Guide to Pandas, a fundamental Python library for data analysis, detailing its core functionalities and applications. It thoroughly explains Pandas' two primary data structures: Series and DataFrames, and covers essential data manipulation techniques like filtering, grouping, combining, and reshaping data.

The text also addresses critical aspects of data cleaning, handling missing values, and performing statistical analysis and aggregations.

Furthermore, it explores data visualization with Pandas' built-in tools and its integration with libraries like Matplotlib and Seaborn, and offers strategies for optimizing performance with large datasets, while outlining common troubleshooting issues and resources for continued learning.