How to find the total value by category in a table
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Cris LaPierre
2019-1-2
编辑:Cris LaPierre
2019-1-2
G = findgroups(Table.Clase);
Result = splitapply(@sum,Table.Monto,G);
Result = table(categories(Table.Clase),Result,'VariableNames',{'Clase','Monto'})
7 个评论
Cris LaPierre
2019-1-4
Ah, you are in 2017a. It looks like reordercats did not yet support the string data type.
Update the line that uses string to use cellstr instead
[cats,ia,~] = unique(cellstr(Table.Clase));
更多回答(2 个)
Peter Perkins
2019-1-23
The problem with Chris' solution is that by going through findgroups in that way, you lose all the categoricalness of your grouping variable. You can work around that in splitapply in a few ways, one of which Chris shows, but here's something more direct:
>> Clase = categorical({'w'; 'w'; 'w'; 'b'; 'b'; 'y'},{'w' 'b' 'y'});
>> Monto = [1;2;3;4;5;6];
>> t = table(Clase,Monto)
t =
6×2 table
Clase Monto
_____ _____
w 1
w 2
w 3
b 4
b 5
y 6
>> tsum = varfun(@mean,t,'GroupingVariable','Clase')
tsum =
3×3 table
Clase GroupCount mean_Monto
_____ __________ __________
w 3 2
b 2 4.5
y 1 6
>> categories(tsum.Clase)
ans =
3×1 cell array
{'w'}
{'b'}
{'y'}
In (very) recent versions of MATLAB, there's also groupsummary:
>> groupsummary(t,"Clase","mean")
ans =
3×3 table
Clase GroupCount mean_Monto
_____ __________ __________
w 3 2
b 2 4.5
y 1 6
varfun only works for tables, whereas groupsummary is more widel applicable.
1 个评论
Cris LaPierre
2019-1-23
Not sure what you mean by losing its categoricalness.
>> summary(Result)
Variables:
Clase: 3×1 categorical
Values:
White 1
Black 1
Yellow 1
Monto: 3×1 double
Values:
Min 4
Median 5
Max 6
Image Analyst
2019-1-2
You can use grpstats() to get the stats by group, if you have the Statistics and Machine Learning Toolbox. It doesn't have sum but it has count and mean so you can multiply those to get the sum. Attach your table in a .mat file, and your expected results if you need more guidance.
3 个评论
Image Analyst
2019-1-3
OK, no problem. You accepted it so it looks like it's solved your problem. Or maybe not?
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