Get max value across subset of rows and compare to constant to return max in new column-CodePudding

I am trying to create a new column in a dataframe that is the maximum value across two columns or a constant value. Whichever is the largest value will be returned to the new column.

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'loan_num': ['111', '333', '555', '777'],
    'bllnterm': [0, 240, 360, 240],
    'amortterm': [0, 360, 360, 360]
})

I have tried using pd.clip, np.maximum, and np.amax but none seem to run without throwing an error.

df = df.assign(amtz = df[['bllnterm', 'amortterm']].clip(lower=1, axis=1))

This returns a ValueError: Wrong number of items passed 2, placement implies 1

df = df.assign(amtz = np.maximum(df[['bllnterm', 'amortterm']], 1))

This returns a ValueError: Wrong number of items passed 2, placement implies 1

df = df.assign(amtz = np.amax(df[['bllnterm', 'amortterm']], axis=1, initial=1))

This returns a TypeError: max() got an unexpected keyword argument 'initial'. However, initial is a keyword in the docs so I'm not sure what is going on there.

My desired output looks like this:

loan_num     bllnterm    amortterm     amtz
----------------------------------------------
111            0            0           1
333            240          360         360
555            360          360         360
777            240          360         360

CodePudding user response：

You were on the right track, you need to combine max and clip:

df['amtz'] = df[['bllnterm', 'amortterm']].max(axis=1).clip(lower=1)

As assign:

df.assign(amtz=df[['bllnterm', 'amortterm']].max(axis=1).clip(lower=1))

output:

  loan_num  bllnterm  amortterm  amtz
0      111         0          0     1
1      333       240        360   360
2      555       360        360   360
3      777       240        360   360