The widespread adoption of sharing bicycles in modern urban transportation has brought a vast amount of riding data which contains rich information about user behavior.This paper aims to conduct an in-depth analysis of the Kaggle sharing bicycle dataset to explore the main factors influencing sharing bicycle usage patterns.It utilizes Pandas library of Python for data processing and employs Seaborn and Matplotlib for visual analysis,providing an intuitive display of data characteristics and user behavior patterns.The study finds that rental quantities are closely related to meteorological factors such as temperature,humidity,and wind speed,with rental activities being more frequent during specific time periods.This research not only demonstrates the superiority of Pandas,Seaborn,and Matplotlib in data visualization,but also provides data support for urban traffic management and sharing bicycle operation,thereby optimizing traffic management and enhancing user experience.
Big Data analysisvisualizationsharing bicycle dataPython
维普
万方数据
