Pandas apply but only for rows where a condition is met
The other answers are excellent, but I thought I'd add one other approach that can be faster in some circumstances – using broadcasting and masking to achieve the same result:
import numpy as npmask = (z['b'] != 0)z_valid = z[mask]z['c'] = 0z.loc[mask, 'c'] = z_valid['a'] / np.log(z_valid['b'])Especially with very large dataframes, this approach will generally be faster than solutions based on apply().
You can just use an if statement in a lambda function.
z['c'] = z.apply(lambda row: 0 if row['b'] in (0,1) else row['a'] / math.log(row['b']), axis=1)I also excluded 1, because log(1) is zero.
Output:
a b c0 4 6 2.2324431 5 0 0.0000002 6 5 3.7280103 7 0 0.0000004 8 1 0.000000