Edvart: A Python-Based Framework for Automated Data Analysis and Visualization

Automated and scalable data analysis frameworks are becoming essential in handling rapidly increasing data volumes across business, scientific, and industrial sectors. Exploratory Data Analysis (EDA) is a crucial step in any analytical pipeline, enabling analysts to identify patterns, detect anomalies, validate assumptions, and guide downstream modeling. However, generating comprehensive EDA reports manually is time-consuming and often inconsistent across projects. This research paper presents Edvart, an open-source Python library designed to automate data exploration and visualization with minimal user intervention. Edvart provides a modular architecture comprising overview statistics, univariate, bivariate, multivariate, and time-series analyses, generating well-structured interactive reports. With support for extensibility, customizable code export, and interoperability with the Python data ecosystem, Edvart enables faster and more reliable analytical workflows. Experimental results demonstrate that Edvart reduces development time for EDA tasks while improving depth of insights. The study highlights the framework’s architecture, design, and performance benefits for data science applications.