pandas获取groupby分组里最大值所在的行方法

import pandas as pd
df = pd.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

df.groupby('Mt').apply(lambda t: t[t.Count==t.Count.max()])

print df.groupby(['Mt'])['Count'].agg(max)

idx=df.groupby(['Mt'])['Count'].transform(max)
print idx
idx1 = idx == df['Count']
print idx1

df[idx1]

Mt
s1 3
s2 10
s3 6
Name: Count, dtype: int64
0 3
1 3
2 10
3 10
4 10
5 6
dtype: int64
0 True
1 False
2 False
3 True
4 True
5 True
dtype: bool

idx = df.groupby('Mt')['Count'].idxmax()
print idx

df.iloc[idx]
Mt
s1 0
s2 3
s3 5
Name: Count, dtype: int64

df.iloc[df.groupby(['Mt']).apply(lambda x: x['Count'].idxmax())]

def using_apply(df):
 return (df.groupby('Mt').apply(lambda subf: subf['Value'][subf['Count'].idxmax()]))

def using_idxmax_loc(df):
 idx = df.groupby('Mt')['Count'].idxmax()
 return df.loc[idx, ['Mt', 'Value']]

print using_apply(df)

using_idxmax_loc(df)

Mt
s1 1
s2 4
s3 6
dtype: int64

df.sort('Count', ascending=False).groupby('Mt', as_index=False).first()