A similar question is asked here:
Python : Getting the Row which has the max value in groups using groupby

However, I just need one record per group even if there are more than one record with maximum value in that group.

In the example below, I need one record for “s2”. For me it doesn’t matter which one.

>>> df = DataFrame({'Sp':['a','b','c','d','e','f'], 'Mt':['s1', 's1', 's2','s2','s2','s3'], 'Value':[1,2,3,4,5,6], 'count':[3,2,5,10,10,6]})
>>> df
   Mt Sp  Value  count
0  s1  a      1      3
1  s1  b      2      2
2  s2  c      3      5
3  s2  d      4     10
4  s2  e      5     10
5  s3  f      6      6
>>> idx = df.groupby(['Mt'])['count'].transform(max) == df['count']
>>> df[idx]
   Mt Sp  Value  count
0  s1  a      1      3
3  s2  d      4     10
4  s2  e      5     10
5  s3  f      6      6
>>> 

You can use first

In [14]: df.groupby('Mt').first()
Out[14]: 
   Sp  Value  count
Mt                 
s1  a      1      3
s2  c      3      5
s3  f      6      6

Update

Set as_index=False to achieve your goal

In [28]: df.groupby('Mt', as_index=False).first()
Out[28]: 
   Mt Sp  Value  count
0  s1  a      1      3
1  s2  c      3      5
2  s3  f      6      6 

Update Again

Sorry for misunderstanding what you mean. You can sort it first if you want the one with max count in a group

In [196]: df.sort('count', ascending=False).groupby('Mt', as_index=False).first()
Out[196]: 
   Mt Sp  Value  count
0  s1  a      1      3
1  s2  e      5     10
2  s3  f      6      6

To get first occurence of maximum count you can use pandas.DataFrame.idxmax() function:

>>> df.iloc[df.groupby(['Mt']).apply(lambda x: x['count'].idxmax())]
   Mt Sp  Value  count
0  s1  a      1      3
3  s2  d      4     10
5  s3  f      6      6

Playing off of Roman Pekar’s answer, I found that that the following code would work:

from math import isnan
df.iloc[[int(x) for x in df.groupby(by=df.Mt).apply(lambda x: x['count'].idxmax()).values if not isnan(y)]]

Note the isnan condition, as my application had some nan entries in the column we are maximizing over.