使用Pandas的左联接表（1：n），行数与左表相同

Question

How do I left join tables with 1:n relationship, while keeping the number of rows the same as left table and concatenating any duplicate data with a character/string like ';'.

Country Table

CountryID      Country      Area
1              UK           1029
2              Russia       8374

Cities Table

CountryID      City     
1              London           
1              Manchester       
2              Moscow          
2              Ufa   

I want:

CountryID      Country      Area      Cities
1              UK           1029      London;Manchester
2              Russia       8374      Moscow;Ufa

I know how to perform a normal left join

country.merge(city, how='left', on='CountryID')

which gives me four rows instead of two:

Area      Country      CountryID      City
1029      UK           1              London
1029      UK           1              Manchester
8374      Russia       2              Moscow
8374      Russia       2              Ufa

Answer 1

Use map by Series created by groupby + join for new column in df1 if performance is important:

df1['Cities'] = df1['CountryID'].map(df2.groupby('CountryID')['City'].apply(';'.join))
print (df1)
   CountryID Country  Area             Cities
0          1      UK  1029  London;Manchester
1          2  Russia  8374         Moscow;Ufa

Detail :

print (df2.groupby('CountryID')['City'].apply(';'.join))
CountryID
1    London;Manchester
2           Moscow;Ufa
Name: City, dtype: object

Another solution with join :

df = df1.join(df2.groupby('CountryID')['City'].apply(';'.join), on='CountryID')
print (df)
   CountryID Country  Area               City
0          1      UK  1029  London;Manchester
1          2  Russia  8374         Moscow;Ufa

Answer 2

This will give you the desired result:

df1.merge(df2, on='CountryID').groupby(['CountryID', 'Country', 'Area']).agg({'City': lambda x: ';'.join(x)}).reset_index()

#   CountryID Country  Area               City
#0          1      UK  1029  London;Manchester
#1          2  Russia  8374         Moscow;Ufa