Hi all. I have two questions, one very basic and one not so basic. I have a DataFrame that looks like this: df = DataFrame([]) df.list = [[1,2,3,6,12,13,14],[20,21,23,24,50],[1,3,5,7,10,11,12,13]] And I need to generate a new column, that only stores the “points” of each array. This means, only …

The logic is: julia> function identify_pairs(v::AbstractVector) local start, stop initialized = false res = eltype(v)[] for x in v if initialized if x == stop + 1 stop = x else …

Writing a loop is fine. Your logic is sufficiently complicated to merit a loop. But you should put things in functions to organize your code and make it readable julia> function get_consec(x) t = [x[1]] for i in 2:length(x) # Consecutive with last one …

Thanks a lot! I should try to use functions more often, especially in this cases. I understand the logic, cheers!

Thanks!! As I said in the previous comment, I should tap more often on functions. I am marking this one as the solution. Thanks again!

Fyi, this Matlab post provides a compact alternative, probably less efficient than Bogumil’s solution. A possible Julia translation here into (~10% slower for your input data) : function sortedendpoints(v) d = diff(eltype(v)[0; diff(v).==1; 0]) return transpose([v[d.>0] v[d.<0]])[:] end t…

This also works: transform(df, :list => ByRow(f->filter(x->(x-1 ∈ f) ⊻ (x+1 ∈ f),f)) => :new) (slower in run-time, faster in type-time)

That’s really neat! Thanks! It still amazeme that something that in my (newbie) head was a really complex problem can be solved in many ways, and sometimes like this, in one line. Got lots to learn

Just that this one has quadratic time complexity, so if your arrays get longer you might run into problems quickly.

How to reduce redundancy in a list inside a DataFrame

New to Julia

rafael.guerra October 12, 2022, 7:14am 8

@Dan, what a beautiful one-liner, which also shows the intention most clearly. You might consider adding it to this list.

1 Like

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How to reduce redundancy in a list inside a DataFrame

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