# Reshaping a matrix based on the first row

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Kendall Galvez il 24 Apr 2024
Commentato: Voss il 27 Apr 2024
I am trying to make a for loop that creates a new array that groups row data into corresponding columns:
My current arrays are something like this (both same size):
[270 270 270 271 272 272 273 273 273]
[12 2 3 14 5 2 6 8 11]
I want 4 columns (each representing 270, 271, 272, 273 respectively) that will produce something like this:
[12 14 5 6; 2 NAN 2 8; 3 NAN NAN 11]
my for loop is currently creating a 9 by 3 array that has the correct values in each column but is producing a bigger shape (more NANs).
for i = 1:length(data1)
for j = 1:length(number)
if data1(i) == number(j) % my dataset == a number array [270, 271, 272, 273]
newArray(i,j) = data2(i);
end
end
end
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### Risposte (2)

Voss il 24 Apr 2024
Here's one way:
A = [270 270 270 271 272 272 273 273 273];
B = [ 12 2 3 14 5 2 6 8 11];
[~,~,cidx] = unique(A,'stable');
ridx = accumarray(cidx,A,[],@(x){1:numel(x)});
ridx = [ridx{:}].';
NR = max(ridx);
NC = max(cidx);
result = NaN(NR,NC);
result(sub2ind([NR,NC],ridx,cidx)) = B
result = 3x4
12 14 5 6 2 NaN 2 8 3 NaN NaN 11
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##### 4 CommentiMostra 2 commenti meno recentiNascondi 2 commenti meno recenti
Kendall Galvez il 26 Apr 2024
A = EventDays
B = Events
Voss il 27 Apr 2024
A = 1x2065
270 270 270 270 270 NaN 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270
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B = 1x2065
15 2 5 4 5 0 0 3 4 9 1 5 5 10 10 11 11 9 7 3 4 6 1 3 6 0 7 0 3 2
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idx = ~isnan(A);
[~,~,cidx] = unique(A(idx),'stable');
ridx = accumarray(cidx,A(idx),[],@(x){1:numel(x)});
ridx = [ridx{:}].';
NR = max(ridx);
NC = max(cidx);
result = NaN(NR,NC);
result(sub2ind([NR,NC],ridx,cidx)) = B(idx)
result = 72x31
15 7 1 4 4 3 9 2 1 6 2 5 4 1 1 1 0 2 1 3 3 2 3 3 1 5 2 1 0 0 2 1 3 0 3 4 2 1 6 13 2 3 2 1 0 0 1 0 5 4 16 14 5 25 0 0 0 0 0 0 5 2 9 1 5 1 0 2 3 7 0 0 3 0 0 2 7 2 1 13 1 1 2 5 2 0 0 0 7 0 4 6 4 1 8 1 0 0 0 1 2 3 3 0 0 4 1 8 3 3 2 8 6 2 9 0 5 0 3 0 5 4 8 6 4 1 4 4 4 4 1 1 3 6 9 1 0 3 12 4 7 2 1 10 0 1 1 0 0 3 0 2 1 2 3 2 8 3 14 5 1 3 2 0 0 0 0 2 3 9 4 6 4 3 0 0 0 0 0 0 3 4 4 3 8 5 6 4 3 5 0 3 0 1 5 3 2 8 10 4 7 6 3 5 1 0 0 0 1 0 4 3 2 6 5 5 1 1 10 0 1 1 1 3 8 1 2 1 3 19 7 2 2 4 0 5 2 1 5 1 9 1 2 7 12 2 2 2 1 0 10 7 6 0 0 0 2 1 16 12 0 5 1 1 0 0 1 0 0 1 1 5 0 1 10 1 3 4 6 4 1 3 23 1 3 0 4 9 12 9 15 11 7 0 0 1 0 0 0 0
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Stephen23 il 27 Apr 2024
Modificato: Stephen23 il 27 Apr 2024
A = [270,270,270,271,272,272,273,273,273];
B = [ 12, 2, 3, 14, 5, 2, 6, 8, 11];
X = ~isnan(A);
C = findgroups(A(X));
R = grouptransform(ones(nnz(X),1),C(:),@cumsum);
M = accumarray([R,C(:)],B(X),[],[],NaN)
M = 3x4
12 14 5 6 2 NaN 2 8 3 NaN NaN 11
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Stephen23 il 27 Apr 2024
Using your uploaded data (my code is unchanged). Note that your data also has NaNs in it, which so far you have not explained how you want to handle. I will remove them.
A = 1x2065
270 270 270 270 270 NaN 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270
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B = 1x2065
15 2 5 4 5 0 0 3 4 9 1 5 5 10 10 11 11 9 7 3 4 6 1 3 6 0 7 0 3 2
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X = ~isnan(A);
C = findgroups(A(X));
R = grouptransform(ones(nnz(X),1),C(:),@cumsum);
M = accumarray([R,C(:)],B(X),[],[],NaN)
M = 72x31
15 7 1 4 4 3 9 2 1 6 2 5 4 1 1 1 0 2 1 3 3 2 3 3 1 5 2 1 0 0 2 1 3 0 3 4 2 1 6 13 2 3 2 1 0 0 1 0 5 4 16 14 5 25 0 0 0 0 0 0 5 2 9 1 5 1 0 2 3 7 0 0 3 0 0 2 7 2 1 13 1 1 2 5 2 0 0 0 7 0 4 6 4 1 8 1 0 0 0 1 2 3 3 0 0 4 1 8 3 3 2 8 6 2 9 0 5 0 3 0 5 4 8 6 4 1 4 4 4 4 1 1 3 6 9 1 0 3 12 4 7 2 1 10 0 1 1 0 0 3 0 2 1 2 3 2 8 3 14 5 1 3 2 0 0 0 0 2 3 9 4 6 4 3 0 0 0 0 0 0 3 4 4 3 8 5 6 4 3 5 0 3 0 1 5 3 2 8 10 4 7 6 3 5 1 0 0 0 1 0 4 3 2 6 5 5 1 1 10 0 1 1 1 3 8 1 2 1 3 19 7 2 2 4 0 5 2 1 5 1 9 1 2 7 12 2 2 2 1 0 10 7 6 0 0 0 2 1 16 12 0 5 1 1 0 0 1 0 0 1 1 5 0 1 10 1 3 4 6 4 1 3 23 1 3 0 4 9 12 9 15 11 7 0 0 1 0 0 0 0
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