[英]determine column maximum value per another column in pandas dataframe
I have a dataframe contains location Id, store name and store revenue.我有一个包含位置 ID、商店名称和商店收入的数据框。 I want to determine the store that has the maximum revenue per area
我想确定每个区域收入最高的商店
I wrote a code for that, but not sure if there is a better way to handle this case我为此写了一个代码,但不确定是否有更好的方法来处理这种情况
import pandas as pd
dframe=pd.DataFrame({"Loc_Id":[1,2,2,1,2,1,3,3],"Store":["A","B","C","B","D","B","A","C"],
"Revenue":[50,70,45,35,80,70,90,65]})
#group by location id, then save max per location in new column
dframe["max_value"]=dframe.groupby("Loc_Id")["Revenue"].transform(max)
#create new column by checking if the revenue equal to max revenue
dframe["is_loc_max"]=dframe.apply(lambda x: 1 if x["Revenue"]==x["max_value"] else 0,axis=1)
#drop the intermediate column
dframe.drop(columns=["max_value"],inplace=True)
and This is the required output:这是所需的输出:
is there a better way to get this output有没有更好的方法来获得这个输出
Create boolean mask by compare by eq
( ==
) and convert it to integer
s - 0, 1
to False, True
:通过
eq
( ==
) 比较创建布尔掩码并将其转换为integer
s - 0, 1
到False, True
:
s = dframe.groupby("Loc_Id")["Revenue"].transform('max')
dframe["max_value"]= s.eq(dframe["Revenue"]).astype(int)
print (dframe)
Loc_Id Store Revenue max_value
0 1 A 50 0
1 2 B 70 0
2 2 C 45 0
3 1 B 35 0
4 2 D 80 1
5 1 B 70 1
6 3 A 90 1
7 3 C 65 0
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