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Getting n rows with smallest column value in each group in Pandas

schedule Aug 10, 2023
Last updated
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Example

Consider the following DataFrame about some products:

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 cheapest products of each brand:

df.sort_values("price", ascending=True).groupby("brand").head(2)
price brand device
d 200 google phone
b 300 google phone
a 500 apple phone
c 700 apple computer

Explanation

We first sort by price in ascending order:

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

Next, we group by brand, and the what's important 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 cheapest device in each group.

robocat
Published by Isshin Inada
Edited by 0 others
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