Alternative to creating a new pandas series based on conditionals?

Question

I have df that has a column "Country" with country codes eg "DE" for Germany, "MX" for Mexico etc. I've created a function below and used.apply to create a new column "Region". I'm wondering if there is a more slick / efficient way to go about achieving this eg with np.where? Still trying to get my head around the syntax for np.where, the below solution works for now, just trying to broaden my knowledge of other ways to achieve this with Pandas:)

def region(df):
if df.Country == 'US':
    return "NA"
elif df.Country == 'DE' or  df.Country == 'ES' or df.Country == 'FR' or df.Country == 'GB' or df.Country == 'IT':
    return "EMEA"
elif df.Country == 'IN':
    return "APAC"
elif df.Country == 'BR' or df.Country == 'MX':
    return "LATAM"

df.insert(2, 'Region', df.apply(region, axis=1)) 

Answer 1

One way to achieve this is to use dictionary with pandas.Series.map function:

#Create a one-time dictionary with mapping of country against region
d = {'US':'NA','DE':'EMEA','ES':'EMEA','FR':'EMEA','GB':'EMEA','IT':'EMEA','IN':'APAC','BR':'LATAM','MX':'LATAM'}

#And use map function to create a new column
df['Region'] = df['Country'].map(d)

print(df)
  Country Region
0      US     NA
1      DE   EMEA
2      ES   EMEA
3      FR   EMEA
4      GB   EMEA
5      IT   EMEA
6      IN   APAC
7      BR  LATAM
8      MX  LATAM