Count elements in a row and create column counter in pandas

I have created the following pandas dataframe:

import pandas as pd

ds = {'col1' : ['A','A','B','C','C','D'],
      'col2' : ['A','B','C','D','D','A']}

df = pd.DataFrame(data=ds)

The dataframe looks like this:

print(df)

  col1 col2
0    A    A
1    A    B
2    B    C
3    C    D
4    C    D
5    D    A

The possible values in col1 and col2 are A, B, C and D.

I need to create 4 new columns, called:

• countA: it counts how many A are in each row / record
• countB: it counts how many B are in each row / record
• countC: it counts how many C are in each row / record
• countD: it counts how many D are in each row / record

So, from the example above, the resulting dataframe would look like this:

Can anyone help me please?

Here is a way using pd.get_dummies()

df.join(pd.get_dummies(df,prefix='',prefix_sep='').T.groupby(level=0).sum().T.rename('count{}'.format,axis=1))

and here is a way using value_counts()

df.join(df.stack().groupby(level=0).value_counts().unstack(fill_value = 0).rename('count{}'.format,axis=1))

Output:

  col1 col2  countA  countB  countC  countD
0    A    A       2       0       0       0
1    A    B       1       1       0       0
2    B    C       0       1       1       0
3    C    D       0       0       1       1
4    C    D       0       0       1       1
5    D    A       1       0       0       1