This chapter added to our arsenal of pandas tricks to aggregate, join, and transform data. Here is a quick recap of the chapter:

• groupby creates groups of rows – one group for each category in a categorical variable (or a combination of categories across categorical variables).
• Using groupby, the same analysis can be performed on different groups efficiently.
• Similarly shaped DataFrames can be concatenated or appended to perform analysis simultaneously for the entire dataset.
• SQL-like joining or merging between DataFrames is also possible.
• Wide data can be made longer, or vice versa, depending on the requirement.
• pandas can handle multi-index data and there are functions to convert multi-index data to single-index data and vice versa.
• Spreadsheet operations such as pivot tables and transposes are possible and provide more flexibility than in spreadsheets.

In the next chapter, we will discuss and elaborate on the methods, syntax, and usage of some of these special data operations in pandas.