我有一个熊猫系列,目前我刚刚使用
signal = pd.Series(thick, index = pd.TimedeltaIndex(time_list_thick,unit = 's'))
resampled_signal = signal.resample('1S').mean()
但是,我的重采样数据包含我要删除的NAN:
00:00:00.415290 451.369402
00:00:01.415290 NaN
00:00:02.415290 451.358724
00:00:03.415290 451.356055
00:00:04.415290 451.350716
00:00:05.415290 451.340039
00:00:06.415290 NaN
00:00:07.415290 451.332031
00:00:08.415290 451.326692
00:00:09.415290 451.318684
00:00:10.415290 451.310675
00:00:11.415290 NaN
00:00:12.415290 451.302667
00:00:13.415290 451.291990
00:00:14.415290 NaN
00:00:15.415290 451.286651
00:00:16.415290 451.278643
00:00:17.415290 451.274639
00:00:18.415290 451.265296
00:00:19.415290 NaN
00:00:20.415290 451.255953
00:00:21.415290 NaN
00:00:22.415290 451.243941
00:00:23.415290 NaN
00:00:24.415290 451.234598
00:00:25.415290 NaN
00:00:26.415290 451.225255
00:00:27.415290 451.219916
00:00:28.415290 451.211908
00:00:29.415290 451.201231
我想做的是用插值点替换这些NaN,插值点的值位于最近的有限数据点之间(例如:我的数据中的第2行大约为451.364..)。这可能吗?如果可能,如何实现
您可以使用
df.interpolate()
来执行此操作。此外,可以使用time
方法来考虑基于时间的索引详情如下:
之前:
之后:
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