Replacing Negative Values in Table with Previous Value in Column

I have a table 17520x5, in the last 2 columns I would like to replace all negative values with the previous value in that column. This is what I have tried so far and it is not working I still get negative values shown.
My table (T) had 5 columns, variable labels are (A, B, C, D, E) for each column for example
D(D < 0) = NaN;
E(E < 0) = NaN;
T(:, {'D', 'E'}) = fillmissing(T(:,{'D', 'E'}), 'previous');
disp(T)

 采纳的回答

T(:, {'D', 'E'}) = num2cell( fillmissing([D,E], 'previous') );

3 个评论

thank you but this now shows an error that the number of rows does not match the height of the table
We can try an example to show that it works:
T=array2table( rand(4,5)-0.5 ,'Var',{'A','B','C','D','E'});
D=T{:,4}; E=T{:,5};
D(D < 0) = NaN;
E(E < 0) = NaN;
T,
T = 4×5 table
A B C D E _______ ________ ________ ________ ________ 0.44208 0.49326 0.12868 0.42218 0.43518 0.24954 -0.42086 -0.39169 -0.31886 -0.13442 0.38232 -0.3545 -0.49562 -0.47566 -0.15402 0.28615 0.3741 0.11841 -0.16468 0.48483
T(:, {'D', 'E'}) = num2cell( fillmissing([D,E], 'previous') )
T = 4×5 table
A B C D E _______ ________ ________ _______ _______ 0.44208 0.49326 0.12868 0.42218 0.43518 0.24954 -0.42086 -0.39169 0.42218 0.43518 0.38232 -0.3545 -0.49562 0.42218 0.43518 0.28615 0.3741 0.11841 0.42218 0.48483

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更多回答(1 个)

Stef, as near as I can tell, the only thing wrong with your original solution is that D and E are in T, not in the workspace. The following works fine, including repeated negative values and negative values in the first row. There's no need to explicitly pull D and E out of the table. Using Matt's setup:
>> T = array2table(rand(4,5)-0.5 ,'Var',["A" "B" "C" "D" "E"])
T =
4×5 table
A B C D E
________ ________ _________ ________ _________
-0.33782 -0.33435 0.18921 -0.27102 0.038342
0.29428 0.10198 0.24815 0.41334 0.49613
-0.18878 -0.23703 -0.049458 -0.34762 -0.42182
0.028533 0.15408 -0.41618 0.32582 -0.057322
>> T.D(T.D < 0) = NaN;
>> T.E(T.E < 0) = NaN
T =
4×5 table
A B C D E
________ ________ _________ _______ ________
-0.33782 -0.33435 0.18921 NaN 0.038342
0.29428 0.10198 0.24815 0.41334 0.49613
-0.18878 -0.23703 -0.049458 NaN NaN
0.028533 0.15408 -0.41618 0.32582 NaN
>> T(:, ["D" "E"]) = fillmissing(T(:,["D" "E"]), 'previous')
T =
4×5 table
A B C D E
________ ________ _________ _______ ________
-0.33782 -0.33435 0.18921 NaN 0.038342
0.29428 0.10198 0.24815 0.41334 0.49613
-0.18878 -0.23703 -0.049458 0.41334 0.49613
0.028533 0.15408 -0.41618 0.32582 0.49613

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