Divide annual timeseries to monthly ones

For a specific year I have created an hourly timetable using retime:

VarPerHour = retime(T, 'hourly', 'sum');

How can I divide VarPerHour into 12 monthly timetables?

Answer 1

[Star Strider]

A for loop is the easiest way to do this —

LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;

VarPerHour = retime(T, 'hourly', 'sum')

VarPerHour = 8760×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Jan-19 00:00:00         588   
    01-Jan-19 01:00:00      608.11   
    01-Jan-19 02:00:00      608.25   
    01-Jan-19 03:00:00      608.33   
    01-Jan-19 04:00:00      608.25   
    01-Jan-19 05:00:00       608.4   
    01-Jan-19 06:00:00      608.59   
    01-Jan-19 07:00:00       608.9   
    01-Jan-19 08:00:00      609.32   
    01-Jan-19 09:00:00      599.51   
    01-Jan-19 10:00:00      609.61   
    01-Jan-19 11:00:00      609.51   
    01-Jan-19 12:00:00      609.39   
    01-Jan-19 13:00:00      609.44   
    01-Jan-19 14:00:00      609.58   
    01-Jan-19 15:00:00      609.83   

for k = 1:12
    MMidx = month(VarPerHour.date_time) == k;
    VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end

VarPerHourMonth

VarPerHourMonth = 12×1 cell array
    {744×1 timetable}
    {672×1 timetable}
    {744×1 timetable}
    {720×1 timetable}
    {744×1 timetable}
    {720×1 timetable}
    {744×1 timetable}
    {744×1 timetable}
    {720×1 timetable}
    {744×1 timetable}
    {720×1 timetable}
    {744×1 timetable}

VarPerHourMonth{1}(1:5,:)

ans = 5×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Jan-19 00:00:00         588   
    01-Jan-19 01:00:00      608.11   
    01-Jan-19 02:00:00      608.25   
    01-Jan-19 03:00:00      608.33   
    01-Jan-19 04:00:00      608.25   

VarPerHourMonth{12}(1:5,:)

ans = 5×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Dec-19 00:00:00      590.64   
    01-Dec-19 01:00:00       193.5   
    01-Dec-19 02:00:00           0   
    01-Dec-19 03:00:00           0   
    01-Dec-19 04:00:00           0   

This uses an existing timetable. It should work with the one you are currently using as well.

.

[Ancalagon8]

Ok @Star Strider that worked fine. Instead of receiving 2 collumns, how can I receive per month a table like the attached one?

T2=T.rainPerHourMonthly{1,1}(:,1:2); %JANUARY
T2datesonly = table(T2.date_time.Day,T2.date_time.Month,T2.date_time.Year,'VariableNames',{'DD','MM','YYYY'});
T2timesOnly = table(T2.date_time.Hour,T2.date_time.Minute, 'VariableNames',{'HH','mm'});

Right now I have splitted the day and the hour, and I assume that i will have to transpose the hours. But how can I fill the gaps with the right corresponding Var values?

[Star Strider]

This was an adventure!

This looks a bit more complicated than it actually is.

I can’t get them exactly in the format you want, however this is reasonably close.

In any event, it’s the best I can do —

LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;

VarPerHour = retime(T, 'hourly', 'sum')

VarPerHour = 8760×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Jan-19 00:00:00         588   
    01-Jan-19 01:00:00      608.11   
    01-Jan-19 02:00:00      608.25   
    01-Jan-19 03:00:00      608.33   
    01-Jan-19 04:00:00      608.25   
    01-Jan-19 05:00:00       608.4   
    01-Jan-19 06:00:00      608.59   
    01-Jan-19 07:00:00       608.9   
    01-Jan-19 08:00:00      609.32   
    01-Jan-19 09:00:00      599.51   
    01-Jan-19 10:00:00      609.61   
    01-Jan-19 11:00:00      609.51   
    01-Jan-19 12:00:00      609.39   
    01-Jan-19 13:00:00      609.44   
    01-Jan-19 14:00:00      609.58   
    01-Jan-19 15:00:00      609.83   

for k = 1:12
    MMidx = month(VarPerHour.date_time) == k;
    VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end

