diff --git a/NEWS.md b/NEWS.md
index d09f779206d899..0a80847360a6c2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -872,6 +872,8 @@ Deprecated or removed
   * The functions `eigs` and `svds` have been moved to the `IterativeEigensolvers` standard
     library module ([#24714]).
 
+  * Sparse array functionality has moved to the `SparseArrays` standard library ([#25249]).
+
   * `@printf` and `@sprintf` have been moved to the `Printf` standard library ([#23929],[#25056]).
 
   * `isnumber` has been deprecated in favor of `isnumeric`, `is_assigned_char`
diff --git a/base/asyncmap.jl b/base/asyncmap.jl
index 9fc681d4ca71eb..6b66799f06dbbc 100644
--- a/base/asyncmap.jl
+++ b/base/asyncmap.jl
@@ -260,13 +260,6 @@ function asyncmap(f, b::BitArray; kwargs...)
     return b2
 end
 
-# TODO: Optimize for sparse arrays
-# For now process as regular arrays and convert back
-function asyncmap(f, s::AbstractSparseArray...; kwargs...)
-    sa = map(Array, s)
-    return sparse(asyncmap(f, sa...; kwargs...))
-end
-
 mutable struct AsyncCollector
     f
     results
diff --git a/base/deprecated.jl b/base/deprecated.jl
index 3b828b81ecaada..94ee6b38481315 100644
--- a/base/deprecated.jl
+++ b/base/deprecated.jl
@@ -469,8 +469,6 @@ import .LinAlg: lufact, lufact!, qrfact, qrfact!, cholfact, cholfact!
 @deprecate cholfact!(A::AbstractMatrix, ::Type{Val{false}}) cholfact!(A, Val(false))
 @deprecate cholfact!(A::AbstractMatrix, ::Type{Val{true}}; tol = 0.0) cholfact!(A, Val(true); tol = tol)
 @deprecate cat(::Type{Val{N}}, A::AbstractArray...) where {N} cat(Val(N), A...)
-@deprecate cat(::Type{Val{N}}, A::SparseArrays._SparseConcatGroup...) where {N} cat(Val(N), A...)
-@deprecate cat(::Type{Val{N}}, A::SparseArrays._DenseConcatGroup...) where {N} cat(Val(N), A...)
 @deprecate cat_t(::Type{Val{N}}, ::Type{T}, A, B) where {N,T} cat_t(Val(N), T, A, B) false
 @deprecate reshape(A::AbstractArray, ::Type{Val{N}}) where {N} reshape(A, Val(N))
 
@@ -505,6 +503,7 @@ function OverflowError()
 end
 
 # PR #22703
+import .LinAlg: Bidiagonal
 @deprecate Bidiagonal(dv::AbstractVector, ev::AbstractVector, isupper::Bool) Bidiagonal(dv, ev, ifelse(isupper, :U, :L))
 @deprecate Bidiagonal(dv::AbstractVector, ev::AbstractVector, uplo::Char) Bidiagonal(dv, ev, ifelse(uplo == 'U', :U, :L))
 @deprecate Bidiagonal(A::AbstractMatrix, isupper::Bool) Bidiagonal(A, ifelse(isupper, :U, :L))
@@ -522,25 +521,6 @@ end
 #     remove parse-with-chains-warn and bitshift-warn
 # update precedence table in doc/src/manual/mathematical-operations.md
 
-# deprecate remaining vectorized methods over SparseVectors (zero-preserving)
-for op in (:floor, :ceil, :trunc, :round,
-        :log1p, :expm1,  :sinpi,
-        :sin,   :tan,    :sind,   :tand,
-        :asin,  :atan,   :asind,  :atand,
-        :sinh,  :tanh,   :asinh,  :atanh)
-    @eval import .Math: $op
-    @eval @deprecate ($op)(x::AbstractSparseVector{<:Number,<:Integer}) ($op).(x)
-end
-# deprecate remaining vectorized methods over SparseVectors (not-zero-preserving)
-for op in (:exp, :exp2, :exp10, :log, :log2, :log10,
-           :cos, :cosd, :acos, :cosh, :cospi,
-           :csc, :cscd, :acot, :csch, :acsch,
-           :cot, :cotd, :acosd, :coth,
-           :sec, :secd, :acotd, :sech, :asech)
-    @eval import .Math: $op
-    @eval @deprecate ($op)(x::AbstractSparseVector{<:Number,<:Integer}) ($op).(x)
-end
-
 # PR #22182
 @deprecate is_apple   Sys.isapple
 @deprecate is_bsd     Sys.isbsd
@@ -573,6 +553,7 @@ end
 
 # PR #22925
 # also uncomment constructor tests in test/linalg/bidiag.jl
+import .LinAlg: Bidiagonal
 function Bidiagonal(dv::AbstractVector{T}, ev::AbstractVector{S}, uplo::Symbol) where {T,S}
     depwarn(string("`Bidiagonal(dv::AbstractVector{T}, ev::AbstractVector{S}, uplo::Symbol) where {T, S}`",
         " is deprecated, manually convert both vectors to the same type instead."), :Bidiagonal)
@@ -582,6 +563,7 @@ end
 
 # PR #23035
 # also uncomment constructor tests in test/linalg/tridiag.jl
+import .LinAlg: SymTridiagonal
 function SymTridiagonal(dv::AbstractVector{T}, ev::AbstractVector{S}) where {T,S}
     depwarn(string("`SymTridiagonal(dv::AbstractVector{T}, ev::AbstractVector{S}) ",
         "where {T, S}` is deprecated, convert both vectors to the same type instead."), :SymTridiagonal)
@@ -591,6 +573,7 @@ end
 
 # PR #23154
 # also uncomment constructor tests in test/linalg/tridiag.jl
+import .LinAlg: Tridiagonal
 function Tridiagonal(dl::AbstractVector{Tl}, d::AbstractVector{Td}, du::AbstractVector{Tu}) where {Tl,Td,Tu}
     depwarn(string("`Tridiagonal(dl::AbstractVector{Tl}, d::AbstractVector{Td}, du::AbstractVector{Tu}) ",
         "where {Tl, Td, Tu}` is deprecated, convert all vectors to the same type instead."), :Tridiagonal)
@@ -710,11 +693,8 @@ end
 # PR #23066
 @deprecate cfunction(f, r, a::Tuple) cfunction(f, r, Tuple{a...})
 
-# PR 23341
-import .LinAlg: diagm
-@deprecate diagm(A::SparseMatrixCSC) sparse(Diagonal(sparsevec(A)))
-
 # PR #23373
+import .LinAlg: diagm
 @deprecate diagm(A::BitMatrix) BitMatrix(Diagonal(vec(A)))
 
 # PR 23341
@@ -828,33 +808,6 @@ end
 
 @deprecate contains(eq::Function, itr, x) any(y->eq(y,x), itr)
 
-# PR #23757
-import .SparseArrays.spdiagm
-@deprecate spdiagm(x::AbstractVector) sparse(Diagonal(x))
-function spdiagm(x::AbstractVector, d::Number)
-    depwarn(string("`spdiagm(x::AbstractVector, d::Number)` is deprecated, use ",
-        "`spdiagm(d => x)` instead, which now returns a square matrix. To preserve the old ",
-        "behaviour, use `sparse(SparseArrays.spdiagm_internal(d => x)...)`"), :spdiagm)
-    I, J, V = SparseArrays.spdiagm_internal(d => x)
-    return sparse(I, J, V)
-end
-function spdiagm(x, d)
-    depwarn(string("`spdiagm((x1, x2, ...), (d1, d2, ...))` is deprecated, use ",
-        "`spdiagm(d1 => x1, d2 => x2, ...)` instead, which now returns a square matrix. ",
-        "To preserve the old behaviour, use ",
-        "`sparse(SparseArrays.spdiagm_internal(d1 => x1, d2 => x2, ...)...)`"), :spdiagm)
-    I, J, V = SparseArrays.spdiagm_internal((d[i] => x[i] for i in 1:length(x))...)
-    return sparse(I, J, V)
-end
-function spdiagm(x, d, m::Integer, n::Integer)
-    depwarn(string("`spdiagm((x1, x2, ...), (d1, d2, ...), m, n)` is deprecated, use ",
-        "`spdiagm(d1 => x1, d2 => x2, ...)` instead, which now returns a square matrix. ",
-        "To specify a non-square matrix and preserve the old behaviour, use ",
-        "`I, J, V = SparseArrays.spdiagm_internal(d1 => x1, d2 => x2, ...); sparse(I, J, V, m, n)`"), :spdiagm)
-    I, J, V = SparseArrays.spdiagm_internal((d[i] => x[i] for i in 1:length(x))...)
-    return sparse(I, J, V, m, n)
-end
-
 # deprecate zeros(D::Diagonal[, opts...])
 function zeros(D::Diagonal)
     depwarn(string("`zeros(D::Diagonal)` is deprecated, use ",
@@ -987,7 +940,6 @@ function eye(::Type{Diagonal{T}}, n::Int) where T
     return Diagonal{T}(I, n)
 end
 @eval Base.LinAlg import Base.eye
-# @eval Base.SparseArrays import Base.eye # SparseArrays has an eye for things cholmod
 
 
 export tic, toq, toc
@@ -1021,8 +973,6 @@ function toc()
     return t
 end
 
-@eval Base.SparseArrays @deprecate sparse(s::UniformScaling, m::Integer) sparse(s, m, m)
-
 # A[I...] .= with scalar indices should modify the element at A[I...]
 function Broadcast.dotview(A::AbstractArray, args::Number...)
     depwarn("the behavior of `A[I...] .= X` with scalar indices will change in the future. Use `A[I...] = X` instead.", :broadcast!)
@@ -1100,12 +1050,6 @@ end
 # PR #25030
 @eval LinAlg @deprecate fillslots! fillstored! false
 
