# How to compute monthly average and standard deviation with for loop

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Valerio Gianforte on 25 May 2020
Commented: Ameer Hamza on 25 May 2020
Hi everyone,
I have to compute the monthly average and standard deviation on each year. I tryed to do this one but I obtain the error 'The logical indices contain a true value outside of the array bounds.' and I don't know how to fix it.
format long g
folder = 'D:\Valerio\data\IPCC_midcent\RCP4.5\BCC_CSM\BCC_CSM.xlsx';
start_yy = 2026;
end_yy = 2044;
range_yy = (start_yy:end_yy).';
r_yy = length(range_yy);
dt = datetime([file(:,1:3) file(:,4)/1E4 repmat([0 0],size(file,1),1)]);
tt = timetable(dt, file(:,5:end));
data = tt.Var1;
a = datenum({'01-Jan-2026','31-Dec-2026'});
aa = datevec(a(1):1:a(2));
Out = aa(:,1:3);
mm = Out(:,2);
for i = 1:r_yy
s1 = sprintf('01-Jan-%d',2025+i);
s2 = sprintf('01-Jan-%d',2026+i);
TR = timerange(s1,s2);
tt_TR = tt(TR,:);
data_TR = tt_TR.Var1;
Hs = data_TR(:,1);
Tp = data_TR(:,2);
for j = 1:12
mm_Hs(j,i) = mean(Hs(mm == j));
mm_Tp(j,i) = mean(Tp(mm == j));
dev_Hs(j,i) = std(Hs(mm == j));
dev_Tp(j,i) = std(Tp(mm == j));
end
end
The error is at the first iteration of the second for loop. Is there someone that can help me. Thank you so much.

Ameer Hamza on 25 May 2020
If you have Statistics and Machine Learning Toolbox, then you can use grpstat(). For example
format long g
folder = 'BCC_CSM.xlsx';
T(:,2:4) = []; % delete month day and hour columns as they are not important for yearly mean
T.Properties.VariableNames = {'year', 'data1', 'data2', 'data3'};
result = grpstats(T, 'year', {'mean', 'std'});
result_mean = grpstats(T, 'year', 'mean');
result_std = grpstats(T, 'year', 'std');
Otherwise you can use splitapply()
data(:, 2:4) = []; % delete month day and hour columns as they are not important for yearly mean
[grps, years] = findgroups(data(:,1));
result_mean = splitapply(@mean, data(:,2:end), grps);
result_mean = [years result_mean];
result_std = splitapply(@std, data(:,2:end), grps);
result_std = [years result_std];
Ameer Hamza on 25 May 2020
In that case, try following codes.
grpstat()
format long g
folder = 'BCC_CSM.xlsx';
T(:,3:4) = []; % delete month day and hour columns as they are not important for yearly mean
T.Properties.VariableNames = {'year', 'months', 'data1', 'data2', 'data3'};
result = grpstats(T, {'year', 'months'}, {'mean', 'std'});
result_mean = grpstats(T, {'year', 'months'}, 'mean');
result_std = grpstats(T, {'year', 'months'}, 'std');
splitapply()
data(:, 3:4) = []; % delete month day and hour columns as they are not important for yearly mean
[grps, Years, Months] = findgroups(data(:,1), data(:,2));
result_mean = splitapply(@(x) mean(x, 1), data(:,3:end), grps);
result_mean = [Years Months result_mean];
result_std = splitapply(@(x) std(x, [], 1), data(:,3:end), grps);
result_std = [Years Months result_std];

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