for k = 1:12
    TTTemp = VarPerHourMonth{k};                                                                                % Create Temporary 'timetable'
    Hours = hour(TTTemp.date_time);                                                                             % Create 'Hours' Variable
    [y,m,d] = ymd(TTTemp.date_time);                                                                            % Begin To Create 'Date' Variable
    Date = datetime(y,m,d);                                                                                     % Finish Creating 'Date' Variable
    TTTemp = addvars(TTTemp, Date, Hours,'Before','Temperature');                                               % Add  'Hours' & 'Date' Variables
    TTTemp.Properties.VariableNames(1:2) = {'Date','Hours'};                                                    % Name 'Hours' & 'Date' Variables
    TTTempT = timetable2table(TTTemp);                                                                          % Convert To 'table'
    VarPerHourMonthT{k,:} = unstack(TTTempT(:,2:end),'Temperature','Hours', 'VariableNamingRule','preserve');   % Unstack & Write To Cell Array
end

VarPerHourMonthT                                                                                                % Display Results

VarPerHourMonthT = 12×1 cell array
    {31×25 table}
    {28×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}
    {30×25 table}
    {31×25 table}

VarPerHourMonthT{1}(1:5,:)

ans = 5×25 table
       Date          0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15        16        17        18        19        20        21        22        23  
    ___________    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______

    01-Jan-2019       588    608.11    608.25    608.33    608.25     608.4    608.59     608.9    609.32    599.51    609.61    609.51    609.39    609.44    609.58    609.83    609.97    610.21    610.35    610.42    610.52    610.54    610.72    610.54
    02-Jan-2019    589.97    610.02    609.79    609.47    609.06    608.76    608.66    608.54    608.66    608.75    608.57    608.08    607.77    607.33    606.85    606.66     606.4    606.22    606.29    606.31    606.17    605.95    605.57    514.48
    03-Jan-2019    574.56    604.75    604.71    604.53    604.05    603.61    603.22    603.14    603.77    604.38    604.76    604.79    604.45    604.75    604.73    604.92    605.31    605.53    606.32    606.63    607.06    607.59    607.94    608.23
    04-Jan-2019    588.18    608.74    608.99    609.25    609.32     609.3    609.35    609.49    609.92    610.62    610.78    610.59    610.29    610.01    609.96    609.88    609.79    610.11    610.26    610.51    610.71    610.28    610.17    610.06
    05-Jan-2019    589.38    609.63    609.23    609.37    609.24    608.86    608.74    608.44    608.65    609.31    609.42    609.41     608.9    608.53    608.53     608.6     608.7    608.82    609.15    609.34    609.36    609.36    609.39    609.23

VarPerHourMonthT{12}(1:5,:)

ans = 5×25 table
       Date          0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15        16        17        18        19        20        21        22        23  
    ___________    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______

    01-Dec-2019    590.64     193.5         0         0         0         0         0         0         0         0         0    603.21    613.27    613.16    613.12    613.12    613.17    163.52         0         0         0         0         0         0
    02-Dec-2019    583.83    614.47    614.55    614.53    614.32    614.18    614.23    614.26    614.48    614.56     614.6    614.43    614.13    613.97    613.75    613.47    613.22    613.05    612.97    613.07    613.12    613.15    613.04    613.09
    03-Dec-2019    592.37    612.51    612.56    612.35    611.93    611.72    611.59     611.5    611.43    611.41    611.53    611.47    611.56    611.59    611.35     611.2     611.2    611.22    611.07    611.13    611.06    611.07    611.08    610.76
    04-Dec-2019    589.94    610.24    610.45    610.26    609.94    609.84    609.82    610.14    610.25    610.56    611.45    600.92    610.75    610.62    610.46    590.17    152.67         0         0         0         0         0         0         0
    05-Dec-2019    583.18    613.92    614.01    614.16    614.15    614.12    614.21    614.31    614.68    604.68    614.93    614.73    614.95    614.92    614.91    614.97    614.94    615.21    615.25    615.63    615.94       616    616.06    616.11

The new ‘VarPerHourMonthT’ cell array is of table arrays, not timetable arrays because I doubt that timetable arrays would support this format. The essential function here is the unstack function, and most of the code in the loop iterations is devoted to creating table arrays that are compatible with it. Using unstack on the timetable arrays themselves is possible (I did that first), however they do not produce the result you want, so I adopted this approach instead. (The problem with using unstack on the timetable arrays themselves is that it produces a diagonal matrix of ‘Temperature’ sums in a matrix of NaN values for each hour, rather than a single row. To get the result you want required creating both the ‘Date’ and ‘Hour’ variables, converting the timetable arrays to table arrays, eliminating the ‘date_time’ variable, and then using unstack on the table arrays.)

