In this lesson, we'll keep exploring the power of the groupby function in the Pandas library. Groupby is a crucial tool for data analysis, allowing us to split data into different groups and then apply aggregates to those groups. This can be very useful in numerous real-life applications, such as summarizing sales data by product and region or understanding passenger statistics in a Titanic dataset.

Our goal today is to understand how to use the groupby function in Pandas for more advanced, multi-level aggregations. We'll work through an example involving grouping by multiple columns and applying multiple aggregation functions to several fields.

Before diving into complex groupby operations, let's review the basics. The groupby function in Pandas is used to split the data into groups based on some criteria. You can then apply various aggregation functions to these groups.

Let's start with a basic example. Suppose we have a simple dataset about students and their scores.

In this example, we grouped the DataFrame by student and calculated the mean score for each student. This is a fundamental operation that helps in summarizing the data efficiently.