I'm pretty new to coding and have a problem resampling my dataframe with Pandas. I need to resample my data ("value") to means for every 10 minutes (13:30, 13:40, etc.). The problem is: The data start around 13:36 and I can't access them by hand because I need to do this for 143 dataframes. Resampling adds the mean at the respective index (e.g. 13:40 for the second value), but because 13:30 is not part of my indices, that value gets lost.
I'm trying two different approaches here: First, I tried every option of resample() (offset, origin, convention, ...). Then I tried adding the missing values manually with a loop, which doesn't run properly because I didn't know how to access the correct spot on the list. The list does include all relevant values though. I also tried adding a row with 13:30 as the index on top of the dataframe but didn't manage to convince Python that my index is legit because it's a timestamp (this is not in the code).
Sorry for the very rough code, it just didn't work in several places which is why I'm asking here.
If you have a possible solution, please keep in mind that it has to function within an already long loop because of the many dataframes I have to work on simultaneously.
Thank you very much!
df["tenminavg"] = df["value"].resample("10Min").mean()
df["tenminavg"] = df["tenminavg"].ffill()
ls1 = df["value"].resample("10Min").mean() #my alternative: list the resampled values in order to eventually access the first relevant timespan
for i in df.index: #this loop doesn't work. It should add the value for the first 10 min
if df["tenminavg"][i]=="nan":
if datetime.time(13,30) <= df.index.time < datetime.time(13,40):
df["tenminavg"][i] = ls1.index.loc[i.floor("10Min")]["value"] #tried to access the corresponding data point in the list
else:
continue
