How to apply single condition to a list of columns in a dataframe and add value to 4th column without using multiple OR's

Question

I need to apply a single condition to 3 columns of a dataframe and change value of 4th without using or statement .

I can do with np.where but if the no of columns is big it's going to take a lot of time

import pandas as pd
import numpy as np

df = pd.DataFrame({'a':[1,2,3,4],'b':[1,3,6,7],'c':[4,6,4,1], 'd':['p','f','p','u'],'e':['a','a','b','c']})

df['d'] = np.where(df.a > 4 | df.b > 4 | df.c > 4 , 'p',df['d']) 

df = pd.DataFrame({'a':[1,2,3,4,5],'b':[1,3,6,7],'c':[4,6,4,1], 'd':['p','f','p','f']})
df['d']=np.where(df.a > 4 | df.b > 4 | df.c > 4 , 'p','f') 

I need someway of implementing same condition > , < to list of columns without using or for each.

Answer 1

Use DataFrame.gt along with np.where:

import numpy as np
import pandas as pd 

df = pd.DataFrame({'a':[1,2,3,4],'b':[1,3,6,7],'c':[4,6,4,1], 'd':['p','f','p','u'],'e':['a','a','b','c']})

# create a subset of a dataframe on which you want to check condition
new_df = df[['a','b','c']]
mask = new_df.gt(4).any(axis=1)  # check if any value is greater than 4

df['d'] = np.where(mask, 'p','f')

print(df)

Output:

   a  b  c  d  e                                                                                                                      
0  1  1  4  f  a                                                                                                                      
1  2  3  6  p  a                                                                                                                      
2  3  6  4  p  b                                                                                                                      
3  4  7  1  p  c