I have the following table:
| ColumnA | ColumnB | ColumnC |
|---|---|---|
| 1 | AB001 | TYPE-A |
| 2 | AB012 | TYPE-A |
| 3 | AB035 | TYPE-B |
| 4 | AB039 | TYPE-B |
| 5 | AB065 | TYPE-A |
| 6 | AB088 | TYPE-B |
I should get the output such that, if ColumnC is TYPE-A then it should save as a separate column (ColumnD) with output www.website.com/abc/AB001 if it is TYPE-B then it should be www.website.com/xyz/AB035. The output table should look like the following:
How do I do that?
Use Series.map by dictionary for types strings and join together by +:
#if no match `TYPE-A` or `TYPE-B` added default value no match
s = df['ColumnC'].map({'TYPE-A':'abc','TYPE-B':'xyz'}).fillna('no match')
df['ColumnD'] = ' www.website.com/' + s + '/' + df['ColumnB'].astype(str)
print (df)
ColumnA ColumnB ColumnC ColumnD
0 1 AB001 TYPE-A www.website.com/abc/AB001
1 2 AB012 TYPE-A www.website.com/abc/AB012
2 3 AB035 TYPE-B www.website.com/xyz/AB035
3 4 AB039 TYPE-B www.website.com/xyz/AB039
4 5 AB065 TYPE-A www.website.com/abc/AB065
5 6 AB088 TYPE-B www.website.com/xyz/AB088
