我正在使用Pandas和Numpy，并尝试替换此类系列中的所有NaN值：
date a
2017-04-24 01:00:00 [1,0,0]
2017-04-24 01:20:00 [1,0,0]
2017-04-24 01:40:00 NaN
2017-04-24 02:00:00 NaN
2017-04-24 02:20:00 [0,1,0]
2017-04-24 02:40:00 [1,0,0]
2017-04-24 03:00:00 NaN
2017-04-24 03:20:00 [0,0,1]
2017-04-24 03:40:00 NaN
2017-04-24 04:00:00 [1,0,0]
与最近的objcet（在这种情况下为Numpy数组）。结果是：
date a
2017-04-24 01:00:00 [1,0,0]
2017-04-24 01:20:00 [1,0,0]
2017-04-24 01:40:00 [1,0,0]
2017-04-24 02:00:00 [0,1,0]
2017-04-24 02:20:00 [0,1,0]
2017-04-24 02:40:00 [1,0,0]
2017-04-24 03:00:00 [1,0,0]
2017-04-24 03:20:00 [0,0,1]
2017-04-24 03:40:00 [0,0,1]
2017-04-24 04:00:00 [1,0,0]
有人知道这样做的有效方法吗？非常感谢。
最佳答案
删除null，然后用reindex填充
df.set_index('date').a.dropna().reindex(df.date, method='nearest').reset_index()
date a
0 2017-04-24 01:00:00 [1, 0, 0]
1 2017-04-24 01:20:00 [1, 0, 0]
2 2017-04-24 01:40:00 [1, 0, 0]
3 2017-04-24 02:00:00 [0, 1, 0]
4 2017-04-24 02:20:00 [0, 1, 0]
5 2017-04-24 02:40:00 [1, 0, 0]
6 2017-04-24 03:00:00 [0, 0, 1]
7 2017-04-24 03:20:00 [0, 0, 1]
8 2017-04-24 03:40:00 [1, 0, 0]
9 2017-04-24 04:00:00 [1, 0, 0]