Pandas forward and backward fill on distinct indices - python-3.x

Question

I have the following dataframe 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
First, I want to remove the string "testschip-" from the index "name" so I get integers only on the indices. Second, per unique index I want to apply forward fill or backward fill (whatever is neccessary to obtain no NaNs) on both columns 'length' and 'width'. Each unique index has the same "length" and "width". On "testschip-3" I dont want to apply backward or forward fill. If I do backward fill on "testschip-1" (which is needed to set the first two indices two '2'), I get an unwanted '4' for the last row of index "testschip-1"). I cannot judge beforehand if I have to apply backward or forward fill beforehand, since I have 4 million rows of data to start with.

Answer 1

Use:
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())
Output
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