Automating Data Analysis with Python: A Comparative Study of Popular Libraries and their Application

In areas like businesses, finance, and science, data analysis plays a very important role. Data analysis needs to be automated to improve productivity and eliminate human error as data volumes and complexity grow. Python is a well-liked language for automated data analysis because of its ease of use and broad library support. For automated data analysis, we are evaluating many well-known Python libraries, such as Python and Pandas, Matplotlib, Seaborn Scikit-learn, and TensorFlow is used in this text. We evaluate the usability, computational efficiency, and accuracy of these libraries when performing various data analysis tasks. We also go into great length on the uses of each library in various sectors, as well as its benefits and drawbacks. Our study shows that the user's level of experience and the type of data analysis task both affect the library choice. We came to the conclusion that choosing the right library is crucial for automating data analysis using Python and significantly affects the accuracy and efficacy of the study.