Loop to replace outliers with NaN extremely slow

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I want to replace outliers with NaN in a large table (> 3 standard deviations from each column's mean) and my code works in principle but is incredibly slow, i.e. still not done after 10 minutes. The table size is about 2000x150. Is there a faster way, maybe without the loop, and could someone tell me what is wrong with my version?
%Version 1: loop through column names
var_list = mytable.Properties.VariableNames(4:140)
for i = 1:length(var_list)
mytable.(var_list{i}) = filloutliers(mytable.(var_list{i}),nan,'mean','ThresholdFactor', 3)
%Version 2: loop through column indices
for i = 4:140
mytable(:,i) = filloutliers(mytable(:,i),nan,'mean','ThresholdFactor', 3)
TL on 21 Jan 2022
Hi Mathieu, yes I do need to replace them with NaN, some values are real errors so they can be 10 times higher than the mean and need to be filtered out

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Accepted Answer

Star Strider
Star Strider on 21 Jan 2022
The way the table addressing is coded is likely the problem.
I’m not certain, however using parentheses () addresses the table (or variables as individual table arrays), while curly braces {} address the variable contents themselves.
So for example
mytable(:,i) =
creates a new table as ‘mytable’ while
mytable{:,i} =
addresses only the contents of the variable.
See the documentations ection on Access Data in Tables for details.
Again, I’m not certain wht the problem is, however experimenting with changing the addressing method could provide a solution.
Also, I’m not certain if the loop is even necessary, since filloutliers appears to work on arrays as well as vectors, and operates on each column separately, according to the documentation.

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