I did my best to comment-document my code here, along with describing it.

.

[Ancalagon8]

Thank you for the detailed approach. Using your dataset I receive the following error:

Error using tabular/unstack (line 112)
Invalid parameter name: VariableNamingRule.

[Star Strider]

My pleasure!

My code works in R2022b. Since you apparently have a different (earlier) version/release (that I may not have access to even if I knew what it was), you may have to experiment with my code to get the same result. For the time being, eliminate that part of my unstack call and hope for the best! (It may be necessary to use the ‘VarPerHourMonthT{k,:}.Properties.VariableNames’ or ‘VarPerHourMonthT.Properties.VariableNames’ option to set their names. I am hesitant to experiment with that with my code since it works as written.)

Upgrading to R2022b would be the easiest option if that is available to you.

[Ancalagon8]

I will try to upgrade to R2022b version. Thanks a lot!

[Star Strider]

As always, my pleasure!

[Ancalagon8]

I tried succesfully to export the 12 excel files from VarPerHourMonthT:

for i = 1:numel(VarPerHourMonthT)
    F = sprintf('month_%d.xlsx',i);
    writetable(VarPerHourMonthT{i},F)
end

Is it possible to save them into one file with 12 sheets?

[Star Strider]

Yes.

See the documentation section on Spreadsheet Files Only . It will probably be necesary to use a for loop to write to each sheet. (I have never had to do this, so I have no experience with it.)

Testing it here —

LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;

VarPerHour = retime(T, 'hourly', 'sum')

VarPerHour = 8760×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Jan-19 00:00:00         588   
    01-Jan-19 01:00:00      608.11   
    01-Jan-19 02:00:00      608.25   
    01-Jan-19 03:00:00      608.33   
    01-Jan-19 04:00:00      608.25   
    01-Jan-19 05:00:00       608.4   
    01-Jan-19 06:00:00      608.59   
    01-Jan-19 07:00:00       608.9   
    01-Jan-19 08:00:00      609.32   
    01-Jan-19 09:00:00      599.51   
    01-Jan-19 10:00:00      609.61   
    01-Jan-19 11:00:00      609.51   
    01-Jan-19 12:00:00      609.39   
    01-Jan-19 13:00:00      609.44   
    01-Jan-19 14:00:00      609.58   
    01-Jan-19 15:00:00      609.83   

for k = 1:12
    MMidx = month(VarPerHour.date_time) == k;
    VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end

for k = 1:12
    TTTemp = VarPerHourMonth{k};                                                                                % Create Temporary 'timetable'
    Hours = hour(TTTemp.date_time);                                                                             % Create 'Hours' Variable
    [y,m,d] = ymd(TTTemp.date_time);                                                                            % Begin To Create 'Date' Variable
    Date = datetime(y,m,d);                                                                                     % Finish Creating 'Date' Variable
    TTTemp = addvars(TTTemp, Date, Hours,'Before','Temperature');                                               % Add  'Hours' & 'Date' Variables
    TTTemp.Properties.VariableNames(1:2) = {'Date','Hours'};                                                    % Name 'Hours' & 'Date' Variables
    TTTempT = timetable2table(TTTemp);                                                                          % Convert To 'table'
    VarPerHourMonthT{k,:} = unstack(TTTempT(:,2:end),'Temperature','Hours', 'VariableNamingRule','preserve');   % Unstack & Write To Cell Array
end

Filename = 'RainPerHourMonth.xlsx';
for k = 1:12
    writetable(VarPerHourMonthT{k}, Filename, 'Sheet',k)
end

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

Warning: Added specified worksheet.