-# PR #25037
-@eval SparseArrays @deprecate spones(A::SparseMatrixCSC) LinAlg.fillstored!(copy(A), 1)
-@eval SparseArrays @deprecate spones(A::SparseVector) LinAlg.fillstored!(copy(A), 1)
-using .SparseArrays.spones
-export spones
-
 function diagm(v::BitVector)
     depwarn(string("`diagm(v::BitVector)` is deprecated, use `diagm(0 => v)` or ",
         "`BitMatrix(Diagonal(v))` instead."), :diagm)
@@ -1154,19 +1098,6 @@ function full(A::Union{Diagonal,Bidiagonal,Tridiagonal,SymTridiagonal})
     return Matrix(A)
 end
 
-# full for sparse arrays
-function full(S::Union{SparseVector,SparseMatrixCSC})
-    (arrtypestr, desttypestr) =
-        isa(S, SparseVector)    ? ("SparseVector",    "Vector") :
-        isa(S, SparseMatrixCSC) ? ("SparseMatrixCSC", "Matrix") :
-            error("should not be reachable!")
-    depwarn(string(
-        "`full(S::$(arrtypestr))` (and `full` in general) has been deprecated. ",
-        "To replace `full(S::$(arrtypestr))`, consider `$(desttypestr)(S)` or, ",
-        "if that option is too narrow, `Array(S)`."), :full)
-    return Array(S)
-end
-
 # full for factorizations
 function full(F::Union{LinAlg.LU,LinAlg.LQ,LinAlg.QR,LinAlg.QRPivoted,LinAlg.QRCompactWY,
                         LinAlg.SVD,LinAlg.LDLt,LinAlg.Schur,LinAlg.Eigen,LinAlg.Hessenberg,
@@ -1279,7 +1210,6 @@ end
 
 # issue #22849
 @deprecate reinterpret(::Type{T}, a::Array{S}, dims::NTuple{N,Int}) where {T, S, N} reshape(reinterpret(T, vec(a)), dims)
-@deprecate reinterpret(::Type{T}, a::SparseMatrixCSC{S}, dims::NTuple{N,Int}) where {T, S, N} reinterpret(T, reshape(a, dims))
 @deprecate reinterpret(::Type{T}, a::ReinterpretArray{S}, dims::NTuple{N,Int}) where {T, S, N} reshape(reinterpret(T, vec(a)), dims)
 
 # issue #24006
@@ -1316,55 +1246,6 @@ end
 # deprecate bits to bitstring (#24263, #24281)
 @deprecate bits bitstring
 
-# deprecate speye
-export speye
-function speye(n::Integer)
-    depwarn(string("`speye(n::Integer)` has been deprecated in favor of `I`, `sparse`, and ",
-                    "`SparseMatrixCSC` constructor methods. For a direct replacement, consider ",
-                    "`sparse(1.0I, n, n)`, `SparseMatrixCSC(1.0I, n, n)`, or `SparseMatrixCSC{Float64}(I, n, n)`. ",
-                    "If `Float64` element type is not necessary, consider the shorter `sparse(I, n, n)` ",
-                    "or `SparseMatrixCSC(I, n, n)` (with default `eltype(I)` of `Bool`)."), :speye)
-    return sparse(1.0I, n, n)
-end
-function speye(m::Integer, n::Integer)
-    depwarn(string("`speye(m::Integer, n::Integer)` has been deprecated in favor of `I`, ",
-                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
-                    "replacement, consider `sparse(1.0I, m, n)`, `SparseMatrixCSC(1.0I, m, n)`, ",
-                    "or `SparseMatrixCSC{Float64}(I, m, n)`. If `Float64` element type is not ",
-                    " necessary, consider the shorter `sparse(I, m, n)` or `SparseMatrixCSC(I, m, n)` ",
-                    "(with default `eltype(I)` of `Bool`)."), :speye)
-    return sparse(1.0I, m, n)
-end
-function speye(::Type{T}, n::Integer) where T
-    depwarn(string("`speye(T, n::Integer)` has been deprecated in favor of `I`, `sparse`, and ",
-                    "`SparseMatrixCSC` constructor methods. For a direct replacement, consider ",
-                    "`sparse(T(1)I, n, n)` if `T` is concrete or `SparseMatrixCSC{T}(I, n, n)` ",
-                    "if `T` is either concrete or abstract. If element type `T` is not necessary, ",
-                    "consider the shorter `sparse(I, n, n)` or `SparseMatrixCSC(I, n, n)` ",
-                    "(with default `eltype(I)` of `Bool`)."), :speye)
-    return SparseMatrixCSC{T}(I, n, n)
-end
-function speye(::Type{T}, m::Integer, n::Integer) where T
-    depwarn(string("`speye(T, m::Integer, n::Integer)` has been deprecated in favor of `I`, ",
-                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
-                    "replacement, consider `sparse(T(1)I, m, n)` if `T` is concrete or ",
-                    "`SparseMatrixCSC{T}(I, m, n)` if `T` is either concrete or abstract. ",
-                    "If element type `T` is not necessary, consider the shorter ",
-                    "`sparse(I, m, n)` or `SparseMatrixCSC(I, m, n)` (with default `eltype(I)` ",
-                    "of `Bool`)."), :speye)
-    return SparseMatrixCSC{T}(I, m, n)
-end
-function speye(S::SparseMatrixCSC{T}) where T
-    depwarn(string("`speye(S::SparseMatrixCSC{T})` has been deprecated in favor of `I`, ",
-                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
-                    "replacement, consider `sparse(T(1)I, size(S)...)` if `T` is concrete or ",
-                    "`SparseMatrixCSC{eltype(S)}(I, size(S))` if `T` is either concrete or abstract. ",
-                    "If preserving element type `T` is not necessary, consider the shorter ",
-                    "`sparse(I, size(S)...)` or `SparseMatrixCSC(I, size(S))` (with default ",
-                    "`eltype(I)` of `Bool`)."), :speye)
-    return SparseMatrixCSC{T}(I, m, n)
-end
-
 # issue #24167
 @deprecate EnvHash EnvDict
 
@@ -2338,87 +2219,6 @@ end
     @deprecate A_mul_Bc(A::AbstractVecOrMat{T}, R::AbstractRotation{S}) where {T,S}     (*)(A, adjoint(R))
 end
 
