python pandas conditional forward fill leave some rows untouched-CodePudding

Let's say I have a dataframe like this:

   name       address             email   paid
0  John  123 State St  [email protected]   5.00
1   NaN           NaN               NaN  15.00
2  John           NaN               NaN   3.00
3   NaN  100 Owen Ave               NaN  10.00

I would like to forward fill both the address and email columns when name is the same, and if the name is NaN, leave the row untouched, after doing so, the dataframe should look like this:

   name       address             email   paid
0  John  123 State St  [email protected]   5.00
1   NaN           NaN               NaN  15.00
2  John  123 State St  [email protected]   3.00
3   NaN  100 Owen Ave               NaN  10.00

I've tried using

df[['address', 'email']] = df[df['name'].notna()].groupby('name', sort=False)[['address', 'email']].ffill()

but it changes the 3rd row address to NaN like this

   name       address             email   paid
0  John  123 State St  [email protected]   5.00
1   NaN           NaN               NaN  15.00
2  John  123 State St  [email protected]   3.00
3   NaN           NaN               NaN  10.00

Is there anyway to let pandas leave some rows as they are?

CodePudding user response：

In the last assignment you are not filtering for only not NaN name rows. You are assigning, instead, the group-by results for all the rows.

This should fix the problem:

df.loc[df['name'].notna(),['address', 'email']] = df[df['name'].notna()].groupby('name', sort=False)[['address', 'email']].ffill()

In this way, you are assigning the results only for the rows with Name different from NaN