Pandas：根据组聚合添加新列

Question

I am a newbie with Pandas data frame and I need some help.

Let's say that I have a DataFrame df :

>>print(df)
        ID    Score       
0       AA      100       
1       AA       10        
2       BB       50 
3       BB      -20
4       BB        0
5       AA      200

And I want to add a new column with value = 1. If it is the lowest score for the ID, and 0 else :

>> print(df_out)
        ID    Score  IsLowestScoreID     
0       AA      100                0
1       AA       10                1
2       BB       50                0
3       BB      -20                1
4       BB        0                0
5       AA      200                0

What is the correct way to achieve such a thing?

Answer 1

You can compare by Series.eq column Score with Series created by transform and min , then cast mask to integer for True/False to 1/0 mapping:

df['IsLowestScoreID'] = df['Score'].eq(df.groupby('ID')['Score'].transform('min')).astype(int)
print (df)
   ID  Score  IsLowestScoreID
0  AA    100                0
1  AA     10                1
2  BB     50                0
3  BB    -20                1
4  BB      0                0
5  AA    200                0

Alternative is use numpy.where for specify values by mask:

mask = df['Score'].eq(df.groupby('ID')['Score'].transform('min'))
df['IsLowestScoreID'] = np.where(mask, 1, 0)

Detail :

print (df.groupby('ID')['Score'].transform('min'))
0    10
1    10
2   -20
3   -20
4   -20
5    10
Name: Score, dtype: int64