Re-fetch and populate missing data in pandas

Question

I have an original dataset that looks like this:

df = pd.DataFrame({'speed': [66.8,67,67.1,70,69],
                   'time': ['2017-08-09T05:41:30.168Z', '2017-08-09T05:41:31.136Z', '2017-08-09T05:41:31.386Z', '2017-08-09T05:41:31.103Z','2017-08-09T05:41:35.563Z' ]})

I could process on it to look (deleted microseconds):

df['time']= pd.to_datetime(df.time)
df['time'] = df['time'].apply(lambda x: x.replace(microsecond=0))

>>> df
   speed                time
0   66.8 2017-08-09 05:41:30
1   67.0 2017-08-09 05:41:31
2   67.1 2017-08-09 05:41:31
3   70.0 2017-08-09 05:41:31
4   69.0 2017-08-09 05:41:35

I now need to re-change the data so that any records that came to the same timestamp are averaged together, and for timestamps that didn't receive any data, use the last available value. Like:

   speed                time
0   66.80 2017-08-09 05:41:30
1   68.03 2017-08-09 05:41:31
2   70.00 2017-08-09 05:41:32
3   70.00 2017-08-09 05:41:33
4   70.00 2017-08-09 05:41:34
5   69.00 2017-08-09 05:41:35

I understand that this may be due to the use of groupby and resample, but as a newbie, I am in this. Any ideas on how to proceed?

I tried this, but I am getting the wrong results:

df.groupby( [df["time"].dt.second]).mean()
          speed
time           
30    66.800000
31    68.033333
35    69.000000

Answer 1

In [279]: df.resample('1S', on='time').mean().ffill()
Out[279]:
                         speed
time
2017-08-09 05:41:30  66.800000
2017-08-09 05:41:31  68.033333
2017-08-09 05:41:32  68.033333
2017-08-09 05:41:33  68.033333
2017-08-09 05:41:34  68.033333
2017-08-09 05:41:35  69.000000