Copy or clone a collection in Julia

I created a one-dimensional array (vector) in Julia, namely a=[1, 2, 3, 4, 5] . Then I want to create a new vector b , where b has exactly the same elements in a , ie b=[1, 2, 3, 4, 5] .

It seems that directly using b = a just create a pointer for the original collection, which means that if I change b and a mutable, the modification will also be reflected in a . For example, if I use !pop(b) , then b=[1, 2, 3, 4] and a=[1, 2, 3, 4] .

I am wondering if there is an official function to simply copy or clone a collection whose change in b will not happen in a . I believe the solution uses b = collect(a) . I would appreciate if someone would provide some other approaches.

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3 answers

b=copy(a)

Gotta do what you want.

methods(copy) will provide you with a list of methods for copy that tells you which types a will work.

 julia> methods(copy) # 32 methods for generic function "copy": copy(r::Range{T}) at range.jl:324 copy(e::Expr) at expr.jl:34 copy(s::SymbolNode) at expr.jl:38 copy(x::Union{AbstractString,DataType,Function,LambdaStaticData,Number,QuoteNode,Symbol,TopNode,Tuple,Union}) at operators.jl:194 copy(V::SubArray{T,N,P<:AbstractArray{T,N},I<:Tuple{Vararg{Union{AbstractArray{T,1},Colon,Int64}}},LD}) at subarray.jl:29 copy(a::Array{T,N}) at array.jl:100 copy(M::SymTridiagonal{T}) at linalg/tridiag.jl:63 copy(M::Tridiagonal{T}) at linalg/tridiag.jl:320 copy{T,S}(A::LowerTriangular{T,S}) at linalg/triangular.jl:36 copy{T,S}(A::Base.LinAlg.UnitLowerTriangular{T,S}) at linalg/triangular.jl:36 copy{T,S}(A::UpperTriangular{T,S}) at linalg/triangular.jl:36 copy{T,S}(A::Base.LinAlg.UnitUpperTriangular{T,S}) at linalg/triangular.jl:36 copy{T,S}(A::Symmetric{T,S}) at linalg/symmetric.jl:38 copy{T,S}(A::Hermitian{T,S}) at linalg/symmetric.jl:39 copy(M::Bidiagonal{T}) at linalg/bidiag.jl:113 copy(S::SparseMatrixCSC{Tv,Ti<:Integer}) at sparse/sparsematrix.jl:184 copy{Tv<:Float64}(A::Base.SparseMatrix.CHOLMOD.Sparse{Tv<:Float64}, stype::Integer, mode::Integer) at sparse/cholmod.jl:583 copy(A::Base.SparseMatrix.CHOLMOD.Dense{T<:Union{Complex{Float64},Float64}}) at sparse/cholmod.jl:1068 copy(A::Base.SparseMatrix.CHOLMOD.Sparse{Tv<:Union{Complex{Float64},Float64}}) at sparse/cholmod.jl:1069 copy(a::AbstractArray{T,N}) at abstractarray.jl:349 copy(s::IntSet) at intset.jl:34 copy(o::ObjectIdDict) at dict.jl:358 copy(d::Dict{K,V}) at dict.jl:414 copy(a::Associative{K,V}) at dict.jl:204 copy(s::Set{T}) at set.jl:35 copy(b::Base.AbstractIOBuffer{T<:AbstractArray{UInt8,1}}) at iobuffer.jl:38 copy(r::Regex) at regex.jl:65 copy(::Base.DevNullStream) at process.jl:98 copy(C::Base.LinAlg.Cholesky{T,S<:AbstractArray{T,2}}) at linalg/cholesky.jl:160 copy(C::Base.LinAlg.CholeskyPivoted{T,S<:AbstractArray{T,2}}) at linalg/cholesky.jl:161 copy(J::UniformScaling{T<:Number}) at linalg/uniformscaling.jl:17 copy(A::Base.SparseMatrix.CHOLMOD.Factor{Tv}) at sparse/cholmod.jl:1070 
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You can use copy and deepcopy :

 help?> copy search: copy copy! copysign deepcopy unsafe_copy! cospi complex Complex complex64 complex32 complex128 complement copy(x) Create a shallow copy of x: the outer structure is copied, but not all internal values. For example, copying an array produces a new array with identically-same elements as the original. help?> deepcopy search: deepcopy deepcopy(x) Create a deep copy of x: everything is copied recursively, resulting in a fully independent object. For example, deep-copying an array produces a new array whose elements are deep copies of the original elements. Calling deepcopy on an object should generally have the same effect as serializing and then deserializing it. As a special case, functions can only be actually deep-copied if they are anonymous, otherwise they are just copied. The difference is only relevant in the case of closures, ie functions which may contain hidden internal references. While it isn't normally necessary, user-defined types can override the default deepcopy behavior by defining a specialized version of the function deepcopy_internal(x::T, dict::ObjectIdDict) (which shouldn't otherwise be used), where T is the type to be specialized for, and dict keeps track of objects copied so far within the recursion. Within the definition, deepcopy_internal should be used in place of deepcopy, and the dict variable should be updated as appropriate before returning. 

Like this:

 julia> a = Any[1, 2, 3, [4, 5, 6]] 4-element Array{Any,1}: 1 2 3 [4,5,6] julia> b = copy(a); c = deepcopy(a); julia> a[4][1] = 42; julia> b # copied 4-element Array{Any,1}: 1 2 3 [42,5,6] julia> c # deep copied 4-element Array{Any,1}: 1 2 3 [4,5,6] 

Please note that the help system indicates the presence of other functions related to copying.

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@SalchiPapa

Sorry, I just have an additional question, but I don’t have enough points to leave a comment. You gave a good example. However, if instead of a[4][1] = 42; I do a[3] = 90 , then I get

 julia> a[3] = 90 90 julia> a 4-element Array{Any,1}: 1 2 90 [42, 5, 6] julia> b 4-element Array{Any,1}: 1 2 3 [42, 5, 6] 

So why is this modification not copied to b ?

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Source: https://habr.com/ru/post/1241934/


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