Apply if else condition in specific pandas column by location

I am trying to apply a condition to a pandas column by location and am not quite sure how. Here is some sample data:

 data = {'Pop': [728375, 733355, 695395, 734658, 732811, 789396, 727761, 751967],
    'Pop2': [728375, 733355, 695395, 734658, 732811, 789396, 727761, 751967]}

 PopDF = pd.DataFrame(data)
 remainder = 6

#I would like to subtract 1 from PopDF['Pop2'] column cells 0-remainder.
#The remaining cells in the column I would like to stay as is (retain original pop values).

1. PopDF['Pop2']= PopDF['Pop2'].iloc[:(remainder)]-1
   

2. PopDF['Pop2'].iloc[(remainder):] = PopDF['Pop'].iloc[(remainder):]
   

The first line works to subtract 1 in the correct locations, however, the remaining cells become NaN. The second line of code does not work – the error is:

ValueError: Length of values (1) does not match length of index (8)

Answer

Instead of selected the first N rows and subtracting them, subtract the entire column and only assign the first 6 values of it:

df.loc[:remainder, 'Pop2'] = df['Pop2'] - 1

Output:

>>> df
      Pop    Pop2
0  728375  728374
1  733355  733354
2  695395  695394
3  734658  734657
4  732811  732810
5  789396  789395
6  727761  727760
7  751967  751967