pandas：如何检查列中的某个值是否在每个组中重复最大值一次（在 groupby 之后）

Question

I have a pandas DataFrame which I want to group by column A, and check that a certain value ('test') in group B does not repeat more than once in each group.

Is there a pandas native way to do the following:
1 - find the groups where 'test' appears in column B more than once?
2 - delete the additional occurrences (keep the one with the min value in column C).

example:

    A   B       C
0   1   test    342
1   1   t       4556
2   1   te      222
3   1   test    56456
4   2   t       234525
5   2   te      123
6   2   test    23434
7   3   test    777
8   3   tes     665

if I groupby 'A', I get that 'test' appears twice in A==1, which is the case I would like to deal with.

Answer 1

Solution for remove duplicated test values by columns A,B - keep first value per group:

df = df[df.B.ne('test') | ~df.duplicated(['A','B'])]
print (df)
   A     B       C
0  1  test     342
1  1     t    4556
2  1    te     222
4  2     t  234525
5  2    te     123
6  2  test   23434
7  3  test     777
8  3   tes     665

EDIT: If need minimal C matched test in B and need all possible duplicated minimal C values compare by GroupBy.transform with replace C to NaN in Series.mask :

m = df.B.ne('test')
df = df[m | ~df.C.mask(m).groupby(df['A']).transform('min').ne(df['C'])]

But if need only first duplicated test value use DataFrameGroupBy.idxmin with filtered DataFrame:

m = df.B.ne('test')
m1 = df.index.isin(df[~m].groupby('A')['C'].idxmin())

df = df[m | m1]

Difference of solutions:

print (df)
    A     B       C
-2  1  test     342
-1  1  test     342
 0  1  test     342
 1  1     t    4556
 2  1    te     222
 3  1  test   56456
 4  2     t  234525
 5  2    te     123
 6  2  test   23434
 7  3  test     777
 8  3   tes     665
 
m = df.B.ne('test')
df1 = df[m | ~df.C.mask(m).groupby(df['A']).transform('min').ne(df['C'])]
print (df1)
    A     B       C
-2  1  test     342
-1  1  test     342
 0  1  test     342
 1  1     t    4556
 2  1    te     222
 4  2     t  234525
 5  2    te     123
 6  2  test   23434
 7  3  test     777
 8  3   tes     665

---

m = df.B.ne('test')
m1 = df.index.isin(df[~m].groupby('A')['C'].idxmin())

df2 = df[m | m1]
print (df2)
    A     B       C
-2  1  test     342
 1  1     t    4556
 2  1    te     222
 4  2     t  234525
 5  2    te     123
 6  2  test   23434
 7  3  test     777
 8  3   tes     665