T6 = readtable(Filename, 'Sheet',6, 'VariableNamingRule','preserve')            % Check 6

T6 = 30×25 table
       Date          0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15        16        17        18        19        20        21        22        23  
    ___________    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______

    01-Jun-2019    590.12    610.21    609.91    609.69    609.39    609.26    609.45    609.56    609.62    609.79    609.73     609.8    609.85    609.93    609.99    610.05    609.95     609.9    609.82    609.79    609.87    610.11    610.22    610.32
    02-Jun-2019    589.91       610    609.75    609.56    609.36    609.43     609.4    609.48    609.66    609.82    609.93    610.05    610.08    610.03    609.77    609.73    609.75    609.61    609.64    609.86    610.03    610.06    610.54    559.69
    03-Jun-2019    590.07    610.38    610.19    609.93    609.65    609.56    609.55    609.72     610.1    610.23    610.29    610.35    610.37    610.34     610.3    610.31    610.13    610.13    609.77    609.69    609.61     609.7    609.99    610.16
    04-Jun-2019     589.7    609.81    609.39    609.14    608.96    608.84    608.93    608.96    609.02    609.24    609.31    609.47     609.6    609.56    609.54    609.37    609.11    608.87     608.7    608.59    608.64    608.66    608.87    608.92
    05-Jun-2019    588.63     608.9    608.82     608.6    608.47    608.43    608.46    608.49    608.58     608.8    608.85    608.95    609.06    608.94    608.78    608.62    608.55    608.46    608.38    608.45    608.63    608.85    609.36    609.43
    06-Jun-2019    589.19    609.53     609.6    609.66    609.68    609.82    609.87    610.12    610.34    610.58    610.71     610.7    610.89    610.93    610.89    610.83    610.69    610.58     610.6    610.62    610.87    610.99     611.3    611.32
    07-Jun-2019    591.04    611.48    611.42    611.23    611.21     611.2    611.38    611.55    611.61    611.72    611.79    611.83    611.97    611.91    611.84    611.74    611.61    611.56    611.45    611.39    611.56    611.71    612.04    612.22
    08-Jun-2019    591.75    612.06    611.75    611.64    611.57    611.44    611.45    611.56    611.67    611.79    611.78    611.53    611.46    611.41     611.2    610.94    610.69     610.4    610.22    610.18    610.24    610.43     610.6    610.84
    09-Jun-2019    590.49    610.77    610.68    610.37    610.12    609.74     609.8    609.97    610.16     610.2    609.81    609.51    609.18     172.6         0         0         0         0         0         0         0         0         0         0
    10-Jun-2019         0         0         0         0         0         0         0         0         0    577.48    607.67    607.64    607.41    607.18    606.86    606.63    363.89    606.34     606.1    606.23     606.5    606.62    606.69    606.93
    11-Jun-2019     586.4    606.44    606.44    606.31    606.12    605.98    606.08    606.26     606.4    606.48    606.49    606.62    606.75    606.81    606.55     606.3    606.37    606.63    607.04    607.08    607.06     607.5    607.95    607.93
    12-Jun-2019    587.48    607.62    607.39    607.33    607.21    607.23     607.5    607.91    608.08    608.17    608.36    608.57    608.55    608.54     608.4    608.31     608.3    608.16    608.33     608.3    608.33    608.55    609.12    609.08
    13-Jun-2019    588.75    609.03    608.79    608.66    608.59    608.66    608.89    609.09    609.46    609.72     609.8    609.93     609.9    609.72    609.48    609.34    609.24    609.14    609.03    608.98    609.02    609.13    609.46    609.63
    14-Jun-2019    589.38     609.6    609.52    609.36    609.17    609.03    609.18    609.22     609.3    609.42    609.47    609.48    609.43    609.39     588.9    588.49    608.61    608.54    608.32    608.25    608.37    608.58    608.49    608.46
    15-Jun-2019    588.22    608.57    608.22    607.96    607.86    607.64    607.61    607.68    607.83    607.89    607.87     607.7    607.65    607.37    607.15    606.85    606.86    606.62    606.46    606.43    606.35    606.53    606.74    607.02
    16-Jun-2019    586.81    606.99    607.09    607.03    606.91    606.99    607.15    607.26    607.16    607.16    607.21    607.25    587.09     607.3    606.87    606.55    606.65    607.31     606.8    606.56    606.81    607.04    607.25    607.52

I do not understand the writetable warnings. It seems to work.

.

[Star Strider]

In your edit to your previous Comment, did anything change that requires a specific reply? (To the best of my knowledge, I do not have the table you are referring to, so I am using the one I have, and the code I created previously to work with it.)

[Ancalagon8]

No @Star Strider, I had a typo error and correct it!

[Star Strider]

Thank you!

[Ancalagon8]

In this loop:

Filename = 'RainPerHourMonth.xlsx';
for k = 1:12
    writetable(VarPerHourMonthT{k}, Filename, 'Sheet',k)
end

can i name each sheet with MMM?