-# former imports into SparseArrays
-@eval Base.SparseArrays begin
-    import Base: A_mul_B!, Ac_mul_B, Ac_mul_B!, At_mul_B, At_mul_B!
-    import Base: A_mul_Bc, A_mul_Bt, Ac_mul_Bc, At_mul_Bt
-    import Base: At_ldiv_B, Ac_ldiv_B, A_ldiv_B!
-    import Base.LinAlg: At_ldiv_B!, Ac_ldiv_B!, A_rdiv_B!, A_rdiv_Bc!, mul!, ldiv!, rdiv!
-end
-
-# A[ct]_(mul|ldiv|rdiv)_B[ct][!] methods from base/sparse/linalg.jl, to deprecate
-@eval Base.SparseArrays begin
-    using Base.LinAlg: Adjoint, Transpose
-    @deprecate Ac_ldiv_B(A::SparseMatrixCSC, B::RowVector)  (\)(adjoint(A), B)
-    @deprecate At_ldiv_B(A::SparseMatrixCSC, B::RowVector)  (\)(transpose(A), B)
-    @deprecate Ac_ldiv_B(A::SparseMatrixCSC, B::AbstractVecOrMat)   (\)(adjoint(A), B)
-    @deprecate At_ldiv_B(A::SparseMatrixCSC, B::AbstractVecOrMat)   (\)(transpose(A), B)
-    @deprecate A_rdiv_Bc!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}  rdiv!(A, adjoint(D))
-    @deprecate A_rdiv_Bt!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}  rdiv!(A, transpose(D))
-    @deprecate A_rdiv_B!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}   rdiv!(A, D)
-    @deprecate A_ldiv_B!(L::LowerTriangular{T,<:SparseMatrixCSCUnion{T}}, B::StridedVecOrMat) where {T}     ldiv!(L, B)
-    @deprecate A_ldiv_B!(U::UpperTriangular{T,<:SparseMatrixCSCUnion{T}}, B::StridedVecOrMat) where {T}     ldiv!(U, B)
-    @deprecate A_mul_Bt(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(A, transpose(B))
-    @deprecate A_mul_Bc(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(A, adjoint(B))
-    @deprecate At_mul_B(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(transpose(A), B)
-    @deprecate Ac_mul_B(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(adjoint(A), B)
-    @deprecate At_mul_Bt(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}    (*)(transpose(A), transpose(B))
-    @deprecate Ac_mul_Bc(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}    (*)(adjoint(A), adjoint(B))
-    @deprecate A_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)     mul!(C, A, B)
-    @deprecate Ac_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)    mul!(C, adjoint(A), B)
-    @deprecate At_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)    mul!(C, transpose(A), B)
-    @deprecate A_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)   mul!(α, A, B, β, C)
-    @deprecate A_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}  (*)(A, x)
-    @deprecate A_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}  (*)(A, B)
-    @deprecate Ac_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)  mul!(α, adjoint(A), B, β, C)
-    @deprecate Ac_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}     (*)(adjoint(A), x)
-    @deprecate Ac_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}     (*)(adjoint(A), B)
-    @deprecate At_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)  mul!(α, transpose(A), B, β, C)
-    @deprecate At_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}     (*)(transpose(A), x)
-    @deprecate At_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}     (*)(transpose(A), B)
-    @deprecate A_mul_Bt(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(A, transpose(B))
-    @deprecate A_mul_Bc(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(A, adjoint(B))
-    @deprecate At_mul_B(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(transpose(A), B)
-    @deprecate Ac_mul_B(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(adjoint(A),B)
-    @deprecate At_mul_Bt(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}  (*)(transpose(A), transpose(B))
-    @deprecate Ac_mul_Bc(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}  (*)(adjoint(A), adjoint(B))
-end
-
-# A[ct]_(mul|ldiv|rdiv)_B[ct][!] methods from base/sparse/sparsevector.jl, to deprecate
-for isunittri in (true, false), islowertri in (true, false)
-    unitstr = isunittri ? "Unit" : ""
-    halfstr = islowertri ? "Lower" : "Upper"
-    tritype = :(Base.LinAlg.$(Symbol(unitstr, halfstr, "Triangular")))
-    @eval Base.SparseArrays begin
-        using Base.LinAlg: Adjoint, Transpose
-        @deprecate At_ldiv_B(A::$tritype{TA,<:AbstractMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}   (\)(transpose(A), b)
-        @deprecate At_ldiv_B(A::$tritype{TA,<:StridedMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}    (\)(transpose(A), b)
-        @deprecate At_ldiv_B(A::$tritype, b::SparseVector)  (\)(transpose(A), b)
-        @deprecate Ac_ldiv_B(A::$tritype{TA,<:AbstractMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}   (\)(adjoint(A), b)
-        @deprecate Ac_ldiv_B(A::$tritype{TA,<:StridedMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}    (\)(adjoint(A), b)
-        @deprecate Ac_ldiv_B(A::$tritype, b::SparseVector)  (\)(adjoint(A), b)
-        @deprecate A_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)   ldiv!(A, b)
-        @deprecate At_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)  ldiv!(transpose(A), b)
-        @deprecate Ac_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)  ldiv!(adjoint(A), b)
-    end
-end
-@eval Base.SparseArrays begin
-    using Base.LinAlg: Adjoint, Transpose
-    @deprecate Ac_mul_B(A::SparseMatrixCSC, x::AbstractSparseVector)    (*)(adjoint(A), x)
-    @deprecate At_mul_B(A::SparseMatrixCSC, x::AbstractSparseVector)    (*)(transpose(A), x)
-    @deprecate Ac_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)   mul!(α, adjoint(A), x, β, y)
-    @deprecate Ac_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}   mul!(y, adjoint(A), x)
-    @deprecate At_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)   mul!(α, transpose(A), x, β, y)
-    @deprecate At_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}   mul!(y, transpose(A), x)
-    @deprecate A_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)    mul!(α, A, x, β, y)
-    @deprecate A_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}    mul!(y, A, x)
-    @deprecate At_mul_B!(α::Number, A::StridedMatrix, x::AbstractSparseVector, β::Number, y::StridedVector)     mul!(α, transpose(A), x, β, y)
-    @deprecate At_mul_B!(y::StridedVector{Ty}, A::StridedMatrix, x::AbstractSparseVector{Tx}) where {Tx,Ty}     mul!(y, transpose(A), x)
-    @deprecate At_mul_B(A::StridedMatrix{Ta}, x::AbstractSparseVector{Tx}) where {Ta,Tx}    (*)(transpose(A), x)
-    @deprecate A_mul_B!(α::Number, A::StridedMatrix, x::AbstractSparseVector, β::Number, y::StridedVector)  mul!(α, A, x, β, y)
-    @deprecate A_mul_B!(y::StridedVector{Ty}, A::StridedMatrix, x::AbstractSparseVector{Tx}) where {Tx,Ty}  mul!(y, A, x)
-end
-
 
 # methods involving RowVector from base/linalg/bidiag.jl, to deprecate
 @eval Base.LinAlg begin
@@ -2439,13 +2239,6 @@ end
     *(D::Diagonal, adjrowvec::Adjoint{<:Any,<:RowVector}) = (rowvec = adjrowvec.parent; D*rvadjoint(rowvec))
 end
 
-# methods involving RowVector from base/sparse/linalg.jl, to deprecate
-@eval Base.SparseArrays begin
-    \(::SparseMatrixCSC, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
-    \(::Adjoint{<:Any,<:SparseMatrixCSC}, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
-    \(::Transpose{<:Any,<:SparseMatrixCSC}, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
-end
-
 # methods involving RowVector from base/linalg/qr.jl, to deprecate
 @eval Base.LinAlg begin
     *(rowvec::RowVector, adjB::Adjoint{<:Any,<:AbstractQ}) = (B = adjB.parent; rvadjoint(B*rvadjoint(rowvec)))
@@ -2504,11 +2297,6 @@ end
     \(A::Transpose{<:Any,<:Factorization{<:Real}}, B::RowVector) = transpose(A.parent) \ B
 end
 
-# methods involving RowVector from base/sparse/higherorderfns.jl, to deprecate
-@eval Base.SparseArrays.HigherOrderFns begin
-    BroadcastStyle(::Type{<:Base.RowVector{T,<:Vector}}) where T = Broadcast.MatrixStyle()
-end
-
 # methods involving RowVector from base/linalg/symmetric.jl, to deprecate
 @eval Base.LinAlg begin
     *(A::RowVector, transB::Transpose{<:Any,<:RealHermSymComplexSym}) = A * transB.parent
@@ -2795,6 +2583,31 @@ end
 @deprecate_moved sum_kbn "KahanSummation"
 @deprecate_moved cumsum_kbn "KahanSummation"
 
+# PR #25249: SparseArrays to stdlib
+## the Base.SparseArrays module itself and exported types are deprecated in base/sysimg.jl
+## functions that were re-exported from Base
+@deprecate_moved nonzeros   "SparseArrays" true true
+@deprecate_moved permute    "SparseArrays" true true
+@deprecate_moved blkdiag    "SparseArrays" true true
+@deprecate_moved dropzeros  "SparseArrays" true true
+@deprecate_moved dropzeros! "SparseArrays" true true
+@deprecate_moved issparse   "SparseArrays" true true
+@deprecate_moved sparse     "SparseArrays" true true
+@deprecate_moved sparsevec  "SparseArrays" true true
+@deprecate_moved spdiagm    "SparseArrays" true true
+@deprecate_moved sprand     "SparseArrays" true true
+@deprecate_moved sprandn    "SparseArrays" true true
+@deprecate_moved spzeros    "SparseArrays" true true
+@deprecate_moved rowvals    "SparseArrays" true true
+@deprecate_moved nzrange    "SparseArrays" true true
+@deprecate_moved nnz        "SparseArrays" true true
+## functions that where exported from Base.SparseArrays but not from Base
+@deprecate_moved droptol!   "SparseArrays" false true
+## deprecated functions that are moved to stdlib/SparseArrays/src/deprecated.jl
+@deprecate_moved spones     "SparseArrays" true true
+@deprecate_moved speye      "SparseArrays" true true
+
+
 # PR #25021
 @deprecate_moved normalize_string "Unicode" true true
 @deprecate_moved graphemes "Unicode" true true
diff --git a/base/exports.jl b/base/exports.jl
index bbc505393a62e8..740e544042b0eb 100644
--- a/base/exports.jl
+++ b/base/exports.jl
@@ -434,7 +434,6 @@ export
     minimum,
     minmax,
     ndims,
-    nonzeros,
     ones,
     parent,
     parentindices,
@@ -442,7 +441,6 @@ export
     partialsort!,
     partialsortperm,
     partialsortperm!,
-    permute,
     permute!,
     permutedims,
     permutedims!,
@@ -513,7 +511,6 @@ export
 # linear algebra
     bkfact!,
     bkfact,
-    blkdiag,
     chol,
     cholfact!,
     cholfact,
@@ -594,10 +591,6 @@ export
     ⋅,
     ×,
 
