how to read a table and multiply its elements with eachother and store it in another table

3 Ansichten (letzte 30 Tage)
abhijeet joseph
abhijeet joseph am 6 Apr. 2022
Bearbeitet: Stephen23 am 6 Apr. 2022
i have a table of dimension 152*3, I want to multiply all the elments in row 1 ie: row (1,1)*(1,2)*(1,3) and store its value in another table.
this should be done for each rows untill the last and each values should be stored and viewed in another table

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Antworten (2)

Davide Masiello
Davide Masiello am 6 Apr. 2022
Ran in:
Example
%% Creating an example table
T = array2table(rand(152,3));
T.Properties.VariableNames = {'A','B','C'};
disp(T)
A B C ________ _________ _________ 0.42378 0.13393 0.22799 0.082036 0.41103 0.47176 0.75376 0.92698 0.35121 0.76344 0.59448 0.15683 0.6067 0.82957 0.65986 0.13342 0.45348 0.40703 0.6811 0.064675 0.4763 0.39749 0.42151 0.49343 0.25655 0.85027 0.75015 0.2818 0.17302 0.22054 0.43358 0.30905 0.094299 0.067233 0.42883 0.31584 0.59127 0.47814 0.64779 0.21798 0.21966 0.81706 0.24648 0.16125 0.71757 0.47834 0.8578 0.90381 0.33964 0.32478 0.6592 0.58716 0.72589 0.63555 0.2079 0.94555 0.11394 0.23356 0.47505 0.62317 0.38324 0.63074 0.96923 0.029543 0.50752 0.66974 0.47596 0.68114 0.10123 0.51391 0.64901 0.5895 0.62073 0.12588 0.42946 0.14064 0.8107 0.55923 0.55861 0.20972 0.026246 0.88244 0.85151 0.67231 0.24386 0.0091537 0.81313 0.24565 0.40811 0.17537 0.82064 0.70851 0.028089 0.13386 0.50029 0.7247 0.023885 0.67824 0.11584 0.049607 0.67578 0.90859 0.67972 0.53723 0.87945 0.70288 0.24487 0.54332 0.10835 0.11454 0.03875 0.586 0.8897 0.48103 0.19033 0.28499 0.43309 0.16974 0.84623 0.8983 0.25022 0.38247 0.49594 0.14369 0.81799 0.49193 0.017027 0.25476 0.9341 0.69411 0.73299 0.91914 0.63633 0.47565 0.94537 0.26791 0.21803 0.67819 0.59145 0.45666 0.23451 0.89969 0.23141 0.91394 0.30595 0.79774 0.54422 0.82913 0.20912 0.3579 0.56273 0.080806 0.60201 0.56198 0.2252 0.013156 0.98202 0.21236 0.67949 0.30891 0.78289 0.93988 0.92229 0.92271 0.84322 0.96672 0.47785 0.45762 0.84022 0.10221 0.68097 0.030949 0.76589 0.77341 0.85085 0.49493 0.91834 0.67863 0.26774 0.45319 0.91824 0.38266 0.58674 0.11981 0.34745 0.3926 0.45039 0.2838 0.065324 0.77591 0.67981 0.37745 0.96183 0.76159 0.98151 0.093357 0.079286 0.80662 0.010607 0.79386 0.62656 0.85305 0.74432 0.57746 0.28166 0.29303 0.31683 0.06148 0.55658 0.52646 0.20803 0.39411 0.26235 0.37679 0.16947 0.16664 0.40762 0.69195 0.94204 0.86755 0.62125 0.15093 0.6197 0.84974 0.67479 0.15956 0.1419 0.069695 0.94574 0.14687 0.3861 0.010654 0.41531 0.95663 0.090716 0.01221 0.38156 0.38419 0.40451 0.96042 0.034027 0.36074 0.36304 0.7896 0.31645 0.44171 0.95109 0.036508 0.011216 0.68574 0.89948 0.24477 0.81051 0.44184 0.06977 0.21326 0.98712 0.89869 0.47508 0.66351 0.025417 0.50793 0.35838 0.21829 0.63055 0.80763 0.81413 0.91354 0.96246 0.28164 0.81592 0.48461 0.45038 0.72469 0.94251 0.36319 0.89172 0.096547 0.72459 0.46539 0.33833 0.85381 0.25951 0.21369 0.10845 0.74297 0.96254 0.48982 0.12529 0.88964 0.55652 0.45328 0.59957 0.49526 0.25019 0.37341 0.85707 0.80745 0.56399 0.17451 0.57691 0.72659 0.40315 0.68526 0.70135 0.69743 0.71913 0.94173 0.8324 0.57787 0.26485 0.12257 0.11568 0.28255 0.86964 0.50212 0.37506 0.0041642 0.67394 0.2847 0.67101 0.81317 0.12404 0.33385 0.5364 0.74537 0.60136 0.11061 0.72833 0.46459 0.10321 0.43555 0.90648 0.93456 0.82242 0.51391 0.65652 0.030484 