Pandas GroupBy : How to get top n values based on a column

Question

forgive me if this is a basic question but i am new to pandas. I have a dataframe with with a column A and i would like to get the top n rows based on the count in Column A. For instance the raw data looks like

A  B  C
x 12  ere
x 34  bfhg
z 6   bgn
z 8   rty
y 567 hmmu,,u
x 545 fghfgj
x 44  zxcbv

Note that this is just a small sample of the data that i am actually working with.

So if we look at Column A, value x appears 4 times,y appears 2 times and z appears 1 time. How can i get the top n values for Column A based on this count?

print df.groupby(['A']).sum()

this gives me

A      B

x      6792117

but when i do

print len(df.groupby(['A']).get_group('x'))

i get

21

furthermore

len(df.index) 

gives me

23657

so how can the count of 'A' == 'x' be 6792117 as seen in the result of group by? what am i missing?

Update

consider

print df.groupby(['A']).describe()

gives me

     Tags           DocID

x    count      21.000000
     mean   323434.142857
     std     35677.410292
     min    266631.000000
     25%    292054.000000
     50%    325575.000000
     75%    347450.000000
     max    380286.000000

which makes sense. i just want to get the row which has the max count as per column A.

Update2

i did

print df.groupby(['A'],as_index=False).count()

i get

         A       B      C
0        x       21     21
1        y       11     11
2        z        8      8

so basically, for Column A, tag x has 21 entries in Column B and 21 in Column C. ColumnsB and C are unique in my case. which is good. now how do i get the top n rows with respect to column C?

Update3

So i tried

import heapq
print heapq.nlargest(3,df.groupby(['A'],as_index=False).count()['C'])

and i get

[151, 85, 72]

so i know that for Column A, i have the above counts as the top 3 counts. But i still dont know which value of Column A do these counts refer to? For example which value in Column A has a count of 151? Is there any way to link this information?

Answer 1

IIUC you can use function nlargest .

I try your sample data and get top 2 rows by column C :

print df
   A    B        C
0  x   12      ere
1  x   34     bfhg
2  z    6      bgn
3  z    8      rty
4  y  567  hmmu,,u
5  x  545   fghfgj
6  x   44    zxcbv

dcf = df.groupby(['A'],as_index=False).count()
print dcf
   A  B  C
0  x  4  4
1  y  1  1
2  z  2  2

#get 2 largest rows by column C
print dcf.nlargest(2,'C')
   A  B  C
0  x  4  4
2  z  2  2

Answer 2

one approach that i tried

import heapq

dcf =  df.groupby(['A'],as_index=False).count()
print dcf.loc[dcf['C'].isin(heapq.nlargest(5,dcf['C']))].sort(['C'],ascending=False)

gives me

      A       B      C
1664  g       151    151
1887  k       85     85
1533  q       72     72
53    y       68     68
1793  t       62     62

verified by

print len(df.loc[df["A"]=="g"])

gives me

151

so i get the desired results as i can see the top 5 values based on the count from Column A. but surely there must be a better way of doing this?