How do I find all rows in a pandas DataFrame which have the max value for count
column, after grouping by ['Sp','Mt']
columns?
Example 1: the following DataFrame:
Sp Mt Value count
0 MM1 S1 a **3**
1 MM1 S1 n 2
2 MM1 S3 cb **5**
3 MM2 S3 mk **8**
4 MM2 S4 bg **10**
5 MM2 S4 dgd 1
6 MM4 S2 rd 2
7 MM4 S2 cb 2
8 MM4 S2 uyi **7**
Expected output is to get the result rows whose count is max in each group, like this:
Sp Mt Value count
0 MM1 S1 a **3**
2 MM1 S3 cb **5**
3 MM2 S3 mk **8**
4 MM2 S4 bg **10**
8 MM4 S2 uyi **7**
Example 2:
Sp Mt Value count
4 MM2 S4 bg 10
5 MM2 S4 dgd 1
6 MM4 S2 rd 2
7 MM4 S2 cb 8
8 MM4 S2 uyi 8
Expected output:
Sp Mt Value count
4 MM2 S4 bg 10
7 MM4 S2 cb 8
8 MM4 S2 uyi 8
Firstly, we can get the max count for each group like this:
In [1]: df
Out[1]:
Sp Mt Value count
0 MM1 S1 a 3
1 MM1 S1 n 2
2 MM1 S3 cb 5
3 MM2 S3 mk 8
4 MM2 S4 bg 10
5 MM2 S4 dgd 1
6 MM4 S2 rd 2
7 MM4 S2 cb 2
8 MM4 S2 uyi 7
In [2]: df.groupby(['Sp', 'Mt'])['count'].max()
Out[2]:
Sp Mt
MM1 S1 3
S3 5
MM2 S3 8
S4 10
MM4 S2 7
Name: count, dtype: int64
To get the indices of the original DF you can do:
In [3]: idx = df.groupby(['Sp', 'Mt'])['count'].transform(max) == df['count']
In [4]: df[idx]
Out[4]:
Sp Mt Value count
0 MM1 S1 a 3
2 MM1 S3 cb 5
3 MM2 S3 mk 8
4 MM2 S4 bg 10
8 MM4 S2 uyi 7
Note that if you have multiple max values per group, all will be returned.
Update
On a Hail Mary chance that this is what the OP is requesting:
In [5]: df['count_max'] = df.groupby(['Sp', 'Mt'])['count'].transform(max)
In [6]: df
Out[6]:
Sp Mt Value count count_max
0 MM1 S1 a 3 3
1 MM1 S1 n 2 3
2 MM1 S3 cb 5 5
3 MM2 S3 mk 8 8
4 MM2 S4 bg 10 10
5 MM2 S4 dgd 1 10
6 MM4 S2 rd 2 7
7 MM4 S2 cb 2 7
8 MM4 S2 uyi 7 7