"First row is date second is hourly mean temp."
That would be column, not row, as just a nit.
Yes, either groupsummary or varfun will do the job, but you need to create the grouping variable to use them...beginning with a timetable, tt that looks like the above, then
>> tt.Day=day(tt.Date);
>> varfun(@mean,tt,'groupingvariables','Day')
ans =
2×3 timetable
Date Day GroupCount mean_Temp
____________________ ___ __________ _________
07-Mar-2019 11:00:00 7 13 15.692
08-Mar-2019 00:00:00 8 8 13.5
>> groupsummary(tt,'Day','mean')
ans =
2×3 table
Day GroupCount mean_Temp
___ __________ _________
7 13 15.692
8 8 13.5
>> [g,ig]=findgroups(tt.Day);
>> splitapply(@mean,tt.Temp,g)
ans =
15.6923
13.5000
>> table(tt.Date(ig),splitapply(@mean,tt.Temp,g))
ans =
2×2 table
Var1 Var2
____________________ ______
07-Mar-2019 17:00:00 15.692
07-Mar-2019 18:00:00 13.5
>>
are various routes to the end...
>> retime(timetable(tt.Date(ig),splitapply(@mean,tt.Temp,g),'VariableNames',{'MeanTemp'}),'daily','nearest')
ans =
2×1 timetable
Time MeanTemp
____________________ ________
07-Mar-2019 00:00:00 15.692
08-Mar-2019 00:00:00 13.5
>>
also gets rid of the odd beginning hour for each day if your data aren't all collected beginning at 00:00 each day.
You could add some error checking to ensure that don't include days that are just nightime or daytime measurements so those would bias results where there is some extreme from day to night.
ERRATUM:
Average over hours, not days as misread original intent:
tt.Hour=hour(tt.Time);
>> varfun(@mean,tt,"InputVariables",'Temp',"GroupingVariables",'Hour')
ans =
21×3 timetable
Time Hour GroupCount mean_Temp
____________________ ____ __________ _________
08-Mar-2019 00:00:00 0 1 16
08-Mar-2019 01:00:00 1 1 15
08-Mar-2019 02:00:00 2 1 13
08-Mar-2019 03:00:00 3 1 12
08-Mar-2019 04:00:00 4 1 16
08-Mar-2019 05:00:00 5 1 12
08-Mar-2019 06:00:00 6 1 12
08-Mar-2019 07:00:00 7 1 12
07-Mar-2019 11:00:00 11 1 16
07-Mar-2019 12:00:00 12 1 14
07-Mar-2019 13:00:00 13 1 12
07-Mar-2019 14:00:00 14 1 19
07-Mar-2019 15:00:00 15 1 23
07-Mar-2019 16:00:00 16 1 14
07-Mar-2019 17:00:00 17 1 15
07-Mar-2019 18:00:00 18 1 18
07-Mar-2019 19:00:00 19 1 17
07-Mar-2019 20:00:00 20 1 13
07-Mar-2019 21:00:00 21 1 14
07-Mar-2019 22:00:00 22 1 14
07-Mar-2019 23:00:00 23 1 15
>>
Best Answer