Mapping columns from one dataframe to another to create a new column
Use map or join:
df1['warehouse'] = df1['store'].map(df2.set_index('store_code')['warehouse'])
print (df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
df1 = df1.join(df2.set_index('store_code'), on=['store']).drop('serialNo', 1)
print (df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
df.merge
out = (df1.merge(df2, left_on='store', right_on='store_code')
.reindex(columns=['id', 'store', 'address', 'warehouse']))
print(out)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
pd.concat + df.sort_values
u = df1.sort_values('store')
v = df2.sort_values('store_code')[['warehouse']].reset_index(drop=1)
out = pd.concat([u, v], 1)
print(out)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
The first sort call is redundant assuming your dataframe is already sorted on store, in which case you may remove it.
df.replace/df.map
s = df1.store.replace(df2.set_index('store_code')['warehouse'])
print(s)
0 Land
1 Sea
2 Land
3 Land
4 Sea
df1['warehouse'] = s
print(df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea
Alternatively, create a mapping explicitly. This works if you want to use it later.
mapping = dict(df2[['store_code', 'warehouse']].values)
df1['warehouse'] = df1.store.map(mapping)
print(df1)
id store address warehouse
0 1 100 xyz Land
1 2 200 qwe Sea
2 3 300 asd Land
3 4 400 zxc Land
4 5 500 bnm Sea