-# sparse
-    dropzeros,
-    dropzeros!,
-
 # bitarrays
     falses,
     flipbits!,
@@ -1183,22 +1176,4 @@ export
     @goto,
     @view,
     @views,
-    @static,
-
-# SparseArrays module re-exports
-    SparseArrays,
-    AbstractSparseArray,
-    AbstractSparseMatrix,
-    AbstractSparseVector,
-    SparseMatrixCSC,
-    SparseVector,
-    issparse,
-    sparse,
-    sparsevec,
-    spdiagm,
-    sprand,
-    sprandn,
-    spzeros,
-    rowvals,
-    nzrange,
-    nnz
+    @static
diff --git a/base/linalg/bidiag.jl b/base/linalg/bidiag.jl
index 34860fefde0986..dbab48db775cf9 100644
--- a/base/linalg/bidiag.jl
+++ b/base/linalg/bidiag.jl
@@ -178,7 +178,8 @@ broadcast(::typeof(big), B::Bidiagonal) = Bidiagonal(big.(B.dv), big.(B.ev), B.u
 # On the other hand, similar(B, [neweltype,] shape...) should yield a sparse matrix.
 # The first method below effects the former, and the second the latter.
 similar(B::Bidiagonal, ::Type{T}) where {T} = Bidiagonal(similar(B.dv, T), similar(B.ev, T), B.uplo)
-similar(B::Bidiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+# The method below is moved to SparseArrays for now
+# similar(B::Bidiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
 
 
 ###################
diff --git a/base/linalg/dense.jl b/base/linalg/dense.jl
index 74e54947810341..76109f7fd63e87 100644
--- a/base/linalg/dense.jl
+++ b/base/linalg/dense.jl
@@ -303,8 +303,6 @@ Vector `kv.second` will be placed on the `kv.first` diagonal.
 versions with fast arithmetic, see [`Diagonal`](@ref), [`Bidiagonal`](@ref)
 [`Tridiagonal`](@ref) and [`SymTridiagonal`](@ref).
 
