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22823
我有以下数据框df:
时间col_A
0 1520582580.000 79.000
1 1520582880.000 22.500
2 1520583180.000 29.361
3 1520583480.000 116.095
4 1520583780.000 19.972
5 1520584080.000 36.857
6 1520584380.000 15.167
7 1520584680.000楠
8 1520584980.000南
9 1520585280.000楠
10 1520585580.000 34.500
11 1520585880.000 17.583
12 1520586180.000楠
13 1520586480.000 48.833
14 1520586780.000 18.806
15 1520587080.000 18.583
col_A缺少一些数据。我想创建一个col_B,它为每个丢失的记录取先前的值。 IE。
6 1520584380.000 15.167
7 1520584680.000 15.167
8 1520584980.000 15.167
9 1520585280.000 15.167
10 1520585580.000 34.500
11 1520585880.000 17.583
12 1520586180.000 17.583
13 1520586480.000 48.833
和col_C,它们使用非缺失点之前和之后的最接近点进行插值。 IE。
6 1520584380.000 15.167
7 1520584680.000 20.001
8 1520584980.000 24.834
9 1520585280.000 29.667
10 1520585580.000 34.500
11 1520585880.000 17.583
12 1520586180.000 33.208
13 1520586480.000 48.833
除了循环遍历数据帧以逐条记录进行计算外,还有没有一个内置函数可以用来优雅地实现这一目的?谢谢! 
我认为需要用插值填充:
df ['colB'] = df ['col_A']。ffill()
df ['colc'] = df ['col_A']。interpolate()
列印(df)
时间col_A colB colc
0 1.520583e + 09 79.000 79.000 79.00000
1 1.520583e + 09 22.500 22.500 22.50000
2 1.520583e + 09 29.361 29.361 29.36100
3 1.520583e + 09 116.095 116.095 116.09500
4 1.520584e + 09 19.972 19.972 19.97200
5 1.520584e + 09 36.857 36.857 36.85700
6 1.520584e + 09 15.167 15.167 15.16700
7 1.520585e + 09 NaN 15.167 20.00025
8 1.520585e + 09 NaN 15.167 24.83350
9 1.520585e + 09 NaN 15.167 29.66675
10 1.520586e + 09 34.500 34.500 34.50000
11 1.520586e + 09 17.583 17.583 17.58300
12 1.520586e + 09 NaN 17.583 33.20800
13 1.520586e + 09 48.833 48.833 48.83300
14 1.520587e + 09 18.806 18.806 18.80600
15 1.520587e + 09 18.583 18.583 18.58300
如果要使用方法时间进行插值:
df ['time'] = pd.to_datetime(df ['time'],unit ='s')
df = df.set_index('time')
df ['colB'] = df ['col_A']。ffill()
df ['colc'] = df ['col_A']。interpolate('time')
打印(df)
col_A colB colc
时间
2018-03-09 08:03:00 79.000 79.000 79.00000
2018-03-09 08:08:00 22.500 22.500 22.50000
2018-03-09 08:13:00 29.361 29.361 29.36100
2018-03-09 08:18:00 116.095 116.095 116.09500
2018-03-09 08:23:00 19.972 19.972 19.97200
2018-03-09 08:28:00 36.857 36.857 36.85700
2018-03-09 08:33:00 15.167 15.167 15.16700
2018-03-09 08:38:00 NaN 15.167 20.00025
2018-03-09 08:43:00 NaN 15.167 24.83350
2018-03-09 08:48:00 NaN 15.167 29.66675
2018-03-09 08:53:00 34.500 34.500 34.50000
2018-03-09 08:58:00 17.583 17.583 17.58300
2018-03-09 09:03:00 NaN 17.583 33.20800
2018-03-09 09:08:00 48.833 48.833 48.83300
2018-03-09 09:13:00 18.806 18.806 18.80600
2018-03-09 09:18:00 18.583 18.583 18.58300
|
你的答案
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