诀窍是要认识到日期时间可以表示为相对于某个时间流逝的秒数。
没有进一步的上下文部分，最难的事情是决定在什么时候你想要得到插值。

import pandas as pd
import numpy as np
from scipy.interpolate import interp1d

df = pd.read_excel(
    "https://github.com/norhther/datasets/raw/main/ncp1b.xlsx", 
    sheet_name="Sheet1",
)

x_columns = [col for col in df.columns if col.startswith("X-Axis")]

# What time do we want to align the columsn to?

# You can use anything else here or define equally spaced time points

# or something else.

target_times = df[x_columns].min(axis=1)

def interpolate_column(target_times, x_times, y_values):
    ref_time = x_times.min()

    # For interpolation we need to represent the values as floats. One options is to
    # compute the delta in seconds between a reference time and the "current" time.
    deltas = (x_times - ref_time).dt.total_seconds()

    # repeat for our target times
    target_times_seconds = (target_times - ref_time).dt.total_seconds()

    return interp1d(deltas, y_values, bounds_error=False,fill_value="extrapolate" )(target_times_seconds)

output_df = pd.DataFrame()
output_df["Times"] = target_times

output_df["Y-Axis Value NCVE-063 VPNDE"] = interpolate_column(
    target_times,
    df["X-Axis Value NCVE-063 VPNDE"],
    df["Y-Axis Value NCVE-063 VPNDE"],
)

# repeat for the other columns, better in a loop