Python 3.x 熊猫在不同的索引上向前和向后填充

我有以下数据帧df：

             length       timestamp       width
name                                          
testschip-1     NaN 2019-08-01 00:00:00    NaN
testschip-1     NaN 2019-08-01 00:00:09    NaN
testschip-1     2   2019-08-01 00:00:20    NaN
testschip-1     2   2019-08-01 00:00:27    NaN
testschip-1     NaN 2019-08-01 00:00:38    1
testschip-2     4   2019-08-01 00:00:39    2
testschip-2     4   2019-08-01 00:00:57    NaN
testschip-2     4   2019-08-01 00:00:58    NaN
testschip-2     NaN 2019-08-01 00:01:17    NaN
testschip-3     NaN 2019-08-01 00:02:27    NaN
testschip-3     NaN 2019-08-01 00:03:47    NaN

首先，我想从索引“name”中删除字符串“testschip-”，以便只在索引上获取整数。第二，对于每个唯一索引，我希望在“length”和“width”列上应用正向填充或反向填充（任何不需要NAN的操作）。每个唯一索引具有相同的“长度”和“宽度”。在“testschip-3”中，我不想应用向后或向前填充。如果我对“testschip-1”（设置前两个索引two'2'）进行反向填充，那么最后一行索引“testschip-1”将得到一个不需要的“4”）。我无法预先判断我是否必须预先应用向后填充或向前填充，因为我有400万行数据要开始

使用：

df.index = df.index.str.lstrip('testschip-').astype(int)
#alternative
#df.index = df.index.str[10:].astype(int)
#df.index = df.index.str.split('-').str[-1].astype(int)
df.groupby(level = 0).apply(lambda x: x.bfill().ffill())

输出

      length           timestamp  width
name                                   
1        2.0 2019-08-01 00:00:00    1.0
1        2.0 2019-08-01 00:00:09    1.0
1        2.0 2019-08-01 00:00:20    1.0
1        2.0 2019-08-01 00:00:27    1.0
1        2.0 2019-08-01 00:00:38    1.0
2        4.0 2019-08-01 00:00:39    2.0
2        4.0 2019-08-01 00:00:57    2.0
2        4.0 2019-08-01 00:00:58    2.0
2        4.0 2019-08-01 00:01:17    2.0
3        NaN 2019-08-01 00:02:27    NaN
3        NaN 2019-08-01 00:03:47    NaN

使用：

输出

      length           timestamp  width
name                                   
1        2.0 2019-08-01 00:00:00    1.0
1        2.0 2019-08-01 00:00:09    1.0
1        2.0 2019-08-01 00:00:20    1.0
1        2.0 2019-08-01 00:00:27    1.0
1        2.0 2019-08-01 00:00:38    1.0
2        4.0 2019-08-01 00:00:39    2.0
2        4.0 2019-08-01 00:00:57    2.0
2        4.0 2019-08-01 00:00:58    2.0
2        4.0 2019-08-01 00:01:17    2.0
3        NaN 2019-08-01 00:02:27    NaN
3        NaN 2019-08-01 00:03:47    NaN