-See also: [`spdiagm`](@ref)
-
 # Examples
 ```jldoctest
 julia> diagm(1 => [1,2,3])
diff --git a/base/linalg/diagonal.jl b/base/linalg/diagonal.jl
index c33b00076f7d14..80ac284f2d12e4 100644
--- a/base/linalg/diagonal.jl
+++ b/base/linalg/diagonal.jl
@@ -59,7 +59,8 @@ Array(D::Diagonal) = Matrix(D)
 # On the other hand, similar(D, [neweltype,] shape...) should yield a sparse matrix.
 # The first method below effects the former, and the second the latter.
 similar(D::Diagonal, ::Type{T}) where {T} = Diagonal(similar(D.diag, T))
-similar(D::Diagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+# The method below is moved to SparseArrays for now
+# similar(D::Diagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
 
 copyto!(D1::Diagonal, D2::Diagonal) = (copyto!(D1.diag, D2.diag); D1)
 
diff --git a/base/linalg/lapack.jl b/base/linalg/lapack.jl
index 29bf3838773369..c39210eacf353d 100644
--- a/base/linalg/lapack.jl
+++ b/base/linalg/lapack.jl
@@ -3759,7 +3759,7 @@ for (stev, stebz, stegr, stein, elty) in
             chklapackerror(info[])
             if any(ifail .!= 0)
                 # TODO: better error message / type
-                error("failed to converge eigenvectors:\n$(nonzeros(ifail))")
+                error("failed to converge eigenvectors:\n$(find(x->x != 0, ifail))")
             end
             z
         end
diff --git a/base/linalg/tridiag.jl b/base/linalg/tridiag.jl
index 383abe2c22001b..f83c8a60833656 100644
--- a/base/linalg/tridiag.jl
+++ b/base/linalg/tridiag.jl
@@ -111,7 +111,8 @@ end
 # On the other hand, similar(S, [neweltype,] shape...) should yield a sparse matrix.
 # The first method below effects the former, and the second the latter.
 similar(S::SymTridiagonal, ::Type{T}) where {T} = SymTridiagonal(similar(S.dv, T), similar(S.ev, T))
-similar(S::SymTridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+# The method below is moved to SparseArrays for now
+# similar(S::SymTridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
 
 #Elementary operations
 broadcast(::typeof(abs), M::SymTridiagonal) = SymTridiagonal(abs.(M.dv), abs.(M.ev))
@@ -497,7 +498,8 @@ Array(M::Tridiagonal) = Matrix(M)
 # On the other hand, similar(M, [neweltype,] shape...) should yield a sparse matrix.
 # The first method below effects the former, and the second the latter.
 similar(M::Tridiagonal, ::Type{T}) where {T} = Tridiagonal(similar(M.dl, T), similar(M.d, T), similar(M.du, T))
-similar(M::Tridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+# The method below is moved to SparseArrays for now
+# similar(M::Tridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
 
 # Operations on Tridiagonal matrices
 copyto!(dest::Tridiagonal, src::Tridiagonal) = (copyto!(dest.dl, src.dl); copyto!(dest.d, src.d); copyto!(dest.du, src.du); dest)
diff --git a/base/sysimg.jl b/base/sysimg.jl
index e8b57aaa39a506..ed1274125e71a3 100644
--- a/base/sysimg.jl
+++ b/base/sysimg.jl
@@ -427,10 +427,6 @@ include("libgit2/libgit2.jl")
 # package manager
 include("pkg/pkg.jl")
 
-# sparse matrices, vectors, and sparse linear algebra
-include("sparse/sparse.jl")
-using .SparseArrays
-
 include("asyncmap.jl")
 
 # worker threads
@@ -499,6 +495,7 @@ Base.require(:IterativeEigensolvers)
 Base.require(:Mmap)
 Base.require(:Profile)
 Base.require(:SharedArrays)
+Base.require(:SparseArrays)
 Base.require(:SuiteSparse)
 Base.require(:Test)
 Base.require(:Unicode)
@@ -512,6 +509,19 @@ Base.require(:Future)
     @deprecate_binding Profile root_module(:Profile) true ", run `using Profile` instead"
     @deprecate_binding Dates root_module(:Dates) true ", run `using Dates` instead"
     @deprecate_binding Distributed root_module(:Distributed) true ", run `using Distributed` instead"
+
+    # PR #25249
+    @deprecate_binding SparseArrays root_module(:SparseArrays) true ", run `using SparseArrays` instead"
+    @deprecate_binding(AbstractSparseArray, root_module(:SparseArrays).AbstractSparseArray, true,
+        ", run `using SparseArrays` to load sparse array functionality")
+    @deprecate_binding(AbstractSparseMatrix, root_module(:SparseArrays).AbstractSparseMatrix, true,
+        ", run `using SparseArrays` to load sparse array functionality")
+    @deprecate_binding(AbstractSparseVector, root_module(:SparseArrays).AbstractSparseVector, true,
+        ", run `using SparseArrays` to load sparse array functionality")
+    @deprecate_binding(SparseMatrixCSC, root_module(:SparseArrays).SparseMatrixCSC, true,
+        ", run `using SparseArrays` to load sparse array functionality")
+    @deprecate_binding(SparseVector, root_module(:SparseArrays).SparseVector, true,
+        ", run `using SparseArrays` to load sparse array functionality")
 end
 
 empty!(LOAD_PATH)
diff --git a/doc/src/base/arrays.md b/doc/src/base/arrays.md
index c3f1efca5d5ea6..b2a1074ab520d3 100644
--- a/doc/src/base/arrays.md
+++ b/doc/src/base/arrays.md
@@ -188,30 +188,3 @@ and can be converted to/from the latter via `Array(bitarray)` and `BitArray(arra
 ```@docs
 Base.flipbits!
 ```
-
-## [Sparse Vectors and Matrices](@id stdlib-sparse-arrays)
-
-Sparse vectors and matrices largely support the same set of operations as their dense counterparts.
-The following functions are specific to sparse arrays.
-
-```@docs
-Base.SparseArrays.SparseVector
-Base.SparseArrays.SparseMatrixCSC
-Base.SparseArrays.sparse
-Base.SparseArrays.sparsevec
-Base.SparseArrays.issparse
-Base.SparseArrays.nnz
-Base.SparseArrays.spzeros
-Base.SparseArrays.spdiagm
-Base.SparseArrays.sprand
-Base.SparseArrays.sprandn
-Base.SparseArrays.nonzeros
-Base.SparseArrays.rowvals
-Base.SparseArrays.nzrange
-Base.SparseArrays.dropzeros!(::SparseMatrixCSC, ::Bool)
-Base.SparseArrays.dropzeros(::SparseMatrixCSC, ::Bool)
-Base.SparseArrays.dropzeros!(::SparseVector, ::Bool)
-Base.SparseArrays.dropzeros(::SparseVector, ::Bool)
-Base.SparseArrays.permute
-Base.permute!{Tv, Ti, Tp <: Integer, Tq <: Integer}(::SparseMatrixCSC{Tv,Ti}, ::SparseMatrixCSC{Tv,Ti}, ::AbstractArray{Tp,1}, ::AbstractArray{Tq,1})
-```
diff --git a/doc/src/base/base.md b/doc/src/base/base.md
index 82e4a2d55ba6e2..ba0136630a83b7 100644
--- a/doc/src/base/base.md
+++ b/doc/src/base/base.md
@@ -98,7 +98,6 @@ Base.Markdown
 Base.Meta
 Base.Pkg
 Base.Serializer
-Base.SparseArrays
 Base.StackTraces
 Base.Sys
 Base.Threads
diff --git a/doc/src/base/index.md b/doc/src/base/index.md
index 5356aaf26ffa0f..7dc8a9fff2879e 100644
--- a/doc/src/base/index.md
+++ b/doc/src/base/index.md
@@ -31,5 +31,6 @@
   * [Memory-mapped I/O](@ref)
   * [Base64](@ref)
   * [File Events](@ref lib-filewatching)
+  * [Sparse Arrays](@ref)
   * [Iterative Eigensolvers](@ref lib-itereigen)
   * [Printf](@ref)
diff --git a/doc/src/base/linalg.md b/doc/src/base/linalg.md
index a1b3ae0e292d5b..e6d500201aba2f 100644
--- a/doc/src/base/linalg.md
+++ b/doc/src/base/linalg.md
@@ -91,7 +91,6 @@ Base.LinAlg.pinv
 Base.LinAlg.nullspace
 Base.repmat
 Base.kron
-Base.SparseArrays.blkdiag
 Base.LinAlg.linreg
 Base.LinAlg.exp(::StridedMatrix{<:Base.LinAlg.BlasFloat})
 Base.LinAlg.log(::StridedMatrix)
diff --git a/doc/src/index.md b/doc/src/index.md
index 43e8bd9f9578ee..b1028b90017a8c 100644
--- a/doc/src/index.md
+++ b/doc/src/index.md
@@ -81,6 +81,7 @@
   * [Delimited Files](@ref)
   * [Distributed Computing](@ref)
   * [File Events](@ref lib-filewatching)
+  * [Sparse Arrays](@ref)
   * [Iterative Eigensolvers](@ref lib-itereigen)
   * [Memory-mapped I/O](@ref)
   * [Printf](@ref)
diff --git a/doc/src/manual/arrays.md b/doc/src/manual/arrays.md
index 1d187a3d769b0e..d9071cf0eb2745 100644
--- a/doc/src/manual/arrays.md
+++ b/doc/src/manual/arrays.md
@@ -762,195 +762,3 @@ julia> r
  -1.58553  -0.921517
   0.0       0.866567
 ```
-
-## Sparse Vectors and Matrices
-
-Julia has built-in support for sparse vectors and
-[sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix). Sparse arrays are arrays
-that contain enough zeros that storing them in a special data structure leads to savings
-in space and execution time, compared to dense arrays.
-
-### [Compressed Sparse Column (CSC) Sparse Matrix Storage](@id man-csc)
-
-In Julia, sparse matrices are stored in the [Compressed Sparse Column (CSC) format](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_.28CSC_or_CCS.29).
-Julia sparse matrices have the type [`SparseMatrixCSC{Tv,Ti}`](@ref), where `Tv` is the
-type of the stored values, and `Ti` is the integer type for storing column pointers and
-row indices. The internal representation of `SparseMatrixCSC` is as follows:
