community with over 612k downloads through PyPI and 10.8k stars on GitHub.
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Keywords

Data Visualization; Exploratory Data Analysis; Computational Notebooks

Abstract

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.