Build matrix from corresponding table variables stored in cell array

Hello, I have a number of tables with identical variables, stored as elements in a cell array. For example:

NoOfCells = randi([5 10]);  % generate number of tables
C = cell(NoOfCells,1);     % preallocate

% generate cell array of tables with same variable names
for i = 1:NoOfCells
    A                           = rand(3);
    T                           = array2table(A);
    T.Properties.VariableNames  = {'Col1' 'Col2' 'Col3'};
    C{i,1}                      = T;
end

Wht I would like to now do is build a matrix from Col1 of each table, essentially wihtout using a for loop (I know how to do it with a for loop).

I was thinking somehting like:

D = [C{:}.Col1]

This unfortunatelly produces: Intermediate brace '{}' indexing produced a comma-separated list with 8 values, but it must produce a single value when followed by subsequent indexing operations.

How could it be done?

Regards,

Cristian

Answer 1

[Matt J]

load data

D=reshape( vertcat(C{:}).Col1  , height(C{1}) ,[])

D = 3×9

0.9058 0.1576 0.9595 0.6555 0.3171 0.4456 0.4984 0.8909 0.2435 0.1270 0.9706 0.6557 0.1712 0.9502 0.6463 0.9597 0.9593 0.9293 0.9134 0.9572 0.0357 0.7060 0.0344 0.7094 0.3404 0.5472 0.3500

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[Cristian Berceanu]

I like this one much better, thanks! 9ms execution time is not so bad.

Cristian

Answer 2

[Matt J]

I hope you understand that speed-wise there is no way to iterate over cell arrays that is faster than a for-loop. However, using cellfun can abbreviate the syntax of a for-loop:

load data; whos C

  Name      Size            Bytes  Class    Attributes

  C         9x1             15039  cell               

disp(C{1})

     Col1       Col2       Col3  
    _______    _______    _______

    0.90579    0.63236    0.54688
    0.12699    0.09754    0.95751
    0.91338     0.2785    0.96489

D=cell2mat( cellfun(@(t) t.Col1, C', 'uni',0) )

D = 3×9

0.9058 0.1576 0.9595 0.6555 0.3171 0.4456 0.4984 0.8909 0.2435 0.1270 0.9706 0.6557 0.1712 0.9502 0.6463 0.9597 0.9593 0.9293 0.9134 0.9572 0.0357 0.7060 0.0344 0.7094 0.3404 0.5472 0.3500

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