
        
        
            
                
                
                    df = pd.DataFrame({"price":[500,300,700, 200,900], "brand": ["apple", "google", "apple", "google","apple"], "device":["phone","phone","computer","phone","phone"]}, index=["a","b","c","d","e"])
df
                
            
               price  brand   device
a  500    apple   phone
b  300    google  phone
c  700    apple   computer
d  200    google  phone
e  900    apple   phone

Solution

To get the top 2 priciest products of each brand:


        
        
            
                
                
                    df.sort_values("price", ascending=False).groupby("brand").head(2)   # returns a DataFrame
                
            
               price  brand   device
e  900    apple   phone
c  700    apple   computer
b  300    google  phone
d  200    google  phone

Explanation

We first sort by price in descending order using sort_values(~):


        
        
            
                
                
                    df.sort_values("price", ascending=False)
                
            
               price  brand   device
e  900    apple   phone
c  700    apple   computer
a  500    apple   phone
b  300    google  phone
d  200    google  phone

Next, we group by brand, and the key here is that groupby(~) preserves order. This means that even after we group by brand, the rows in every group will still be sorted by price. For this very reason, calling head(2) would return the 2 priciest device in each group.

Published by Isshin Inada

Edited by 0 others

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