我可以使用此代码使用向前传播填充值,但这仅适用于03:31和03:32,而不是03:27和03:28.

import pandas as pd
import numpy as np

df = pd.read_csv('test.csv', index_col = 0)
data = df.fillna(method='ffill')
ndata = data.to_csv('test1.csv')

结果是：

DateTime A B
01-01-2017 03:27
01-01-2017 03:28
01-01-2017 03:29 0.18127718 -0.178835737
01-01-2017 03:30 0.186923018 -0.183260853
01-01-2017 03:31 0.186923018 -0.183260853
01-01-2017 03:32 0.186923018 -0.183260853
01-01-2017 03:33 0.18127718 -0.178835737

我如何使用backfil包含’Bfill’来填补03:27和03:28的缺失值？

如果需要替换NaN值向前和向后填充,您可以使用ffill和bfill：

print (df)
A B
DateTime
01-01-2017 03:27 NaN NaN
01-01-2017 03:28 NaN NaN
01-01-2017 03:29 0.181277 -0.178836
01-01-2017 03:30 0.186923 -0.183261
01-01-2017 03:31 NaN NaN
01-01-2017 03:32 NaN NaN
01-01-2017 03:33 0.181277 -0.178836

data = df.ffill().bfill()
print (data)
A B
DateTime
01-01-2017 03:27 0.181277 -0.178836
01-01-2017 03:28 0.181277 -0.178836
01-01-2017 03:29 0.181277 -0.178836
01-01-2017 03:30 0.186923 -0.183261
01-01-2017 03:31 0.186923 -0.183261
01-01-2017 03:32 0.186923 -0.183261
01-01-2017 03:33 0.181277 -0.178836

与带有参数的功能fillna相同：

data = df.fillna(method='ffill').fillna(method='bfill')