这似乎有效.可能有点清理代码.但是你得到了它的要点

from datetime import datetime

import pandas as pd

import time

#Create data

df = pd.DataFrame({ 'interval' : [0.782296, 0.795469, 0.821426, 0.834957,

0.864383, 0.906240],

'datetime' : [datetime(2012, 11, 19, 12, 40, 10), pd.NaT,

datetime(2012, 11, 19, 12, 35, 10), pd.NaT,

datetime(2012, 11, 19, 12, 30, 10),

datetime(2012, 11, 19, 12, 25, 10)

]})

#Cast date to seconds (also recast the NaT to Nan)

df['seconds'] = [time.mktime(t.timetuple()) if t is not pd.NaT else float('nan') for t in df['datetime'] ]

#Set the interval as the index, as interpolation uses the index

df.set_index('interval', inplace=True)

#Use the 'values'-argument to actually use the values of the index and not the spacing

df['intepolated'] = df['seconds'].interpolate('values')

#Cast the interpolated seconds back to datetime

df['datetime2'] = [datetime.utcfromtimestamp(t) for t in df['intepolated']]

#Clean up

df.reset_index(inplace=True)

df = df[['interval', 'datetime2']]

>>>>df

Out[25]:

interval datetime2

0 0.782296 2012-11-19 11:40:10.000000

1 0.795469 2012-11-19 11:38:29.005878

2 0.821426 2012-11-19 11:35:10.000000

3 0.834957 2012-11-19 11:33:35.503178

4 0.864383 2012-11-19 11:30:10.000000

5 0.906240 2012-11-19 11:25:10.000000

希望这是你想要的.