Your orders table has 50,000 rows. Your customers table has 8,000 rows. Your products table has 300 rows. The answer to your question lives across all three. Which customers in which cities bought which products at what price last month. No single table has everything. You need to combine them. That is what this post is about. Three Tools, Three Situations Pandas gives you three main ways to combine DataFrames. merge is for combining tables that share a common column, like a SQL join. This is what you will use 80% of the time. concat is for stacking DataFrames together, either adding more rows or more columns. Use it when you have data split across multiple files with the same structure. join is a convenience wrapper around merge that works on the index. Useful sometimes, but merge is more explicit and flexible. merge: The Core Operation import pandas as pd import numpy as np customers = pd .…