Below is my dataframe.
| a | b |
|---|---|
| 12 | 0 |
| 1 | 21 |
| 0 | 0 |
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
| a | b | c |
|---|---|---|
| 12 | 0 | yes |
| 1 | 21 | yes |
| 0 | 0 | no |
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2 Answers
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')
Comments
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')
