我认为您需要add
reset_index，然后将参数设置
ascending=False为，
sort_values因为
sort返回：
FutureWarning：不建议使用sort（columns = ....），请使用sort_values（by =
.....）.sort_values（[‘count’]，ascending = False）
df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] .count() .reset_index(name='count') .sort_values(['count'], ascending=False) .head(5)
样品：
df = pd.Dataframe({'STNAME':list('abscscbcdbcsscae'), 'CTYNAME':[4,5,6,5,6,2,3,4,5,6,4,5,4,3,6,5]})print (df) CTYNAME STNAME0 4 a1 5 b2 6 s3 5 c4 6 s5 2 c6 3 b7 4 c8 5 d9 6 b10 4 c11 5 s12 4 s13 3 c14 6 a15 5 edf = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] .count() .reset_index(name='count') .sort_values(['count'], ascending=False) .head(5)print (df) STNAME count2 c 55 s 41 b 30 a 23 d 1但似乎您需要
Series.nlargest：
df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].count().nlargest(5)
要么：
df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].size().nlargest(5)
size和之间的区别count是：size计NaN数值，count不计数。
样品：
df = pd.Dataframe({'STNAME':list('abscscbcdbcsscae'), 'CTYNAME':[4,5,6,5,6,2,3,4,5,6,4,5,4,3,6,5]})print (df) CTYNAME STNAME0 4 a1 5 b2 6 s3 5 c4 6 s5 2 c6 3 b7 4 c8 5 d9 6 b10 4 c11 5 s12 4 s13 3 c14 6 a15 5 edf = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] .size() .nlargest(5) .reset_index(name='top5')print (df) STNAME top50 c 51 s 42 b 33 a 24 d 1
