使用其他已知列值的Pandas Dataframe fillna（）

Question

Given the following sample df :

   Other1  Other2     Name Value
0       0       1  Johnson     C
1       0       0  Johnson   NaN
2       1       1    Smith     R
3       1       1    Smith   NaN
4       0       1  Jackson     X
5       1       1  Jackson   NaN
6       1       1  Jackson   NaN

I want to be able to fill the NaN values with the df['Value'] value associated with the given name in that row. My desired outcome is the following, which I know can be achieved like so:

df['Value'] = df['Value'].fillna(method='ffill')

   Other1  Other2     Name Value
0       0       1  Johnson     C
1       0       0  Johnson     C
2       1       1    Smith     R
3       1       1    Smith     R
4       0       1  Jackson     X
5       1       1  Jackson     X
6       1       1  Jackson     X

However, this solution will not achieve the desired result if the names are not followed by one another in order. I also cannot sort by df['Name'] , as the order is important. Is there an efficient means of simply filling a given NaN value by it's associated name value and assigning it to that?

It's also important to note that a given Name will always only have a single value associated with it. Thank you in advance.

Answer 1

You should use groupby and transform :

df['Value'] = df.groupby('Name')['Value'].transform('first')
df

   Other1  Other2     Name Value
0       0       1  Johnson     C
1       0       0  Johnson     C
2       1       1    Smith     R
3       1       1    Smith     R
4       0       1  Jackson     X
5       1       1  Jackson     X
6       1       1  Jackson     X

Peter's answer is not correct because the first valid value may not always be the first in the group, in which case ffill will pollute the next group with the previous group's value.

ALollz's answer is fine, but dropna incurs some degree of overhead.