Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

Inner Join

Anytime you perform a join, you need a unique column to join the data with. In our case, we can use the names column to join the ratings DataFrame with our original DataFrame. Let’s perform an inner join on these two datasets so that we can connect users with their ratings:

# performing an inner join with our df and ratings DataFrames based on names, get data that matches matched_ratings = df.merge(ratings, on="names", how="inner") matched_ratings.head( )

Go ahead and run the cell. We’ll get an output that looks like Table 10-5:
CHapter 10 INtroduCtIoN to data aNalYsIs
Table 10-5. Joining DataFrames to view customer ratings and ages together

ages	names	tenure	age_group	ratings
0 20	tyler	6	teenager	9
1 25	Jess	5	adult	10
2 29	ted	8	adult	6

Using the merge method, we were able to perform a join. By specifying the how parameter to “inner,” we were able to return a DataFrame with only those records who posted a rating. We could do a lot more with this data now than before. We could calculate average age of customers who gave us a rating, average rating per age group, etc. Joins will always help to connect separate DataFrames together, which helps especially when working with databases.