group by pandas removes duplicates

Question

I have a dataframe (df)

a     b     c
1     2     20
1     2     15
2     4     30
3     2     20
3     2     15

and I want to recognize only max values from column c

I tried

a = df.loc[df.groupby('b')['c'].idxmax()] 

but it group by removes duplicates so I get

    a     b     c
    1     2     20
    2     4     30

it removes rows 3 because they are the same was rows 1.

Is it any way to write the code to not remove duplicates?

Answer 1

Just also take column a into account when you do the groupby :

a = df.loc[df.groupby(['a', 'b'])['c'].idxmax()]

   a  b   c
0  1  2  20
2  2  4  30
3  3  2  20

Answer 2

I think you need:

df = df[df['c'] == df.groupby('b')['c'].transform('max')] 
print (df)
   a  b   c
0  1  2  20
2  2  4  30
3  3  2  20

Difference in changed data:

print (df)
   a  b   c
0  1  2  30
1  1  2  30
2  1  2  15
3  2  4  30
4  3  2  20
5  3  2  15

#only 1 max rows per groups a and b
a = df.loc[df.groupby(['a', 'b'])['c'].idxmax()]
print (a)
   a  b   c
0  1  2  30
3  2  4  30
4  3  2  20

#all max rows per groups b
df1 = df[df['c'] == df.groupby('b')['c'].transform('max')] 
print (df1)
   a  b   c
0  1  2  30
1  1  2  30
3  2  4  30

#all max rows per groups a and b
df2 = df[df['c'] == df.groupby(['a', 'b'])['c'].transform('max')] 
print (df2)
   a  b   c
0  1  2  30
1  1  2  30
3  2  4  30
4  3  2  20