Good job! You may have noticed two interesting things.
1. Because both datasets contained a column named items_sold, pandas had to find a way to distinguish between them. It did it by renaming one of the columns to items_sold_x, and the other one to items_sold_y.
2. Only data from the years 2013-2015 were shown. Those years were the only periods when both store_a and store_b were open. Before 2013, there was only data for store_b. After 2015, there was only data for store_a. This type of merge is called an "inner merge". If, instead, you want to see all data from both files, you should use an "outer" merge in the following way:
stores_comparison = store_a.merge(store_b, left_on='year', right_on='period', how='outer')
Note that we added the argument how=outer at the end. The image below illustrates the difference between "inner" and "outer" merge:
Change your code so that it uses an "outer" merge type.
As you can see, we now have all data in place. Missing values have been replaced with NaNs.
Add: how='outer'.
