Pandas DataFrame Groupby two columns and get counts

I have a pandas dataframe in the following format:

    df = pd.DataFrame([[1.1, 1.1, 1.1, 2.6, 2.5, 3.4,2.6,2.6,3.4,3.4,2.6,1.1,1.1,3.3], list('AAABBBBABCBDDD'), [1.1, 1.7, 2.5, 2.6, 3.3, 3.8,4.0,4.2,4.3,4.5,4.6,4.7,4.7,4.8], ['x/y/z','x/y','x/y/z/n','x/u','x','x/u/v','x/y/z','x','x/u/v/b','-','x/y','x/y/z','x','x/u/v/w'],['1','3','3','2','4','2','5','3','6','3','5','1','1','1']]).T
    df.columns = ['col1','col2','col3','col4','col5']

df:

       col1 col2 col3     col4 col5
    0   1.1    A  1.1    x/y/z    1
    1   1.1    A  1.7      x/y    3
    2   1.1    A  2.5  x/y/z/n    3
    3   2.6    B  2.6      x/u    2
    4   2.5    B  3.3        x    4
    5   3.4    B  3.8    x/u/v    2
    6   2.6    B    4    x/y/z    5
    7   2.6    A  4.2        x    3
    8   3.4    B  4.3  x/u/v/b    6
    9   3.4    C  4.5        -    3
    10  2.6    B  4.6      x/y    5
    11  1.1    D  4.7    x/y/z    1
    12  1.1    D  4.7        x    1
    13  3.3    D  4.8  x/u/v/w    1

Now I want to group this by two columns like following:

    df.groupby(['col5','col2']).reset_index()

OutPut:

                 index col1 col2 col3     col4 col5
    col5 col2                                      
    1    A    0      0  1.1    A  1.1    x/y/z    1
         D    0     11  1.1    D  4.7    x/y/z    1
              1     12  1.1    D  4.7        x    1
              2     13  3.3    D  4.8  x/u/v/w    1
    2    B    0      3  2.6    B  2.6      x/u    2
              1      5  3.4    B  3.8    x/u/v    2
    3    A    0      1  1.1    A  1.7      x/y    3
              1      2  1.1    A  2.5  x/y/z/n    3
              2      7  2.6    A  4.2        x    3
         C    0      9  3.4    C  4.5        -    3
    4    B    0      4  2.5    B  3.3        x    4
    5    B    0      6  2.6    B    4    x/y/z    5
              1     10  2.6    B  4.6      x/y    5
    6    B    0      8  3.4    B  4.3  x/u/v/b    6

I want to get the count by each row like following. Expected Output:

    col5 col2 count
    1    A      1
         D      3
    2    B      2
    etc...

How to get my expected output? And I want to find largest count for each 'col2' value?

Followed by @Andy's answer, you can do following to solve your second question:

    In [56]: df.groupby(['col5','col2']).size().reset_index().groupby('col2')[[0]].max()
    Out[56]: 
          0
    col2   
    A     3
    B     2
    C     1
    D     3

From: stackoverflow.com/q/17679089