[FIXED] compare strings in the same row but in different columns and catch in which column they are not equal

Issue

I have a Dataset like this:

dictionary = {'Month1': ['C1','C2',0,0,'C5'], 'Month2': ['C1','C2','C3','C4',0], 'Month3': ['C1','C2','C3','C4',0], 'Month4' : [0,'C2','C3',0,0]}
df = pd.DataFrame(dictionary)


    Month1  Month2  Month3  Month4
0   C1        C1     C1       0
1   C2        C2     C2       C2
2   0         C3     C3       C3
3   0         C4     C4        0
4   C5         0      0        0

I want to compare each row between all the columns of this DataFrame, i.e. compare if df.loc[0,'Month1'] is equal to: df.loc[0,'Month2'], df.loc[0,'Month3'] and df.loc[0,'Month2']. I want to do this because my goal with this comparison is to know from and to what month there is the same string, I would like to put the initial month that the value appear in one column and the last month that the string is repeated in other one. Someone like this:

    Month1  Month2  Month3  Month4  Firts appear last appear
0   C1        C1     C1        0        Month1      Month3
1   C2        C2     C2       C2        Month1      Month4
2   0         C3     C3       C3        Month2      Month4
3   0         C4     C4        0        Month2      Month3
4   C5         0      0        0        Month1      Month1

I'm thinking in uses np.where() or a loop but I really don't know how to do it. Please help me

Solution

Use idxmax

>>> df.assign(first_appear=df.ne(0).idxmax(1),
              last_appear=df.loc[:, df.columns[::-1]].ne(0).idxmax(1))

    Month1  Month2  Month3  Month4  Firts appear last appear
0   C1        C1     C1        0        Month1      Month3
1   C2        C2     C2       C2        Month1      Month4
2   0         C3     C3       C3        Month2      Month4
3   0         C4     C4        0        Month2      Month3
4   C5         0      0        0        Month1      Month1

Answered By - rafaelc