用熊猫计数和排序

我认为您需要add

reset_index

ascending=False

sort_values

sort

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