In the previous example, the merge function “knew” how to combine tables based on the columns that were the same between two tables. For instance, products and orders both had a column called product_id. This won’t always be true when we want to perform a merge.
Generally, the products and customers DataFrames would not have the columns product_id or customer_id. Instead, they would both be called id and it would be implied that the id was the product_id for the products table and customer_id for the customers table. They would look like this:
Customers
| id | customer_name | address | phone_number |
|---|---|---|---|
| 1 | John Smith | 123 Main St. | 212-123-4567 |
| 2 | Jane Doe | 456 Park Ave. | 949-867-5309 |
| 3 | Joe Schmo | 798 Broadway | 112-358-1321 |
Products
| id | description | price |
|---|---|---|
| 1 | thing-a-ma-jig | 5 |
| 2 | whatcha-ma-call-it | 10 |
| 3 | doo-hickey | 7 |
| 4 | gizmo | 3 |
**How would this affect our merges?**
Because the id columns would mean something different in each table, our default merges would be wrong.
One way that we could address this problem is to use .rename to rename the columns for our merges. In the example below, we will rename the column id to customer_id, so that orders and customers have a common column for the merge.
Instructions
Merge orders and products using rename. Save your results to the variable orders_products.
Display orders_products using print.