-
-```julia
-struct SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrix{Tv,Ti}
-    m::Int                  # Number of rows
-    n::Int                  # Number of columns
-    colptr::Vector{Ti}      # Column i is in colptr[i]:(colptr[i+1]-1)
-    rowval::Vector{Ti}      # Row indices of stored values
-    nzval::Vector{Tv}       # Stored values, typically nonzeros
-end
-```
-
-The compressed sparse column storage makes it easy and quick to access the elements in the column
-of a sparse matrix, whereas accessing the sparse matrix by rows is considerably slower. Operations
-such as insertion of previously unstored entries one at a time in the CSC structure tend to be slow. This is
-because all elements of the sparse matrix that are beyond the point of insertion have to be moved
-one place over.
-
-All operations on sparse matrices are carefully implemented to exploit the CSC data structure
-for performance, and to avoid expensive operations.
-
-If you have data in CSC format from a different application or library, and wish to import it
-in Julia, make sure that you use 1-based indexing. The row indices in every column need to be
-sorted. If your `SparseMatrixCSC` object contains unsorted row indices, one quick way to sort
-them is by doing a double transpose.
-
-In some applications, it is convenient to store explicit zero values in a `SparseMatrixCSC`. These
-*are* accepted by functions in `Base` (but there is no guarantee that they will be preserved in
-mutating operations). Such explicitly stored zeros are treated as structural nonzeros by many
-routines. The [`nnz`](@ref) function returns the number of elements explicitly stored in the
-sparse data structure, including structural nonzeros. In order to count the exact number of
-numerical nonzeros, use [`count(!iszero, x)`](@ref), which inspects every stored element of a sparse
-matrix. [`dropzeros`](@ref), and the in-place [`dropzeros!`](@ref), can be used to
-remove stored zeros from the sparse matrix.
-
-```jldoctest
-julia> A = sparse([1, 2, 3], [1, 2, 3], [0, 2, 0])
-3×3 SparseMatrixCSC{Int64,Int64} with 3 stored entries:
-  [1, 1]  =  0
-  [2, 2]  =  2
-  [3, 3]  =  0
-
-julia> dropzeros(A)
-3×3 SparseMatrixCSC{Int64,Int64} with 1 stored entry:
-  [2, 2]  =  2
-```
-
-### Sparse Vector Storage
-
-Sparse vectors are stored in a close analog to compressed sparse column format for sparse
-matrices. In Julia, sparse vectors have the type [`SparseVector{Tv,Ti}`](@ref) where `Tv`
-is the type of the stored values and `Ti` the integer type for the indices. The internal
-representation is as follows:
-
-```julia
-struct SparseVector{Tv,Ti<:Integer} <: AbstractSparseVector{Tv,Ti}
-    n::Int              # Length of the sparse vector
-    nzind::Vector{Ti}   # Indices of stored values
-    nzval::Vector{Tv}   # Stored values, typically nonzeros
-end
-```
-
-As for [`SparseMatrixCSC`](@ref), the `SparseVector` type can also contain explicitly
-stored zeros. (See [Sparse Matrix Storage](@ref man-csc).).
-
-### Sparse Vector and Matrix Constructors
-
-The simplest way to create a sparse array is to use a function equivalent to the [`zeros`](@ref)
-function that Julia provides for working with dense arrays. To produce a
-sparse array instead, you can use the same name with an `sp` prefix:
-
-```jldoctest
-julia> spzeros(3)
-3-element SparseVector{Float64,Int64} with 0 stored entries
-```
-
-The [`sparse`](@ref) function is often a handy way to construct sparse arrays. For
-example, to construct a sparse matrix we can input a vector `I` of row indices, a vector
-`J` of column indices, and a vector `V` of stored values (this is also known as the
-[COO (coordinate) format](https://en.wikipedia.org/wiki/Sparse_matrix#Coordinate_list_.28COO.29)).
-`sparse(I,J,V)` then constructs a sparse matrix such that `S[I[k], J[k]] = V[k]`. The
-equivalent sparse vector constructor is [`sparsevec`](@ref), which takes the (row) index
-vector `I` and the vector `V` with the stored values and constructs a sparse vector `R`
-such that `R[I[k]] = V[k]`.
-
-```jldoctest sparse_function
-julia> I = [1, 4, 3, 5]; J = [4, 7, 18, 9]; V = [1, 2, -5, 3];
-
-julia> S = sparse(I,J,V)
-5×18 SparseMatrixCSC{Int64,Int64} with 4 stored entries:
-  [1 ,  4]  =  1
-  [4 ,  7]  =  2
-  [5 ,  9]  =  3
-  [3 , 18]  =  -5
-
-julia> R = sparsevec(I,V)
-5-element SparseVector{Int64,Int64} with 4 stored entries:
-  [1]  =  1
-  [3]  =  -5
-  [4]  =  2
-  [5]  =  3
-```
-
-The inverse of the [`sparse`](@ref) and [`sparsevec`](@ref) functions is
-[`findnz`](@ref), which retrieves the inputs used to create the sparse array.
-There is also a [`findn`](@ref) function which only returns the index vectors.
-
-```jldoctest sparse_function
-julia> findnz(S)
-([1, 4, 5, 3], [4, 7, 9, 18], [1, 2, 3, -5])
-
-julia> findn(S)
-([1, 4, 5, 3], [4, 7, 9, 18])
-
-julia> findnz(R)
-([1, 3, 4, 5], [1, -5, 2, 3])
-
-julia> find(!iszero, R)
-4-element Array{Int64,1}:
- 1
- 3
- 4
- 5
-```
-
-Another way to create a sparse array is to convert a dense array into a sparse array using
-the [`sparse`](@ref) function:
-
-```jldoctest
-julia> sparse(Matrix(1.0I, 5, 5))
-5×5 SparseMatrixCSC{Float64,Int64} with 5 stored entries:
-  [1, 1]  =  1.0
-  [2, 2]  =  1.0
-  [3, 3]  =  1.0
-  [4, 4]  =  1.0
-  [5, 5]  =  1.0
-
-julia> sparse([1.0, 0.0, 1.0])
-3-element SparseVector{Float64,Int64} with 2 stored entries:
-  [1]  =  1.0
-  [3]  =  1.0
-```
-
-You can go in the other direction using the [`Array`](@ref) constructor. The [`issparse`](@ref)
-function can be used to query if a matrix is sparse.
-
-```jldoctest
-julia> issparse(spzeros(5))
-true
-```
-
-### Sparse matrix operations
-
-Arithmetic operations on sparse matrices also work as they do on dense matrices. Indexing of,
-assignment into, and concatenation of sparse matrices work in the same way as dense matrices.
-Indexing operations, especially assignment, are expensive, when carried out one element at a time.
-In many cases it may be better to convert the sparse matrix into `(I,J,V)` format using [`findnz`](@ref),
-manipulate the values or the structure in the dense vectors `(I,J,V)`, and then reconstruct
-the sparse matrix.
-
-### Correspondence of dense and sparse methods
-
-The following table gives a correspondence between built-in methods on sparse matrices and their
-corresponding methods on dense matrix types. In general, methods that generate sparse matrices
-differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern
-as a given sparse matrix `S`, or that the resulting sparse matrix has density `d`, i.e. each matrix
-element has a probability `d` of being non-zero.
-
-Details can be found in the [Sparse Vectors and Matrices](@ref stdlib-sparse-arrays)
-section of the Julia Base reference.
-
-| Sparse                     | Dense                  | Description                                                                                                                                                           |
-|:-------------------------- |:---------------------- |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| [`spzeros(m,n)`](@ref)     | [`zeros(m,n)`](@ref)   | Creates a *m*-by-*n* matrix of zeros. ([`spzeros(m,n)`](@ref) is empty.)                                                                                              |
-| [`sparse(I, n, n)`](@ref)  | [`Matrix(I,n,n)`](@ref)| Creates a *n*-by-*n* identity matrix.                                                                                                                                 |
-| [`Array(S)`](@ref)         | [`sparse(A)`](@ref)    | Interconverts between dense and sparse formats.                                                                                                                       |
-| [`sprand(m,n,d)`](@ref)    | [`rand(m,n)`](@ref)    | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed uniformly on the half-open interval ``[0, 1)``.                            |
-| [`sprandn(m,n,d)`](@ref)   | [`randn(m,n)`](@ref)   | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution.                  |
-| [`sprandn(m,n,d,X)`](@ref) | [`randn(m,n,X)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the *X* distribution. (Requires the `Distributions` package.) |
diff --git a/stdlib/Future/test/runtests.jl b/stdlib/Future/test/runtests.jl
index 4233dc0fad0bdb..820f4bdd3cedd7 100644
--- a/stdlib/Future/test/runtests.jl
+++ b/stdlib/Future/test/runtests.jl
@@ -2,6 +2,7 @@
 
