Replace values based on two columns in pandas df

Question

I have a really large df.

UserID      Name   TempName
Matt      458488   None
Matt      458488   None
Matt      458488   None
Matt      458488   None
Matt      458488   None
Pablo     213123   8748
Pablo     213123   8748
Pablo     213123   8748
Pablo     213123   8748
Pablo     213123   8748
Pablo     213123   8748
Pablo     213123   8748
Kate       2442    None
 ...      ...      ...

Need to replace None in tempName by Name Values. But only there where users has None in Temp Val. If User as Pablo in Temp != None, should pass and tempValue should be unchanged.

None is a string, no NaN

Example result of TempName:

   UserID      Name   TempName
    Matt      458488   458488   
    Matt      458488   458488   
    Matt      458488   458488   
    Matt      458488   458488   
    Matt      458488   458488   
    Pablo     213123   8748
    Pablo     213123   8748
    Pablo     213123   8748
    Pablo     213123   8748
    Pablo     213123   8748
    Pablo     213123   8748
    Pablo     213123   8748
    Kate       2442    2442    
     ...      ...      ...

Now my code works on simple but really slow loop and if.

For record in df:
if df.TempName.iloc[record] == 'None':
   df.TempName.iloc[record] = df.Name.iloc[record]
else:
   pass

I looking for faster an maybe better solution of this problem. Maybe someone have a better idea? On SO i found only a solution of merging or concat whole cols, I need to do that partialy. DF has 9mln records.

It's not duplicated coz None is not NaN value, just string. I can't replace str'None' to NaN too.