I agree, you have a good point 🙂

Since the rmfield() code calls struct2cell() then cell2struct(), it looks like it’s saying that behind the scene, struct() is basically a high level wrapper around cells using hash keys to map the name to indices: a useful piece of information to keep in mind for performance tuning. Actually, table() or dataset() object deals with cells under the hood, I just wasn’t expecting struct() to be the same given its origins in C.

I found rmfield() so slow that I’ve actually written a keepField() long time ago for the exact same reason as your application scenario: if I need to remove 5000 fields, I might as well keep what I want by adding to an empty (fieldless) struct one-field at a time. i.e.

for k=1:length(fieldsToKeep)
  Y.(fieldsToKeep{k}) = X.(fieldsToKeep{k});
end

It turned out to be much faster too because there are no names to search for. Dynamic field names are done with hash table (checked with TMW, it’s not documented), so it’s on average O(1) time. It boils down to the same O(nlog(n)) time as the setdiff() proposed if you ultimately have to identify the fields to remove instead.

Unfortunately MATLAB has only rmfield(), so I suspect a lot of people might have done a set-op (spent O(nlog(n)) time) to get the list to keep (the complementary set to remove) then run through the O(n) algorithm in rmfield() when they could have done it in average O(1) time by just transferring the wanted fields.

if ~isa(x,'double')
   x=double(x);
end

could often safely be replaced with

x=double(x);

Also, a try-catch sequence in place of conditional tests was often faster.

ML

@Peter – excellent usage example. Thanks for sharing.