By far the best solution is to avoid representing numeric values as categorical values in the first place. If you can un-do that, that's the best solution.
If that cannot be done, here's how to convert categorical values that contain numeric values in a table.
Table T can contain mixed classes (some classes may cause errors).
This demo detects which columns of T contains values that can be converted to numers. It then creates an output table T_converted that contains the num-categorical-number columns of T and the categorical-number columns converted to numbers.
T = table(["A";"B";"C";"D";"E"], ...
categorical(randi(10,5,1)), ...
categorical(randi(10,5,1)), ...
'VariableNames', {'A','B','C','D'})
T =
A B C D
___ __ _ _
"A" 2 3 4
"B" 3 4 8
"C" 9 6 8
"D" 9 4 5
"E" 10 5 6
varfun(@class, T)
ans =
class_A class_B class_C class_D
_______ ___________ _______ ___________
string categorical double categorical
iscat = varfun(@iscategorical, T,'OutputFormat','Uniform');
Tnum = array2table(str2double(string(T{:,iscat})), ...
'VariableNames', T.Properties.VariableNames(iscat));
T_converted = [T(:, ~iscat), Tnum];
[~,colorder] = ismember(T_converted.Properties.VariableNames, T.Properties.VariableNames);
T_converted(:,colorder)
ans =
A B C D
___ __ _ _
"A" 2 3 4
"B" 3 4 8
"C" 9 6 8
"D" 9 4 5
"E" 10 5 6
varfun(@class, T_converted)
ans =
class_A class_C class_B class_D
_______ _______ _______ _______
string double double double
This answer was corrected on 9/23/22; thanks to Ahmed Rady for pointing out the problem
