Linear interpolation between unique values - Python

Question

I have a df that contains multiple values at duplicate time points. I want to interpolate values for two specific columns but only between unique time points. Using the df below, I want to interpolate X and Y only but between unique time points.

import pandas as pd
import numpy as np

df = pd.DataFrame({       
        'Time' : ['09:00:00.1','09:00:00.1','09:00:00.2','09:00:00.2','09:00:00.3'],       
        'X' : [1,np.nan,np.nan,np.nan,3],
        'Y' : [1,np.nan,np.nan,np.nan,3],
        'A' : [5,np.nan,np.nan,np.nan,6],
        'B' : [5,np.nan,np.nan,np.nan,6],                           
        })  

df1 = df.groupby('Time').apply(lambda x: x.interpolate(method='linear'))

Note: I don't want,

df[['X','Y']] = df[['X','Y']].interpolate(method = 'linear')

The intended output is:

        Time    X    Y    A    B
0  09:00:00.1  1.0  1.0  5.0  5.0
1  09:00:00.1  1.0  1.0  Nan  NaN
2  09:00:00.2  2.0  2.0  NaN  NaN
3  09:00:00.2  2.0  2.0  NaN  NaN
4  09:00:00.3  3.0  3.0  6.0  6.0

Answer 1

First we drop_duplicates based on Time to get unique rows, then we interpolate, and update our original dataframe with these values.

Finally we use ffill to forwardfill our values:

interpolation = df.drop_duplicates('Time')[['X', 'Y']].interpolate()
df.loc[interpolation.index, ['X', 'Y']] = interpolation
df.loc[:, ['X', 'Y']] = df[['X', 'Y']].ffill()

         Time    X    Y    A    B
0  09:00:00.1 1.00 1.00 5.00 5.00
1  09:00:00.1 1.00 1.00  nan  nan
2  09:00:00.2 2.00 2.00  nan  nan
3  09:00:00.2 2.00 2.00  nan  nan
4  09:00:00.3 3.00 3.00 6.00 6.00

---

Another method would be to use np.floor but this only works if you have the scenario as in your example dataframe (and this will probably not be the case):

df[['X', 'Y']] = np.floor(df[['X', 'Y']].interpolate())

         Time    X    Y    A    B
0  09:00:00.1 1.00 1.00 5.00 5.00
1  09:00:00.1 1.00 1.00  nan  nan
2  09:00:00.2 2.00 2.00  nan  nan
3  09:00:00.2 2.00 2.00  nan  nan
4  09:00:00.3 3.00 3.00 6.00 6.00