Best method for non-regular index-based interpolation on grouped dataframes

Question

Problem statement

I had the following problem:

• I have samples that ran independent tests. In my dataframe, tests of sample with the same "test name" are also independent. So the couple (test,sample) is independent and unique.
• data are collected at non regular sampling rates, so we're speaking about unequaly spaced indices. This "time series" index is called unreg_idx in the example. For the sake of simplicity, it is a float between 0 & 1.
• I want to figure out what the value at a specific index, e.g. for unreg_idx=0.5. If the value is missing, I just want a linear interpolation that depends on the index. If extrapolating because the value is at an extremum in the sorted unreg_idx of the group (test,sample), it can leave NaN.

Note the following from pandas documentation:

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.

’linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.

The only solution I found is long, complex and slow. I am wondering if I am missing out on something, or on the contrary something is missing from the pandas library. I believe this is a typical issue in scientific and engineering fields to have independent tests on various samples with non regular indices.

What I tried

sample data set preparation

This part is just for making an example

import pandas as pd

import numpy as np


tests = (f'T{i}' for i in range(20))
samples = (chr(i) for i in range(97,120))

idx = pd.MultiIndex.from_product((tests,samples),names=('tests','samples'))

idx

dfs=list()
for ids in idx:
    group_idx = pd.MultiIndex.from_product(((ids[0],),(ids[1],),tuple(np.random.random_sample(size=(90,))))).sort_values()
    dfs.append(pd.DataFrame(1000*np.random.random_sample(size=(90,)),index=group_idx))


df = pd.concat(dfs)

df = df.rename_axis(index=('test','sample','nonreg_idx')).rename({0:'value'},axis=1)

The (bad) solution

add_missing = df.index.droplevel('nonreg_idx').unique().to_frame().reset_index(drop=True)

add_missing['nonreg_idx'] = .5

add_missing = pd.MultiIndex.from_frame(add_missing)

added_missing = df.reindex(add_missing)

df_full = pd.concat([added_missing.loc[~added_missing.index.isin(df.index)], df])

df_full.sort_index(inplace=True)

def interp_fnc(group):
    try:
        return group.reset_index(['test','sample']).interpolate(method='slinear').set_index(['test','sample'], append=True).reorder_levels(['test','sample','value']).sort_index()
    except:
        return group

grouped = df_full.groupby(level=['test','sample'])

df_filled = grouped.apply(
    interp_fnc
)

Here, the wanted values are in df_filled. So I can do df_filled.loc[(slice(None), slice(None), .5),'value'] to get what I need for each sample/test.

I would have expected to be able to do the same within 1 or maximum 2 lines of code. I have 14 here. apply is quite a slow method. I can't even use numba.

Question

Can someone propose a better solution? If you think there is no better alternative, please comment and I'll open an issue...