 using Test
 using Future
+using SparseArrays
 
 @testset "Future.copy! for AbstractSet" begin
     for S = (Set, BitSet)
diff --git a/stdlib/IterativeEigensolvers/test/runtests.jl b/stdlib/IterativeEigensolvers/test/runtests.jl
index 06b094e03f84fc..6f9516d295f243 100644
--- a/stdlib/IterativeEigensolvers/test/runtests.jl
+++ b/stdlib/IterativeEigensolvers/test/runtests.jl
@@ -1,7 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 using IterativeEigensolvers
-using Test
+using Test, SparseArrays
 
 @testset "eigs" begin
     srand(1234)
diff --git a/stdlib/SparseArrays/docs/src/index.md b/stdlib/SparseArrays/docs/src/index.md
new file mode 100644
index 00000000000000..95e4b4f3bc4426
--- /dev/null
+++ b/stdlib/SparseArrays/docs/src/index.md
@@ -0,0 +1,216 @@
+# Sparse Arrays
+
+Julia has support for sparse vectors and [sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix)
+in the `SparseArrays` stdlib module. Sparse arrays are arrays that contain enough zeros
+that storing them in a special data structure leads to savings in space and execution time,
+compared to dense arrays.
+
+## [Compressed Sparse Column (CSC) Sparse Matrix Storage](@id man-csc)
+
+In Julia, sparse matrices are stored in the [Compressed Sparse Column (CSC) format](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_.28CSC_or_CCS.29).
+Julia sparse matrices have the type [`SparseMatrixCSC{Tv,Ti}`](@ref), where `Tv` is the
+type of the stored values, and `Ti` is the integer type for storing column pointers and
+row indices. The internal representation of `SparseMatrixCSC` is as follows:
+
+```julia
+struct SparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrix{Tv,Ti}
+    m::Int                  # Number of rows
+    n::Int                  # Number of columns
+    colptr::Vector{Ti}      # Column i is in colptr[i]:(colptr[i+1]-1)
+    rowval::Vector{Ti}      # Row indices of stored values
+    nzval::Vector{Tv}       # Stored values, typically nonzeros
+end
+```
+
+The compressed sparse column storage makes it easy and quick to access the elements in the column
+of a sparse matrix, whereas accessing the sparse matrix by rows is considerably slower. Operations
+such as insertion of previously unstored entries one at a time in the CSC structure tend to be slow. This is
+because all elements of the sparse matrix that are beyond the point of insertion have to be moved
+one place over.
+
+All operations on sparse matrices are carefully implemented to exploit the CSC data structure
+for performance, and to avoid expensive operations.
+
+If you have data in CSC format from a different application or library, and wish to import it
+in Julia, make sure that you use 1-based indexing. The row indices in every column need to be
+sorted. If your `SparseMatrixCSC` object contains unsorted row indices, one quick way to sort
+them is by doing a double transpose.
+
+In some applications, it is convenient to store explicit zero values in a `SparseMatrixCSC`. These
+*are* accepted by functions in `Base` (but there is no guarantee that they will be preserved in
+mutating operations). Such explicitly stored zeros are treated as structural nonzeros by many
+routines. The [`nnz`](@ref) function returns the number of elements explicitly stored in the
+sparse data structure, including structural nonzeros. In order to count the exact number of
+numerical nonzeros, use [`count(!iszero, x)`](@ref), which inspects every stored element of a sparse
+matrix. [`dropzeros`](@ref), and the in-place [`dropzeros!`](@ref), can be used to
+remove stored zeros from the sparse matrix.
+
+```jldoctest
+julia> A = sparse([1, 2, 3], [1, 2, 3], [0, 2, 0])
+3×3 SparseMatrixCSC{Int64,Int64} with 3 stored entries:
+  [1, 1]  =  0
+  [2, 2]  =  2
+  [3, 3]  =  0
+
+julia> dropzeros(A)
+3×3 SparseMatrixCSC{Int64,Int64} with 1 stored entry:
+  [2, 2]  =  2
+```
+
+## Sparse Vector Storage
+
+Sparse vectors are stored in a close analog to compressed sparse column format for sparse
+matrices. In Julia, sparse vectors have the type [`SparseVector{Tv,Ti}`](@ref) where `Tv`
+is the type of the stored values and `Ti` the integer type for the indices. The internal
+representation is as follows:
+
+```julia
+struct SparseVector{Tv,Ti<:Integer} <: AbstractSparseVector{Tv,Ti}
+    n::Int              # Length of the sparse vector
+    nzind::Vector{Ti}   # Indices of stored values
+    nzval::Vector{Tv}   # Stored values, typically nonzeros
+end
+```
+
+As for [`SparseMatrixCSC`](@ref), the `SparseVector` type can also contain explicitly
+stored zeros. (See [Sparse Matrix Storage](@ref man-csc).).
+
+## Sparse Vector and Matrix Constructors
+
+The simplest way to create a sparse array is to use a function equivalent to the [`zeros`](@ref)
+function that Julia provides for working with dense arrays. To produce a
+sparse array instead, you can use the same name with an `sp` prefix:
+
+```jldoctest
+julia> spzeros(3)
+3-element SparseVector{Float64,Int64} with 0 stored entries
+```
+
+The [`sparse`](@ref) function is often a handy way to construct sparse arrays. For
+example, to construct a sparse matrix we can input a vector `I` of row indices, a vector
+`J` of column indices, and a vector `V` of stored values (this is also known as the
+[COO (coordinate) format](https://en.wikipedia.org/wiki/Sparse_matrix#Coordinate_list_.28COO.29)).
+`sparse(I,J,V)` then constructs a sparse matrix such that `S[I[k], J[k]] = V[k]`. The
+equivalent sparse vector constructor is [`sparsevec`](@ref), which takes the (row) index
+vector `I` and the vector `V` with the stored values and constructs a sparse vector `R`
+such that `R[I[k]] = V[k]`.
+
+```jldoctest sparse_function
+julia> I = [1, 4, 3, 5]; J = [4, 7, 18, 9]; V = [1, 2, -5, 3];
+
+julia> S = sparse(I,J,V)
+5×18 SparseMatrixCSC{Int64,Int64} with 4 stored entries:
+  [1 ,  4]  =  1
+  [4 ,  7]  =  2
+  [5 ,  9]  =  3
+  [3 , 18]  =  -5
+
+julia> R = sparsevec(I,V)
+5-element SparseVector{Int64,Int64} with 4 stored entries:
+  [1]  =  1
+  [3]  =  -5
+  [4]  =  2
+  [5]  =  3
+```
+
+The inverse of the [`sparse`](@ref) and [`sparsevec`](@ref) functions is
+[`findnz`](@ref), which retrieves the inputs used to create the sparse array.
+There is also a [`findn`](@ref) function which only returns the index vectors.
+
+```jldoctest sparse_function
+julia> findnz(S)
+([1, 4, 5, 3], [4, 7, 9, 18], [1, 2, 3, -5])
+
+julia> findn(S)
+([1, 4, 5, 3], [4, 7, 9, 18])
+
+julia> findnz(R)
+([1, 3, 4, 5], [1, -5, 2, 3])
+
+julia> find(!iszero, R)
+4-element Array{Int64,1}:
+ 1
+ 3
+ 4
+ 5
+```
+
+Another way to create a sparse array is to convert a dense array into a sparse array using
+the [`sparse`](@ref) function:
+
+```jldoctest
+julia> sparse(Matrix(1.0I, 5, 5))
+5×5 SparseMatrixCSC{Float64,Int64} with 5 stored entries:
+  [1, 1]  =  1.0
+  [2, 2]  =  1.0
+  [3, 3]  =  1.0
+  [4, 4]  =  1.0
+  [5, 5]  =  1.0
+
+julia> sparse([1.0, 0.0, 1.0])
+3-element SparseVector{Float64,Int64} with 2 stored entries:
+  [1]  =  1.0
+  [3]  =  1.0
+```
+
+You can go in the other direction using the [`Array`](@ref) constructor. The [`issparse`](@ref)
+function can be used to query if a matrix is sparse.
+
+```jldoctest
+julia> issparse(spzeros(5))
+true
+```
+
+## Sparse matrix operations
+
+Arithmetic operations on sparse matrices also work as they do on dense matrices. Indexing of,
+assignment into, and concatenation of sparse matrices work in the same way as dense matrices.
+Indexing operations, especially assignment, are expensive, when carried out one element at a time.
+In many cases it may be better to convert the sparse matrix into `(I,J,V)` format using [`findnz`](@ref),
+manipulate the values or the structure in the dense vectors `(I,J,V)`, and then reconstruct
+the sparse matrix.
+
+## Correspondence of dense and sparse methods
+
+The following table gives a correspondence between built-in methods on sparse matrices and their
+corresponding methods on dense matrix types. In general, methods that generate sparse matrices
+differ from their dense counterparts in that the resulting matrix follows the same sparsity pattern
+as a given sparse matrix `S`, or that the resulting sparse matrix has density `d`, i.e. each matrix
+element has a probability `d` of being non-zero.
+
+Details can be found in the [Sparse Vectors and Matrices](@ref stdlib-sparse-arrays)
+section of the standard library reference.
+
+| Sparse                     | Dense                  | Description                                                                                                                                                           |
+|:-------------------------- |:---------------------- |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [`spzeros(m,n)`](@ref)     | [`zeros(m,n)`](@ref)   | Creates a *m*-by-*n* matrix of zeros. ([`spzeros(m,n)`](@ref) is empty.)                                                                                              |
+| [`sparse(I, n, n)`](@ref)  | [`Matrix(I,n,n)`](@ref)| Creates a *n*-by-*n* identity matrix.                                                                                                                                 |
+| [`Array(S)`](@ref)         | [`sparse(A)`](@ref)    | Interconverts between dense and sparse formats.                                                                                                                       |
+| [`sprand(m,n,d)`](@ref)    | [`rand(m,n)`](@ref)    | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed uniformly on the half-open interval ``[0, 1)``.                            |
+| [`sprandn(m,n,d)`](@ref)   | [`randn(m,n)`](@ref)   | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution.                  |
+| [`sprandn(m,n,d,X)`](@ref) | [`randn(m,n,X)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the *X* distribution. (Requires the `Distributions` package.) |
+
+# [Sparse Arrays](@id stdlib-sparse-arrays)
+
+```@docs
+SparseArrays.SparseVector
+SparseArrays.SparseMatrixCSC
+SparseArrays.sparse
+SparseArrays.sparsevec
+SparseArrays.issparse
+SparseArrays.nnz
+SparseArrays.spzeros
+SparseArrays.spdiagm
+SparseArrays.blkdiag
+SparseArrays.sprand
+SparseArrays.sprandn
+SparseArrays.nonzeros
+SparseArrays.rowvals
+SparseArrays.nzrange
+SparseArrays.dropzeros!(::SparseMatrixCSC, ::Bool)
+SparseArrays.dropzeros(::SparseMatrixCSC, ::Bool)
+SparseArrays.dropzeros!(::SparseVector, ::Bool)
+SparseArrays.dropzeros(::SparseVector, ::Bool)
+SparseArrays.permute
+permute!{Tv, Ti, Tp <: Integer, Tq <: Integer}(::SparseMatrixCSC{Tv,Ti}, ::SparseMatrixCSC{Tv,Ti}, ::AbstractArray{Tp,1}, ::AbstractArray{Tq,1})
+```
diff --git a/base/sparse/sparse.jl b/stdlib/SparseArrays/src/SparseArrays.jl
similarity index 75%
rename from base/sparse/sparse.jl
rename to stdlib/SparseArrays/src/SparseArrays.jl
index 28ca964b7bdc05..ab00566065773f 100644
--- a/base/sparse/sparse.jl
+++ b/stdlib/SparseArrays/src/SparseArrays.jl
@@ -1,5 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
+__precompile__(true)
+
 """
 Support for sparse arrays. Provides `AbstractSparseArray` and subtypes.
 """
@@ -36,5 +38,14 @@ include("sparsematrix.jl")
 include("sparsevector.jl")
 include("higherorderfns.jl")
 include("linalg.jl")
+include("deprecated.jl")
+
+
+# temporarily moved here and commented out from from base/linalg/diagonal.jl, base/linalg/tridiag.jl
+# and base/linalg/bidiag.jl due to their usage of spzeros
+similar(B::Bidiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+similar(D::Diagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+similar(S::SymTridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
+similar(M::Tridiagonal, ::Type{T}, dims::Union{Dims{1},Dims{2}}) where {T} = spzeros(T, dims...)
 
