Python's Pandas library is a widely used open-source tool for analyzing and manipulating data. Its user-friendly data structures, including DataFrames, make it easy to handle structured data effectively. With Pandas, you can import data from CSV, Excel, and SQL databases and arrange it into two-dimensional labeled data structures called dataframes, which resemble tables with rows representing observations or records and columns representing variables or attributes. The library offers a broad range of functionalities to manipulate and transform data, including filtering, sorting, grouping, joining, and aggregating data, handling missing values, and performing mathematical computations. It also has powerful data visualization capabilities, enabling you to create plots and charts directly from the data. Pandas integrate well with other Python libraries used in data analysis, such as NumPy for numerical computations and Matplotlib or Seaborn for data visualization. It is widely used in