如何 select 基于 pandas 中的多个条件每行一列

Question

I have the following dataframe called df:

    Identifier   Name1   Name2     Country   Otherdata.......
0   N102314      BDH.A   0123      AUS
1   D19u248      DDF     DDF.X     DEN
2   J19j09f      XXG.X   XXG.DD    GER
3   Jd139jf      D07.SS  D07       SG
4   Jh39222      DEE     DEE.O     US
5   HH819jf      HHD.OH  HHD       MX
6   Jajh393      HXX     HXX.K     US  
7   DeeaJJd      MSS.O   DEX.O     US 

I want to create a new column called Name0 where I select a column per row based on the following conditions.

If Country == "US", ALWAYS select what's in Name1 for Name0.

Otherwise, check which name contain a ".", and choose that item for Name0. if both of Name1 and Name2 contains a dot print the word NAMEERROR in Name0.

So the final frame will look like this:

    Identifier   Name1   Name2     Country  Name0      NOTES....... 
0   N102314      BDH.A   0123      AUS      BDH.A      #not US so chose the one with the "."
1   D19u248      DDF     DDF.X     DEN      DDF.X      #not US so chose the one with the "."
2   J19j09f      XXG.X   XXG.DD    GER      NAMEERROR  #not US and both contains ".", print NAMEERROR
3   Jd139jf      D07.SS  D07       SG       D07.SS     #not US so chose the one with the "."
4   Jh39222      DEE     DEE.O     US       DEE        #US so chose Name1
5   HH819jf      HHD.OH  HHD       MX       HHD.OH     #not US so chose the one with the "."
6   Jajh393      HXX     HXX.K     US       HXX        #US so chose Name1
7   DeeaJJd      MSS.O   DEX.O     US       MSS.O      #both contain "." but US so chose Name1

I was thinking it might look like for the first part

df['Name0'] = np.NaN
df['Name0'] = np.where(df['Country'].str.contains('US'),df['Name1'],df['Name0'])

but I don't know where to start for the rest of the condition.

Answer 1

apply is handy here.

def fix(country, n1, n2):
    if country == 'US':
        return n1
    else:
        if ('.' in n1) & ('.' in n2):
            return 'NAMERERROR'
        elif '.' in n1:
            return n1
        elif '.' in n2:
            return n2


df['Name0'] = df.apply(lambda x: fix(country=x['Country'],
                                     n1 = x['Name1'],
                                     n2 = x['Name2']), axis=1)