[Star Strider]

Let’s do that experiment —

LD = load(websave('dataset','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1255052/dataset.mat'));
T = LD.TT1;

VarPerHour = retime(T, 'hourly', 'sum')

VarPerHour = 8760×1 timetable
        date_time         Temperature
    __________________    ___________

    01-Jan-19 00:00:00         588   
    01-Jan-19 01:00:00      608.11   
    01-Jan-19 02:00:00      608.25   
    01-Jan-19 03:00:00      608.33   
    01-Jan-19 04:00:00      608.25   
    01-Jan-19 05:00:00       608.4   
    01-Jan-19 06:00:00      608.59   
    01-Jan-19 07:00:00       608.9   
    01-Jan-19 08:00:00      609.32   
    01-Jan-19 09:00:00      599.51   
    01-Jan-19 10:00:00      609.61   
    01-Jan-19 11:00:00      609.51   
    01-Jan-19 12:00:00      609.39   
    01-Jan-19 13:00:00      609.44   
    01-Jan-19 14:00:00      609.58   
    01-Jan-19 15:00:00      609.83   

for k = 1:12
    MMidx = month(VarPerHour.date_time) == k;
    VarPerHourMonth{k,:} = VarPerHour(MMidx,:);
end

for k = 1:12
    TTTemp = VarPerHourMonth{k};                                                                                % Create Temporary 'timetable'
    Hours = hour(TTTemp.date_time);                                                                             % Create 'Hours' Variable
    [y,m,d] = ymd(TTTemp.date_time);                                                                            % Begin To Create 'Date' Variable
    Date = datetime(y,m,d);                                                                                     % Finish Creating 'Date' Variable
    TTTemp = addvars(TTTemp, Date, Hours,'Before','Temperature');                                               % Add  'Hours' & 'Date' Variables
    TTTemp.Properties.VariableNames(1:2) = {'Date','Hours'};                                                    % Name 'Hours' & 'Date' Variables
    TTTempT = timetable2table(TTTemp);                                                                          % Convert To 'table'
    VarPerHourMonthT{k,:} = unstack(TTTempT(:,2:end),'Temperature','Hours', 'VariableNamingRule','preserve');   % Unstack & Write To Cell Array
    MMM{k,:} = month(TTTemp.date_time(1,:),'shortname');
end

Filename = 'RainPerHourMonth.xlsx';
for k = 1:12
    writetable(VarPerHourMonthT{k}, Filename, 'Sheet',string(MMM{k}))
end

T6 = readtable(Filename, 'Sheet','Jun', 'VariableNamingRule','preserve')        % Check 6

T6 = 30×25 table
       Date          0         1         2         3         4         5         6         7         8         9         10        11        12        13        14        15        16        17        18        19        20        21        22        23  
    ___________    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______    ______

