Get the min of [0, x] element wise for a column

Question

I need to compute a column where the value is the result of a vectorized operation over other columns:

df["new_col"] = df["col1"] - min(0,df["col2"])

It turned out, however, that I cannot use min as in the above syntax. So, what is the right way to get the min between zero and a given value of pandas column?

Answer 1

you can use numpy.minimum to find the element-wise minimum of an array

import numpy as np
df["new_col"] = df["col1"] - np.minimum(0,df["col2"])

Answer 2

You could use some masking and a temporary column. Totally ignoring the 'min' function.

magicnumber = 0
tempcol = df['col2']
mask = tempcol < magicnumber
tempcol.loc[df[~mask].index] = magicnumber
df['col1'] - tempcol

Or you can use a lambda function:

magicnumber = 0
df['col1'] - df['col2'].apply(lambda x: np.min(magicnumber, x))

OR you can apply over two columns:

df['magicnumber'] = 0
df['col1'] - df[['col2', 'magicnumber']].apply(np.min, axis=1)