[FIXED] How to satisfy the condition of 2 columns different rows at the same time

Issue

My logic is like this:

cond2 column is true before expected column, and cond1 column is true before cond2 column, then expected column can be true

input

import pandas as pd
import numpy as np
d={'cond1':[False,False,True,False,False,False,False,True,False,False],'cond2':[False,True,False,True,True,False,False,False,True,False]}
df = pd.DataFrame(d)

expected result table

    cond1   cond2   expected
0   FALSE   FALSE   
1   FALSE   TRUE    
2   TRUE    FALSE   
3   FALSE   TRUE    
4   FALSE   TRUE    
5   FALSE   FALSE   TRUE
6   FALSE   FALSE   TRUE
7   TRUE    FALSE   
8   FALSE   TRUE    
9   FALSE   FALSE   TRUE

I have such an idea

get the number of lines from cond1 is true to the present, and then use the cumsum function to calculate the number of lines where cond2 is true is greater than 0

But how to get the number of lines from cond1 is true to the present

Solution

The description is not fully clear. It looks like you need a cummax per group starting with True in cond1:

m = df.groupby(df['cond1'].cumsum())['cond2'].cummax()

df['expected'] = df['cond2'].ne(m)

Output:

   cond1  cond2  expected
0  False  False     False
1  False   True     False
2   True  False     False
3  False   True     False
4  False   True     False
5  False  False      True
6  False  False      True
7   True  False     False
8  False   True     False
9  False  False      True

Answered By - mozway