I'm working with a pandas DataFrame which looks like this:
(**N.B - the offset is set as the index of the DataFrame)
offset X Y Z
0 -0.140137 -1.924316 -0.426758
10 -2.789123 -1.111212 -0.416016
20 -0.133789 -1.923828 -4.408691
30 -0.101112 -1.457891 -0.425781
40 -0.126465 -1.926758 -0.414062
50 -0.137207 -1.916992 -0.404297
60 -0.130371 -3.784591 -0.987654
70 -0.125000 -1.918457 -0.403809
80 -0.123456 -1.917480 -0.413574
90 -0.126465 -1.926758 -0.333554
I have applied the rolling mean with window size = 5, to the data frame using the following code. I need to keep this window size = 5 and I need values for the whole dataframe for all of the offset values (no NaNs).
df = df.rolling(center=False, window=5).mean()
Which gives me:
offset X Y Z
0.0 NaN NaN NaN
10.0 NaN NaN NaN
20.0 NaN NaN NaN
30.0 NaN NaN NaN
40.0 -0.658125 -1.668801 -1.218262
50.0 -0.657539 -1.667336 -1.213769
60.0 -0.125789 -2.202012 -1.328097
70.0 -0.124031 -2.200938 -0.527121
80.0 -0.128500 -2.292856 -0.524679
90.0 -0.128500 -2.292856 -0.508578
I would like the DataFrame to be able to keep the first values that are NaN unchanged and have the the rest of the values as the result of the rolling mean. Is there a simple way that I would be able to do this? Thanks
i.e.
offset X Y Z
0.0 -0.140137 -1.924316 -0.426758
10.0 -2.789123 -1.111212 -0.416016
20.0 -0.133789 -1.923828 -4.408691
30.0 -0.101112 -1.457891 -0.425781
40.0 -0.658125 -1.668801 -1.218262
50.0 -0.657539 -1.667336 -1.213769
60.0 -0.125789 -2.202012 -1.328097
70.0 -0.124031 -2.200938 -0.527121
80.0 -0.128500 -2.292856 -0.524679
90.0 -0.128500 -2.292856 -0.508578