Below is my dataframe.

Now want to add a column name 'c' which return yes when a or b is non-zero and return no when a and b is zero

CodePudding user response：

If need test all columns compare by 0 and test if all values per row by DataFrame.all with set yes, 'no' by numpy.where:

df['c'] = np.where(df.eq(0).all(axis=1), 'no','yes')

print (df)
    a   b    c
0  12   0  yes
1   1  21  yes
2   0   0   no

Another idea is test if at leastone value is not 0 by DataFrame.any, then is used mapping:

df['c'] = df.ne(0).any(axis=1).map({False: 'no',True:'yes'})

If possible multiple columns and need test only a,b columns:

cols = ['a','b']
df['c'] = np.where(df[cols].eq(0).all(axis=1), 'no','yes')

CodePudding user response：

Here is the simplest solution I can think of:

import numpy as np
df['c'] = np.where( (df['a']>0) | (df['b']>0), 'yes', 'no')