# extracting rows from large table

3 views (last 30 days)
Mark Bodner on 30 Sep 2022
Commented: Walter Roberson on 11 Oct 2022
I have a large table (23000000x11) and want to extract rows from the table based on values in the 5th column.
for example the data looks like:
66172 8:17 1 2 1 1 1 1 1 6 8
66842 7:17 1 3 0 1 1 1 1 1 7
.
.
.
and I want to create a smaller table with all the rows whose value of the fifth column are 0. This is of course easy to do with for loops and if statements, but takes enormous amounts of time to run. Is there a way using maybe ismember, intersect, or some other means to carry this out. I'm sure there must be. Any suggestions are appreciated.

Walter Roberson on 30 Sep 2022
mask = YourTable{:, 5} == 0;
Walter Roberson on 11 Oct 2022
I was curious about the timing.
In my tests, the first 5 or so {:,5} accesses were reliably much slower than the others, so I skip those in the plot.
I must admit that I do not understand why {:,5} would be 5 or 6 times slower than .(5)
format long g
YourTable = array2table(randi(9, 5000, 7), 'VariableNames', {'a', 'b', 'c', 'd', 'e', 'f', 'g'});
N = 50;
skip = 7;
time_brace = zeros(N,1);
time_named = zeros(N,1);
time_dotnum = zeros(N,1);
for K = 1 : N
tic; YourTable.(5); t=toc(); time_dotnum(K) = t;
tic; YourTable{:,5}; t=toc(); time_brace(K) = t;
tic; YourTable.e; t=toc(); time_named(K) = t;
end
subset = [time_brace(skip+1:end), time_named(skip+1:end), time_dotnum(skip+1:end)];
plot(subset);
legend({'\{:,5\}', '.e', '.(5)'})
mean(subset)
ans = 1×3
1.0e+00 * 7.27209302325581e-05 1.4046511627907e-05 1.01627906976744e-05

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