Replacing value in whole dataframe if part of the string match

Question

I am trying to replace the value in my whole dataframe if part of the string match. I tried to use df.replace df.str.contains etc but none of them are working. Here are similar questions but they are not answering my question [sample][1]:

I have datframe like this: df

Brand                        2Brand                         3Brand
Audi                          BMW                           None of the above
None from list                BMW                           Audi
None                          None below or above           BMW
NaN                           Audi                          NaN

I just want to replace everywhere None appear.

The desired otput would be like this:

Brand                        2Brand                         3Brand
Audi                          BMW                           None
None                          BMW                           Audi
None                          None                          BMW
NaN                           Audi                          NaN

Answer 1

Use DataFrame.mask with Series.str.contains per columns in DataFrame.apply , for avoid convert possible None and NaN values is used na=False parameter:

df = df.mask(df.apply(lambda x: x.str.contains('None', na=False)), 'None')
print (df)
  Brand 2Brand 3Brand
0  Audi    BMW   None
1  None    BMW   Audi
2  None   None    BMW

EDIT: If possible some numeric columns use DataFrame.select_dtypes for get only strings columns (obviously objects ate strings) and then add to mask numeric column filled by False by DataFrame.reindex :

print (df)
            Brand               2Brand             3Brand  col
0            Audi                  BMW  None of the above    4
1  None from list                  BMW               Audi    7
2            None  None below or above                BMW    9
3             NaN                 Audi                NaN    5

mask = (df.select_dtypes(object)
          .apply(lambda x: x.str.contains('None', na=False))
          .reindex(df.columns, fill_value=False, axis=1))
df = df.mask(mask, None)
print (df)
  Brand 2Brand 3Brand  col
0  Audi    BMW   None    4
1  None    BMW   Audi    7
2  None   None    BMW    9
3   NaN   Audi    NaN    5

Answer 2

Use applymap and mask :

In [1732]: df.mask(df.applymap(lambda x: 'None' in x), None)
Out[1732]: 
  Brand 2Brand 3Brand
0  Audi    BMW   None
1  None    BMW   Audi
2  None   None    BMW