Raw datasets are often too granular. To find the average sales per region or the total users per month, you need to group data. Pandas uses the 'Split-Apply-Combine' strategy to make these calculations efficient and easy to write.
1Split-Apply-Combine
This is the core philosophy of grouping. First, you split the data into groups based on a key (like Category). Then, you apply a function (like sum or mean) to each group. Finally, you combine the results into a new DataFrame.
2Pivot Tables: High-Level Views
When you need to cross-tabulate data across multiple dimensions (e.g., Sales by Region AND Product), pivot tables provide a powerful Excel-like interface to summarize information in a 2D matrix.
