This article aims to evaluate the application effect of Python in intelligent transportation system (ITS) data analysis. With the development of informatization and digitalization, ITS has become the core of urban traffic management. The article first introduces the concept of ITS, technical framework and principles of Python data analysis, and then analyzes in detail the application of Python in data collection, processing, traffic mode prediction and traffic safety analysis. The research adopted a case analysis method and selected the intelligent transportation system of a certain city for empirical research. Through tools such as Python’s Pandas library and SciPy library, the collected traffic data is processed and analyzed, and models such as linear regression and time series prediction are used to predict and optimize traffic flow and safety. The results show that data analysis using Python effectively improves traffic circulation, reduces accident rates, and optimizes the traffic signal system.