Abstract
Environmental pollution has become a global problem, and accurate analysis of environmental data and prediction of pollution trends are of great significance for environmental management and pollution control. Environmental data has the characteristics of multi - source, heterogeneous, and large time - space span, which brings challenges to data processing and analysis. This paper studies the application of Python in environmental data analysis and pollution prediction. First, use Python's Pandas, GeoPandas, and Xarray libraries to process multi - source environmental data, including air quality data, water quality data, and meteorological data, realizing data cleaning, integration, and spatial - temporal analysis. Then, build a pollution prediction model based on Python's TensorFlow framework, which combines the long short - term memory (LSTM) network and the attention mechanism to capture the temporal and spatial correlation of pollution data. Finally, verify the model on the air quality data of a certain city. The results show that the model can accurately predict the concentration of PM2.5 and other pollutants in the next 72 hours, with an average prediction error of less than 10%. The Python - based data analysis tool can effectively process massive environmental data, and the prediction model has high accuracy and practical value, which can provide a scientific basis for environmental decision - making.
Abstract
With the in - depth development of educational informatization, intelligent education platforms have become an important carrier for promoting personalized teaching and improving teaching quality. The traditional education platform has problems such as single function, poor scalability, and low intelligence. This paper develops an intelligent education platform based on Python web frameworks, which takes Django and Flask as the core, and integrates machine learning and data mining technologies to realize functions such as personalized course recommendation, intelligent homework correction, and learning situation analysis. First, the platform uses Django to build the back - end management system, responsible for user management, course management, and data storage; uses Flask to build the front - end interactive interface, improving the response speed of the interface. Then, use the collaborative filtering algorithm based on Python to analyze the user's learning behavior data and realize personalized course recommendation. Use the NLTK and OpenCV libraries to realize intelligent correction of text homework and image homework respectively. Finally, the platform is applied in a middle school for a one - semester trial. The results show that the platform has stable performance, and the user satisfaction rate reaches 89.2%. The average learning score of students using the platform is 12.3% higher than that of students not using the platform, and the time for teachers to correct homework is reduced by 65% - 75%, which effectively improves the teaching and learning efficiency.
Keywords
Python; Web Framework; Django; Flask; Intelligent Education Platform; Personalized Recommendation Python - Driven Automation Testing Framework for Software Development Life Cycle
Abstract
The traditional manual testing method has the problems of low efficiency, high error rate, and difficulty in covering complex test scenarios. This paper proposes a Python - driven automation testing framework that covers the entire SDLC, including unit testing, integration testing, system testing, and regression testing. The framework uses Python's unittest and pytest libraries as the core testing tools, and integrates Selenium, Appium, and Requests libraries to realize automated testing of Web applications, mobile applications, and API interfaces. First, design a test case management module based on Excel and MySQL to realize the standardized management and version control of test cases. Then, use the Jenkins continuous integration tool to integrate the framework into the SDLC, realizing automatic triggering of tests after code submission. Finally, use the Allure library to generate visual test reports, which can clearly show the test results and defect information. The framework is applied in the development of a e - commerce platform. The results show that the framework can reduce the test cycle by 40% - 50%, the defect detection rate is increased by 35% compared with manual testing, and the repeatability and maintainability of test cases are significantly improved. The framework has good compatibility and can be applied to the automation testing of different types of software projects.
Keywords
Python; Automation Testing; Software Development Life Cycle; pytest; Selenium; Continuous Integration
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