Copy or clone a collection in Julia

Question

I have 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

Answer 1

You can use the copy and deepcopy functions:

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, i.e. 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]

Notice that the help system hints of the existence of other copy related functions.

Answer 2

b=copy(a)

Should do what you want.

methods(copy) will give you a list of methods for copy, which will tell you what types of a this will work for.

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