[Python] Excessive NaNs when Resampling + Interpolating in Pandas

I am trying to downsample a time series in Pandas from 8 seconds to 10 seconds. For the purposes of this example, I've generated fake data that linearly increases with the number of seconds, over a minute. Importantly, for this example, the time intervals of the two time series are not multiples of each other.

When using .resample().interpolate() in Pandas, it seems unable to interpolate for the first few points, for which there is sufficient data. Can someone help explain this and how to work around it? Here's the example:

import numpy as np
import pandas as pd
import datetime

a = datetime.datetime(2025, 12, 2, 17, 39, 6)
interval8df = pd.DataFrame(np.linspace(60, 124, 9), columns=['Hi'], index=pd.date_range(a, periods=9, freq='8s'))
interval8df['Hi']

2025-12-02 17:39:06 60.0
2025-12-02 17:39:14 68.0
2025-12-02 17:39:22 76.0
2025-12-02 17:39:30 84.0
2025-12-02 17:39:38 92.0
2025-12-02 17:39:46 100.0
2025-12-02 17:39:54 108.0
2025-12-02 17:40:02 116.0
2025-12-02 17:40:10 124.0
Freq: 8s, Name: Hi, dtype: float64


When using resample interpolate, this is the result:

interval8df.resample('10s').interpolate(method='time')['Hi']

2025-12-02 17:39:00 NaN
2025-12-02 17:39:10 NaN
2025-12-02 17:39:20 NaN
2025-12-02 17:39:30 84.0
2025-12-02 17:39:40 94.0
2025-12-02 17:39:50 104.0
2025-12-02 17:40:00 114.0
2025-12-02 17:40:10 124.0
Freq: 10s, Name: Hi, dtype: float64


While I can understand the first 17:39:00 going NaN, both 17:39:10 and 17:39:20 are both surrounded by points in the original time series (6 and 14 seconds, then 14 and 20 seconds respectively). I'm confused why this is happening and am hoping someone can explain why it's occurring.

This is my first time asking a question, apologies if I could've improved my wording.

I've tried using mean, but that produced no NaNs.

interval8df.resample('10s').mean()['Hi']

2025-12-02 17:39:00 60.0
2025-12-02 17:39:10 68.0
2025-12-02 17:39:20 76.0
2025-12-02 17:39:30 88.0
2025-12-02 17:39:40 100.0
2025-12-02 17:39:50 108.0
2025-12-02 17:40:00 116.0
2025-12-02 17:40:10 124.0
Freq: 10s, Name: Hi, dtype: float64


Additionally, changing the interpolate method does not seem to have improved the solution.

The workaround I've been using is upsampling from 8 seconds to 1 second using interpolate, then downsampling from 1 second to 10 seconds using the mean, which is obviously clunky. I would like to be able to do this directly in one step.

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