0.69796 0.80008 0.633 0.13769 0.027733 0.32003 0.96669 0.48346 0.80731 0.47872 0.42179 0.12564 0.92077 0.69997 0.43716 0.06826 0.62567 0.78622 0.58397 0.55886 0.9579 0.91042 0.23519 0.15555 0.014037 0.97677 0.57281 0.83796 0.84611 0.68231 0.99345 0.2854 0.67092 0.68178 0.28414 0.15534 0.52458 0.67874 0.4807 0.87333 0.47587 0.044565 0.7615 0.15419 0.46918 0.54308 0.49933 0.91122 0.31492 0.44477 0.34637 0.26137 0.19595 0.69232 0.92582 0.58582 0.12117 0.39024 0.71311 0.65721 0.85462 0.09565 0.61922 0.51532 0.96703 0.22528 0.19192 0.21275 0.029376 0.2551 0.031148 0.75886 0.52969 0.82803 0.65453 0.21013 0.70479 0.11133 0.89817 0.029264 0.18215 0.0044201 0.81203 0.059566 0.23815 0.52536 0.81921 0.47153 0.73952 0.93181 0.079821 0.52958 0.96271 0.5986 0.95124 0.86234 0.71223 0.81267 0.57192 0.77478 0.17716 0.61609 0.028296 0.040548 0.41582 0.72524 0.24942 0.58864 0.59282 0.44106 0.32027 0.59054 0.44587 0.019645 0.87156 0.14007 0.56612 0.91016
%% New table with product
T_prod = array2table(prod(T{:,:},2));
T_prod.Properties.VariableNames = {'product'};
disp(T_prod)
product __________ 0.01294 0.015907 0.2454 0.071177 0.33211 0.024626 0.020981 0.082671 0.16364 0.010752 0.012636 0.009106 0.18314 0.039121 0.02852 0.37086 0.072715 0.27088 0.022398 0.069143 0.23429 0.010042 0.032819 0.19662 0.033556 0.063763 0.0030748 0.50518 0.0018151 0.017581 0.016332 0.048534 0.0018765 0.030459 0.32114 0.093513 0.0004809 0.25079 0.023492 0.12903 0.047463 0.057822 0.0040519 0.46764 0.28614 0.039614 0.06334 0.19028 0.13283 0.062056 0.027375 0.001665 0.1417 0.2273 0.71759 0.2114 0.058478 0.018333 0.38672 0.082344 0.20617 0.016344 0.0083496 0.1991 0.71898 0.0059705 0.0052759 0.36666 0.026149 0.018014 0.021509 0.01064 0.26571 0.081347 0.35534 0.0015781 0.053631 0.0042328 0.00042264 0.14926 0.0044563 0.11037 0.00038943 0.15097 0.024986 0.18919 0.008012 0.039736 0.4146 0.24763 0.17808 0.24807 0.062382 0.13444 0.0060139 0.35029 0.062029 0.1346 0.080069 0.079471 0.16899 0.33519 0.56372 0.018759 0.028423 0.00078423 0.12875 0.033675 0.24044 0.037429 0.04075 0.39499 0.013969 0.069735 0.0085799 0.18685 0.048794 0.020887 0.28726 0.48737 0.00051353 0.46884 0.57352 0.13055 0.023154 0.28494 0.016149 0.039289 0.14329 0.040264 0.1256 0.027701 0.40053 0.030522 0.041811 0.0015943 0.01252 0.11389 0.070475 2.3562e-05 0.011519 0.20294 0.055004 0.30519 0.58424 0.36011 0.0030884 0.012228 0.087039 0.083421 0.0076343 0.072172
Stephen23
Stephen23 am 6 Apr. 2022
Bearbeitet: Stephen23 am 6 Apr. 2022
Ran in:
If all you need are numeric operations across all columns then storing the data in one numeric matrix would make your task much easier and more efficient. But if you must use a table:
T = array2table(rand(152,3), 'VariableNames',{'A','B','C'})
T = 152×3 table
A B C ________ _________ ________ 0.54535 0.98312 0.43502 0.58908 0.0013448 0.016707 0.21073 0.047291 0.45771 0.88526 0.26006 0.45427 0.23476 0.82321 0.67366 0.85761 0.96404 0.5221 0.41686 0.18252 0.96219 0.334 0.84439 0.44205 0.4472 0.78245 0.59439 0.034742 0.69014 0.75689 0.56919 0.90472 0.40937 0.97928 0.78709 0.84837 0.63946 0.40587 0.32266 0.96012 0.37461 0.27548 0.86979 0.64672 0.70659 0.73997 0.85677 0.45199
F = @(varargin)prod([varargin{:}]);
Z = rowfun(F,T)
Z = 152×1 table
Var1 __________ 0.23323 1.3235e-05 0.0045614 0.10458 0.13019 0.43166 0.073207 0.12467 0.20798 0.018148 0.21081 0.65391 0.083741 0.099082 0.39747 0.28656

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