熊猫数据框初学者。我的数据集如下,其中 A 列和 B 列缺少值 (Test.csv):
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
01-01-2017 03:32
01-01-2017 03:33 0.18127718 -0.178835737
我可以使用此代码通过前向传播来填充值,但这仅填充 03:31 和 03:32,而不填充 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
如何使用 backfil 包含“Bfill”来填充 03:27 和 03:28 的缺失值?
您可以使用ffill and bfill如果需要更换NaN
值向前和向后填充:
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
与功能相同fillna带参数:
data = df.fillna(method='ffill').fillna(method='bfill')
本文内容由网友自发贡献,版权归原作者所有,本站不承担相应法律责任。如您发现有涉嫌抄袭侵权的内容,请联系:hwhale#tublm.com(使用前将#替换为@)