Pandas : change values in column based on a mapping of two different columns

I have this dataframe:

    id      result.value.text       result.value.labels result.id   result.from_id  result.to_id
0   793     skin melanoma           indication          5jSiC_n3IM  NaN              NaN
1   793     proteinase              protein             Lso-iCCHar  NaN              NaN
2   793     plasminogen activator   protein             _17D_kE5zf  NaN              NaN
3   793     NaN                     NaN                 NaN         5jSiC_n3IM       Lso-iCCHar
4   793     NaN                     NaN                 NaN         5jSiC_n3IM       _17D_kE5zf

I want to change the values of result.from_id and result.to_id columns, and instead of having the values of the result.id column, to replace them with the corresponding values of the result.value.text column.

Wanted Output

    id      result.value.text       result.value.labels result.from_id  result.to_id
0   793     skin melanoma           indication          NaN                 NaN
1   793     proteinase              protein             NaN                 NaN
2   793     plasminogen activator   protein             NaN                 NaN
3   793     NaN                     NaN                 skin melanoma   proteinase
4   793     NaN                     NaN                 skin melanoma   plasminogen activator

Can someone help?

>Solution :

Create dictionary with remove missing rows per result.id, result.value.text and then mapping both columns:

d = df.dropna(subset=['result.id','result.value.text']).set_index('result.id')['result.value.text'].to_dict()

cols = ['result.from_id','result.to_id']
df[cols] = df[cols].apply(lambda x: x.map(d))