Data often resides in multiple files or structures. For effective analysis, you'll frequently need to combine these separate datasets. For instance, you might have user information in one table and their activity logs in another, requiring you to link them for a complete view.

This chapter focuses on the techniques Pandas provides for combining DataFrame objects. We will cover two main approaches:

1. Concatenation: Stacking DataFrames vertically or horizontally using pd.concat.
2. Merging/Joining: Performing database-style joins to combine DataFrames based on common columns or index labels using pd.merge and the .join method.

You will learn how different types of joins (inner, outer, left, right) affect the resulting combined DataFrame and how to apply these operations effectively based on your data's structure and analytical goals.