Count and Sort with Pandas

I think you need add reset_index, then parameter ascending=False to sort_values because sort return:

FutureWarning: sort(columns=….) is deprecated, use 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)

Sample:

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 STNAME
0         4      a
1         5      b
2         6      s
3         5      c
4         6      s
5         2      c
6         3      b
7         4      c
8         5      d
9         6      b
10        4      c
11        5      s
12        4      s
13        3      c
14        6      a
15        5      e

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] \
                             .count() \
                             .reset_index(name="count") \
                             .sort_values(['count'], ascending=False) \
                             .head(5)

print (df)
  STNAME  count
2      c      5
5      s      4
1      b      3
0      a      2
3      d      1

But it seems you need Series.nlargest:

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].count().nlargest(5)

or:

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'].size().nlargest(5)

The difference between size and count is:

size counts NaN values, count does not.

Sample:

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 STNAME
0         4      a
1         5      b
2         6      s
3         5      c
4         6      s
5         2      c
6         3      b
7         4      c
8         5      d
9         6      b
10        4      c
11        5      s
12        4      s
13        3      c
14        6      a
15        5      e

df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME']
                             .size()
                             .nlargest(5)
                             .reset_index(name="top5")
print (df)
  STNAME  top5
0      c     5
1      s     4
2      b     3
3      a     2
4      d     1