You can make use of the pd.merge function
Example: You have a "country" df with "country", "city" and "zip" columns and "continent" df with "country" and "continent" columns. Use the pd.merge function on the common column "country"
country = pd.DataFrame([['country1','city1','zip1'],['country1','city1','zip2'],['country1','city2','zip3'],['country1','city2','zip4'],
                       ['country2','city3','zip5'],['country2','city3','zip6'],['country2','city4','zip7'],
                       ['country3','city5','zip8'],['country3','city6','zip9']],
                      columns=['country','city','zipcode'])
continent = pd.DataFrame([['country1','A'],['country2','B'],['country3','C'],['country4','D'],['country5','E']],
                      columns=['country','continent'])
country = country.merge(continent, on=['country'])
print(country)
Output:
    country   city zipcode continent
0  country1  city1    zip1         A
1  country1  city1    zip2         A
2  country1  city2    zip3         A
3  country1  city2    zip4         A
4  country2  city3    zip5         B
5  country2  city3    zip6         B
6  country2  city4    zip7         B
7  country3  city5    zip8         C
8  country3  city6    zip9         C