Pandas DataFrame Groupby two columns and get counts

Question

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?

Answer 1

You are looking for size:

In [11]: df.groupby(['col5', 'col2']).size() Out[11]: col5  col2 1     A       1       D       3 2     B       2 3     A       3       C       1 4     B       1 5     B       2 6     B       1 dtype: int64 

---

To get the same answer as waitingkuo (the "second question"), but slightly cleaner, is to groupby the level:

In [12]: df.groupby(['col5', 'col2']).size().groupby(level=1).max() Out[12]: col2 A       3 B       2 C       1 D       3 dtype: int64