How to sum over some columns based on condition in pandas

I have a data frame like this:

mydf = {'p1':[0.1, 0.2, 0.3], 'p2':[0.2, 0.1,0.3], 'p3':[0.1,0.9, 0.01], 'p4':[0.11, 0.2, 0.4], 'p5':[0.3, 0.1,0.5],
        'w1':['cancel','hello', 'hi'], 'w2':['good','bad','ugly'], 'w3':['thanks','CUSTOM_MASK','great'],
        'w4':['CUSTOM_MASK','CUSTOM_UNKNOWN', 'trible'],'w5':['CUSTOM_MASK','CUSTOM_MASK','job']}
df = pd.DataFrame(mydf)

So what I need to do is to sum up all values in column p1,p2,p3,p4,p5 if the correspondent values in w1,w2,w3,w4,w5 is not CUSTOM_MASK or CUSTOM_UNKNOWN.

So the result would be to add a column to the data frame like this: (0.1+0.2+0.1=0.4 is for the first row).

top_p
0.4
0.3
1.51

So my question is that is there any pandas way to do this?

What I have done so far is to loop through the rows and then columns and check the values (CUSTOM_MASK, CUSTOM_UNKNOWN) and then sum it up if those values was not exist in the columns.

>Solution :

You can use mask. The idea is to create a boolean mask with the w columns, and use it to filter the relevant w columns and sum:

df['top_p'] = df.filter(like='p').mask(df.filter(like='w').isin(['CUSTOM_MASK','CUSTOM_UNKNOWN']).to_numpy()).sum(axis=1)

Output:

    p1   p2    p3    p4   p5      w1    w2           w3              w4           w5  top_p
0  0.1  0.2  0.10  0.11  0.3  cancel  good       thanks     CUSTOM_MASK  CUSTOM_MASK   0.40
1  0.2  0.1  0.90  0.20  0.1   hello   bad  CUSTOM_MASK  CUSTOM_UNKNOWN  CUSTOM_MASK   0.30
2  0.3  0.3  0.01  0.40  0.5      hi  ugly        great          trible          job   1.51

Before summing, the output of mask looks like:

    p1   p2    p3   p4   p5
0  0.1  0.2  0.10  NaN  NaN
1  0.2  0.1   NaN  NaN  NaN
2  0.3  0.3  0.01  0.4  0.5