在一系列非数字对象中用最接近的值替换NaN？

Question

我正在使用Pandas和Numpy,我正在尝试替换像这样的系列中的所有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.

Answer 1

drop nulls然后填充备份 reindex

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]