I have sample dataframe like

_id    Dept    Result
1234    dept1   NaN
1235    dept2   Good
1235    dept2   Bad
1235    dept2   Good
1235    dept2   none
1235    dept2   none

I want to convert it into

_id     Name   Good Bad none
1234    dept1   0   0   5
1235    dept2   2   1   2

I am able to do it with for loop. As I have too much data so using for loop is slow, need some optimised solution. Can it be achieved by using some dataframe function

CodePudding user response：

Use crosstab and for prevent remove None column replace NaN and Nones by same value, e.g. non:

df = pd.crosstab([df['_id'], df['Dept']], df['Result'].fillna('non'))
print (df)
Result      Bad  Good  non
_id  Dept                 
1234 dept1    0     0    1
1235 dept2    1     2    2