Research on Python-Enabled Web Crawling and Data Visualization for Structured Data Analysis

This paper comprehensively analyzes the application of the Python programming language in the domains of web crawling and data visualization, specifically focusing on structured data analysis. With the unprecedented and rapid growth of Internet data across various sectors, traditional manual data collection methods can no longer meet the contemporary needs of efficient, large-scale data analysis. Consequently, automated extraction techniques have become indispensable. Python provides robust technical support and a highly versatile ecosystem for webpage data acquisition, data cleaning, structured processing, and visual presentation. This is achieved through the deployment of powerful libraries such as Requests, BeautifulSoup, Scrapy, and Selenium for extraction, alongside Pandas for data manipulation. Furthermore, Matplotlib, Seaborn, Plotly, and Pyecharts are utilized for advanced graphical representation. This study systematically discusses the fundamental processes of Python-based web crawling, detailing the methodologies of data cleaning, transformation, and formatting. Additionally, it evaluates the strategic selection of appropriate visualization tools tailored for diverse analytical scenarios and business intelligence requirements. The empirical results demonstrate that Python-driven frameworks can effectively and significantly improve data collection efficiency, enhance overall data quality, and facilitate deeper result interpretation. However, despite these advantages, several critical challenges remain. Issues such as sophisticated anti-crawling mechanisms, strict data privacy compliance, inherently unstable raw data quality, and the potential for subjective chart interpretation still require careful attention and ongoing methodological refinement.