    01-Jun-2019    590.12    610.21    609.91    609.69    609.39    609.26    609.45    609.56    609.62    609.79    609.73     609.8    609.85    609.93    609.99    610.05    609.95     609.9    609.82    609.79    609.87    610.11    610.22    610.32
    02-Jun-2019    589.91       610    609.75    609.56    609.36    609.43     609.4    609.48    609.66    609.82    609.93    610.05    610.08    610.03    609.77    609.73    609.75    609.61    609.64    609.86    610.03    610.06    610.54    559.69
    03-Jun-2019    590.07    610.38    610.19    609.93    609.65    609.56    609.55    609.72     610.1    610.23    610.29    610.35    610.37    610.34     610.3    610.31    610.13    610.13    609.77    609.69    609.61     609.7    609.99    610.16
    04-Jun-2019     589.7    609.81    609.39    609.14    608.96    608.84    608.93    608.96    609.02    609.24    609.31    609.47     609.6    609.56    609.54    609.37    609.11    608.87     608.7    608.59    608.64    608.66    608.87    608.92
    05-Jun-2019    588.63     608.9    608.82     608.6    608.47    608.43    608.46    608.49    608.58     608.8    608.85    608.95    609.06    608.94    608.78    608.62    608.55    608.46    608.38    608.45    608.63    608.85    609.36    609.43
    06-Jun-2019    589.19    609.53     609.6    609.66    609.68    609.82    609.87    610.12    610.34    610.58    610.71     610.7    610.89    610.93    610.89    610.83    610.69    610.58     610.6    610.62    610.87    610.99     611.3    611.32
    07-Jun-2019    591.04    611.48    611.42    611.23    611.21     611.2    611.38    611.55    611.61    611.72    611.79    611.83    611.97    611.91    611.84    611.74    611.61    611.56    611.45    611.39    611.56    611.71    612.04    612.22
    08-Jun-2019    591.75    612.06    611.75    611.64    611.57    611.44    611.45    611.56    611.67    611.79    611.78    611.53    611.46    611.41     611.2    610.94    610.69     610.4    610.22    610.18    610.24    610.43     610.6    610.84
    09-Jun-2019    590.49    610.77    610.68    610.37    610.12    609.74     609.8    609.97    610.16     610.2    609.81    609.51    609.18     172.6         0         0         0         0         0         0         0         0         0         0
    10-Jun-2019         0         0         0         0         0         0         0         0         0    577.48    607.67    607.64    607.41    607.18    606.86    606.63    363.89    606.34     606.1    606.23     606.5    606.62    606.69    606.93
    11-Jun-2019     586.4    606.44    606.44    606.31    606.12    605.98    606.08    606.26     606.4    606.48    606.49    606.62    606.75    606.81    606.55     606.3    606.37    606.63    607.04    607.08    607.06     607.5    607.95    607.93
    12-Jun-2019    587.48    607.62    607.39    607.33    607.21    607.23     607.5    607.91    608.08    608.17    608.36    608.57    608.55    608.54     608.4    608.31     608.3    608.16    608.33     608.3    608.33    608.55    609.12    609.08
    13-Jun-2019    588.75    609.03    608.79    608.66    608.59    608.66    608.89    609.09    609.46    609.72     609.8    609.93     609.9    609.72    609.48    609.34    609.24    609.14    609.03    608.98    609.02    609.13    609.46    609.63
    14-Jun-2019    589.38     609.6    609.52    609.36    609.17    609.03    609.18    609.22     609.3    609.42    609.47    609.48    609.43    609.39     588.9    588.49    608.61    608.54    608.32    608.25    608.37    608.58    608.49    608.46
    15-Jun-2019    588.22    608.57    608.22    607.96    607.86    607.64    607.61    607.68    607.83    607.89    607.87     607.7    607.65    607.37    607.15    606.85    606.86    606.62    606.46    606.43    606.35    606.53    606.74    607.02
    16-Jun-2019    586.81    606.99    607.09    607.03    606.91    606.99    607.15    607.26    607.16    607.16    607.21    607.25    587.09     607.3    606.87    606.55    606.65    607.31     606.8    606.56    606.81    607.04    607.25    607.52

For some reason, writetable doesn’t like the cell array (even though indexing into it should produce a character array), however it accepts the string argument. Referring to it by name in the readtable test works. (The ‘MMM’ cell array didn’t initially appear in this version of my code, so I added it in the loop.)

So, an emphatic ‘Yes!’

.

[Ancalagon8]

Worked perfect (as always)! Thanks!!

[Star Strider]

As always, my pleasure!

Answer 2

[Christopher McCausland]

Hi Ancalogon,

Having a look at the documentation here; you can just replace hourly with any timestep such as:

Time Step

'yearly'

'quarterly'

'monthly'

'weekly'

'daily'

'hourly'

'minutely'

'secondly'

Have you tried;

VarPermonth = retime(T, 'monthly', 'sum');

Let me know if this is what you are looking for, if not please provide a snippit of the data and the expected output.

Christopher

[Ancalagon8]

Thanks for your answer @Christopher McCausland.

VarPermonth = retime(T, 'monthly', 'sum');

returns me only one value per month (12X1 timetable).

VarPerHour = retime(T, 'hourly', 'sum') is a 8760X1 timetable (365 days X 24 hours).

I need to split VarPerHour per month but keep all values.

[Christopher McCausland]

Hi Ancalagon,

I get what you want now.

What you really need to do is filter ValPerHour by months, heres an example of how to do so:

https://uk.mathworks.com/matlabcentral/answers/892852-filter-timetable-by-month

And also an ealier suggestion from Walter;

https://uk.mathworks.com/matlabcentral/answers/1561736-extract-data-from-timetable-for-specific-months

I hope this helps!

Christopher