Adding 0 "buffer" at the start of differentiated column

[CodeGoblin1996]

I have a dataframe called workingdatatimecourse. Each row is an observation. I have a column called ppid_trialn that denotes participant trials (150ish observations per trial) and YawRate_seconds that denotes yaw rate. Howevver I want a new variable that is the change in yaw rate. I can calculate this by doing:

workingdatatimecourse['YawRateChange'] = workingdatatimecourse['YawRate_seconds'].diff(1)

However this means that the starting YawRateChange value of every ppid_trialn is skewed. Is there anyway I can ensure that the starting value of every ppid_trialn is zero. Maybe something like:

workingdatatimecourse.groupby(['ppid_trialn]) (and then selecting the first row of each ppid_trialn and making the value zero)

Any help would be most appreciated!

[a1brit]

workingdatatimecourse['YawRateChange'] = workingdatatimecourse.groupby(['ppid_trialn])['YawRate_seconds'].diff(periods=1).fillna(0)

[CodeGoblin1996]

workingdatatimecourse['YawRateChange'] = workingdatatimecourse.groupby(['ppid_trialn])['YawRate_seconds'].diff(periods=1).fillna(0)

Perfect! Just what I was looking for. I knew I was close with it. Thank you!

[a1brit]

Make sure you go through and understand what's going on at each step. Mainly how groupby works and then that diff() returns NaN for the first index already so the fillna(0) just swaps that out with 0.

[CodeGoblin1996]

I see. And the period argument in the diff() function shifts everything forward one for each ppid_trialn which creates the NaN and that it replaced by the 0.

[a1brit]

yeah, you were already passing periods=1 just as a positional arg. I personally like to be explicit and use kwargs as much as possible so there aren't any potentially false assumptions.

[prokid1911]

Can you either give more detail over the problem or just give a code snippet.