I have the following data

data = {
    'Subject': ['3','3','3','3','3','3','3','3','3','10','10','10','10','10','10','10','10','10'],
    'Day': [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'Value': [19.0959, 19.2321, 19.3088, 19.2589, 19.3085, 19.0455, 19.3491, NaN, 19.1823, 25.7506, 25.8287, NaN, 26.2913, NaN, 26.1501, 25.9447, 25.9493, 25.9629]
}

which becomes this dataframe after pd.DataFrame(data):

   Subject   Day    Value
0        3     1  19.0959
1        3     2  19.2321
2        3     3  19.3088
3        3     4  19.2589
4        3     5  19.3085
5        3     6  19.0455
6        3     7  19.3491
7        3     8      NaN
8        3     9  19.1823
9       10     1  25.7506
10      10     2  25.8287
11      10     3      NaN
12      10     4  26.2913
13      10     5      NaN
14      10     6  26.1501
15      10     7  25.9447
16      10     8  25.9493
17      10     9  25.9629

I have attempted to interpolate the missing data in the ‘Value’ column but this dataframe being 3 columns seems to be causing issues to where the data is not being interpolated properly within the groups whenever a MUCH LARGER dataframe with many more subjects are involved.

For example:

df['Value'] = df.groupby('Subject')['Value'].transform(lambda group: group.interpolate())

works for small datasets like the one shown, but whenever I apply the same code to replace NaN’s with interpolation when there are 1,000 differnt subjects each with 9 days of data values, there are instances for subject 10 where day 5 still remains NaN after interpolation. Any advice on this? Thanks!