python piecewise linear interpolation across dataframes in a list

I am trying to apply piecewise linear interpolation. I first tried to use pandas built-in interpolate function but it was not working.

Example data looks below

import pandas as pd
import numpy as np

d = {'ID':[5,5,5,5,5,5,5], 'month':[0,3,6,9,12,15,18], 'num':[7,np.nan,5,np.nan,np.nan,5,8]}
tempo = pd.DataFrame(data = d)
d2 = {'ID':[6,6,6,6,6,6,6], 'month':[0,3,6,9,12,15,18], 'num':[5,np.nan,2,np.nan,np.nan,np.nan,7]}
tempo2 = pd.DataFrame(data = d2)
this = []
this.append(tempo)
this.append(tempo2)

The actual data has over 1000 unique IDs, so I filtered each ID into a dataframe and put them into the list.

The first dataframe in the list looks as below

I am trying to go through all the dataframe in the list to do a piecewise linear interpolation. I tried to change month to a index and use .interpolate(method='index', inplace = True) but it was not working.

The expected output is

ID | month | num

5 | 0 | 7

5 | 3 | 6

5 | 6 | 5

5 | 9 | 5

5 | 12 | 5

5 | 15 | 5

5 | 18 | 8

This needs to be applied across all the dataframes in the list.

Assuming this is a follow up of your previous question, change the code to:

for i, df in enumerate(this):
    this[i] = (df
        .set_index('month')
        # optional, because of the previous question
        .reindex(range(df['month'].min(), df['month'].max()+3, 3))
        .interpolate()
        .reset_index()[df.columns]
        )

NB. I simplified the code to remove the groupby, which only works if you have a single group per DataFrame, as you mentioned in the other question.

Output:

[   ID  month  num
0   5      0  7.0
1   5      3  6.0
2   5      6  5.0
3   5      9  5.0
4   5     12  5.0
5   5     15  5.0
6   5     18  8.0,
   ID  month   num
0   6      0  5.00
1   6      3  3.50
2   6      6  2.00
3   6      9  3.25
4   6     12  4.50
5   6     15  5.75
6   6     18  7.00]