Data analysis and visualization involve the process of examining and interpreting data to gain insights and make informed decisions. One of the key tools used in this process is the pandas library, which is a powerful and flexible tool for data manipulation in Python. Pandas provides data structures such as DataFrames, which are ideal for handling structured data. With pandas, you can easily clean, filter, and transform data, allowing for efficient handling of large datasets. It also integrates seamlessly with other libraries used for data analysis and visualization, like Matplotlib and Seaborn, enabling the creation of informative charts and graphs. By leveraging these capabilities, you can turn raw data into actionable insights and present them in a visually compelling way.