So i have a column called owners that looks like this

'Owned by Terrick Inc.',
 nan,
 'Owned by MHPI Inc.',
 'Owned by Zeman Homes & Neighborhoods',
 'Owned by Santefort Neighborhood Properties',
 'Owned by MHPI Inc.',
 'Owned by MHPI Inc.',
 nan,
 2,
 nan,
 5,
 'Owned by RHP Properties',
 'Owned by Zeman Homes & Neighborhoods',
 'Owned by Zeman Homes & Neighborhoods',
 'Owned by Gateway Manufactured Home Community',
 3,
 nan,
 'Owned by Zeman Homes & Neighborhoods',
 'Owned by MHPI Inc.',
 nan,
 'Owned by Indian Trails LLC',
 'Owned by Zeman Homes & Neighborhoods',
 1,
 'Owned by Lake View Manufactured Home Community',
 5,
 2,
 'Owned by Zeman Homes & Neighborhoods'

I am trying to change all the values that are not number to a number 10. but for some reason it is not working. I tried 2 methods this is the first, but this just replaces the whole column with 9 so its not taking in my conditions.

df['Owner'][(df['Owner'] != 0) | (df['Owner'] != '1') | (df['Owner'] != '2') | (df['Owner'] != 3) | (df['Owner'] != 4) | (df['Owner'] != 5) | (df['Owner'] != 6)] = 9

the second method i used was a loop with an if statment. but this isnt doing anything to the list

for i in df['Owner']:
    if i == 1:
        pass
    elif i == 2:
        pass
    elif i == 3:
        pass
    elif i == 4:
        pass
    elif i == 5:
        pass
    elif i == 6:
        pass
    elif i == 0:
        pass
    else:
        i = 9

Thanks in advance

CodePudding user response：

You can map the type function, and compare to int, then use where to keep the integers and replace the rest with 10:

df['Owner'] = df['Owner'].where(df['Owner'].map(type).eq(int), 10)

output:

  Owner
0    10
1    10
2     2
3    10
4     5
5    10

Used input:

from numpy import nan
df = pd.DataFrame({'Owner': ['Owned by MHPI Inc.', nan, 2, nan, 5, 'Owned by RHP Properties',]})

CodePudding user response：

df['Owner'] = [10 if type(x) is not int else x for x in df['Owner']]

Should do it. It reads like plain English like so often in Python.