I have a df like this :
| PRODUCTNUMBER | Jerarquía principal | Jerarquía secundaria marcas | COT | Ecommerce | dabra-catalog | Dexter-ecommerce | Stockcenter-ecommerce |
|---|---|---|---|---|---|---|---|
| AD802309 | Medias-Hombre | ADIDAS | 950699 | NaN | NaN | NaN | NaN |
| AD481076 | NaN | Adidas | 950699 | NaN | NaN | NaN | NaN |
| AD481137 | Medias-Hombre | Adidas | 950699 | Medias-Hombre | Medias-Hombre | Medias-Hombre | Medias-Hombre |
and I need to get this output:
| PRODUCTNUMBER | PRODUCTCATEGORYNAME | PRODUCTCATEGORYHIERARCHYNAME |
|---|---|---|
| AD802309 | Medias-Hombre | Jerarquía principal |
| AD802309 | ADIDAS | Jerarquía secundaria marcas |
| AD802309 | 950699 | COT |
| AD481076 | Adidas | Jerarquía secundaria marcas |
| AD481076 | 950699 | COT |
| AD481137 | Medias-Hombre | Jerarquía principal |
| AD481137 | Adidas | Jerarquía secundaria marcas |
| AD481137 | 950699 | COT |
| AD481137 | Medias-Hombre | Ecommerce |
| AD481137 | Medias-Hombre | dabra-catalog |
| AD481137 | Medias-Hombre | Dexter-ecommerce |
| AD481137 | Medias-Hombre | Stockcenter-ecommerce |
is it possible? "NaN" values must not be transposed
Try with melt
out = df.melt('PRODUCTNUMBER',
value_name='PRODUCTCATEGORYHIERARCHYNAME',
var_name='PRODUCTCATEGORYNAME').dropna()
Out[201]:
PRODUCTNUMBER PRODUCTCATEGORYNAME PRODUCTCATEGORYHIERARCHYNAME
0 AD802309 Jerarquía principal Medias-Hombre
2 AD481137 Jerarquía principal Medias-Hombre
3 AD802309 Jerarquía secundaria marcas ADIDAS
4 AD481076 Jerarquía secundaria marcas Adidas
5 AD481137 Jerarquía secundaria marcas Adidas
6 AD802309 COT 950699
7 AD481076 COT 950699
8 AD481137 COT 950699
11 AD481137 Ecommerce Medias-Hombre
14 AD481137 dabra-catalog Medias-Hombre
17 AD481137 Dexter-ecommerce Medias-Hombre
20 AD481137 Stockcenter-ecommerce Medias-Hombre