 end
diff --git a/base/sparse/abstractsparse.jl b/stdlib/SparseArrays/src/abstractsparse.jl
similarity index 100%
rename from base/sparse/abstractsparse.jl
rename to stdlib/SparseArrays/src/abstractsparse.jl
diff --git a/stdlib/SparseArrays/src/deprecated.jl b/stdlib/SparseArrays/src/deprecated.jl
new file mode 100644
index 00000000000000..8cae8ed0d544f0
--- /dev/null
+++ b/stdlib/SparseArrays/src/deprecated.jl
@@ -0,0 +1,229 @@
+# This file is a part of Julia. License is MIT: https://julialang.org/license
+
+using Base: @deprecate, depwarn
+
+# BEGIN 0.7 deprecations
+
+# PR #22475
+import Base: cat
+@deprecate cat(::Type{Val{N}}, A::_SparseConcatGroup...) where {N} cat(Val(N), A...)
+@deprecate cat(::Type{Val{N}}, A::_DenseConcatGroup...) where {N} cat(Val(N), A...)
+
+# deprecate remaining vectorized methods over SparseVectors (zero-preserving)
+for op in (:floor, :ceil, :trunc, :round,
+        :log1p, :expm1,  :sinpi,
+        :sin,   :tan,    :sind,   :tand,
+        :asin,  :atan,   :asind,  :atand,
+        :sinh,  :tanh,   :asinh,  :atanh)
+    @eval import Base.Math: $op
+    @eval @deprecate ($op)(x::AbstractSparseVector{<:Number,<:Integer}) ($op).(x)
+end
+# deprecate remaining vectorized methods over SparseVectors (not-zero-preserving)
+for op in (:exp, :exp2, :exp10, :log, :log2, :log10,
+           :cos, :cosd, :acos, :cosh, :cospi,
+           :csc, :cscd, :acot, :csch, :acsch,
+           :cot, :cotd, :acosd, :coth,
+           :sec, :secd, :acotd, :sech, :asech)
+    @eval import Base.Math: $op
+    @eval @deprecate ($op)(x::AbstractSparseVector{<:Number,<:Integer}) ($op).(x)
+end
+
+# PR 23341
+import Base.LinAlg: diagm
+@deprecate diagm(A::SparseMatrixCSC) sparse(Diagonal(sparsevec(A)))
+
+# PR #23757
+@deprecate spdiagm(x::AbstractVector) sparse(Diagonal(x))
+function spdiagm(x::AbstractVector, d::Number)
+    depwarn(string("`spdiagm(x::AbstractVector, d::Number)` is deprecated, use ",
+        "`spdiagm(d => x)` instead, which now returns a square matrix. To preserve the old ",
+        "behaviour, use `sparse(SparseArrays.spdiagm_internal(d => x)...)`"), :spdiagm)
+    I, J, V = spdiagm_internal(d => x)
+    return sparse(I, J, V)
+end
+function spdiagm(x, d)
+    depwarn(string("`spdiagm((x1, x2, ...), (d1, d2, ...))` is deprecated, use ",
+        "`spdiagm(d1 => x1, d2 => x2, ...)` instead, which now returns a square matrix. ",
+        "To preserve the old behaviour, use ",
+        "`sparse(SparseArrays.spdiagm_internal(d1 => x1, d2 => x2, ...)...)`"), :spdiagm)
+    I, J, V = spdiagm_internal((d[i] => x[i] for i in 1:length(x))...)
+    return sparse(I, J, V)
+end
+function spdiagm(x, d, m::Integer, n::Integer)
+    depwarn(string("`spdiagm((x1, x2, ...), (d1, d2, ...), m, n)` is deprecated, use ",
+        "`spdiagm(d1 => x1, d2 => x2, ...)` instead, which now returns a square matrix. ",
+        "To specify a non-square matrix and preserve the old behaviour, use ",
+        "`I, J, V = SparseArrays.spdiagm_internal(d1 => x1, d2 => x2, ...); sparse(I, J, V, m, n)`"), :spdiagm)
+    I, J, V = spdiagm_internal((d[i] => x[i] for i in 1:length(x))...)
+    return sparse(I, J, V, m, n)
+end
+
+@deprecate sparse(s::UniformScaling, m::Integer) sparse(s, m, m)
+
+# PR #25037
+@deprecate spones(A::SparseMatrixCSC) LinAlg.fillstored!(copy(A), 1)
+@deprecate spones(A::SparseVector)    LinAlg.fillstored!(copy(A), 1)
+export spones
+
+# full for sparse arrays
+import Base: full
+function full(S::Union{SparseVector,SparseMatrixCSC})
+    (arrtypestr, desttypestr) =
+        isa(S, SparseVector)    ? ("SparseVector",    "Vector") :
+        isa(S, SparseMatrixCSC) ? ("SparseMatrixCSC", "Matrix") :
+            error("should not be reachable!")
+    depwarn(string(
+        "`full(S::$(arrtypestr))` (and `full` in general) has been deprecated. ",
+        "To replace `full(S::$(arrtypestr))`, consider `$(desttypestr)(S)` or, ",
+        "if that option is too narrow, `Array(S)`."), :full)
+    return Array(S)
+end
+
+# issue #22849
+import Base: reinterpret
+@deprecate reinterpret(::Type{T}, a::SparseMatrixCSC{S}, dims::NTuple{N,Int}) where {T, S, N} reinterpret(T, reshape(a, dims))
+
+# deprecate speye
+export speye
+function speye(n::Integer)
+    depwarn(string("`speye(n::Integer)` has been deprecated in favor of `I`, `sparse`, and ",
+                    "`SparseMatrixCSC` constructor methods. For a direct replacement, consider ",
+                    "`sparse(1.0I, n, n)`, `SparseMatrixCSC(1.0I, n, n)`, or `SparseMatrixCSC{Float64}(I, n, n)`. ",
+                    "If `Float64` element type is not necessary, consider the shorter `sparse(I, n, n)` ",
+                    "or `SparseMatrixCSC(I, n, n)` (with default `eltype(I)` of `Bool`)."), :speye)
+    return sparse(1.0I, n, n)
+end
+function speye(m::Integer, n::Integer)
+    depwarn(string("`speye(m::Integer, n::Integer)` has been deprecated in favor of `I`, ",
+                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
+                    "replacement, consider `sparse(1.0I, m, n)`, `SparseMatrixCSC(1.0I, m, n)`, ",
+                    "or `SparseMatrixCSC{Float64}(I, m, n)`. If `Float64` element type is not ",
+                    " necessary, consider the shorter `sparse(I, m, n)` or `SparseMatrixCSC(I, m, n)` ",
+                    "(with default `eltype(I)` of `Bool`)."), :speye)
+    return sparse(1.0I, m, n)
+end
+function speye(::Type{T}, n::Integer) where T
+    depwarn(string("`speye(T, n::Integer)` has been deprecated in favor of `I`, `sparse`, and ",
+                    "`SparseMatrixCSC` constructor methods. For a direct replacement, consider ",
+                    "`sparse(T(1)I, n, n)` if `T` is concrete or `SparseMatrixCSC{T}(I, n, n)` ",
+                    "if `T` is either concrete or abstract. If element type `T` is not necessary, ",
+                    "consider the shorter `sparse(I, n, n)` or `SparseMatrixCSC(I, n, n)` ",
+                    "(with default `eltype(I)` of `Bool`)."), :speye)
+    return SparseMatrixCSC{T}(I, n, n)
+end
+function speye(::Type{T}, m::Integer, n::Integer) where T
+    depwarn(string("`speye(T, m::Integer, n::Integer)` has been deprecated in favor of `I`, ",
+                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
+                    "replacement, consider `sparse(T(1)I, m, n)` if `T` is concrete or ",
+                    "`SparseMatrixCSC{T}(I, m, n)` if `T` is either concrete or abstract. ",
+                    "If element type `T` is not necessary, consider the shorter ",
+                    "`sparse(I, m, n)` or `SparseMatrixCSC(I, m, n)` (with default `eltype(I)` ",
+                    "of `Bool`)."), :speye)
+    return SparseMatrixCSC{T}(I, m, n)
+end
+function speye(S::SparseMatrixCSC{T}) where T
+    depwarn(string("`speye(S::SparseMatrixCSC{T})` has been deprecated in favor of `I`, ",
+                    "`sparse`, and `SparseMatrixCSC` constructor methods. For a direct ",
+                    "replacement, consider `sparse(T(1)I, size(S)...)` if `T` is concrete or ",
+                    "`SparseMatrixCSC{eltype(S)}(I, size(S))` if `T` is either concrete or abstract. ",
+                    "If preserving element type `T` is not necessary, consider the shorter ",
+                    "`sparse(I, size(S)...)` or `SparseMatrixCSC(I, size(S))` (with default ",
+                    "`eltype(I)` of `Bool`)."), :speye)
+    return SparseMatrixCSC{T}(I, size(S)...)
+end
+
+# former imports into SparseArrays
+import Base: A_mul_B!, Ac_mul_B, Ac_mul_B!, At_mul_B, At_mul_B!
+import Base: A_mul_Bc, A_mul_Bt, Ac_mul_Bc, At_mul_Bt
+import Base: At_ldiv_B, Ac_ldiv_B, A_ldiv_B!
+import Base.LinAlg: At_ldiv_B!, Ac_ldiv_B!, A_rdiv_B!, A_rdiv_Bc!, mul!, ldiv!, rdiv!
+
+# A[ct]_(mul|ldiv|rdiv)_B[ct][!] methods from base/sparse/linalg.jl, to deprecate
+using Base.LinAlg: Adjoint, Transpose
+@deprecate Ac_ldiv_B(A::SparseMatrixCSC, B::RowVector)  (\)(adjoint(A), B)
+@deprecate At_ldiv_B(A::SparseMatrixCSC, B::RowVector)  (\)(transpose(A), B)
+@deprecate Ac_ldiv_B(A::SparseMatrixCSC, B::AbstractVecOrMat)   (\)(adjoint(A), B)
+@deprecate At_ldiv_B(A::SparseMatrixCSC, B::AbstractVecOrMat)   (\)(transpose(A), B)
+@deprecate A_rdiv_Bc!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}  rdiv!(A, adjoint(D))
+@deprecate A_rdiv_Bt!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}  rdiv!(A, transpose(D))
+@deprecate A_rdiv_B!(A::SparseMatrixCSC{T}, D::Diagonal{T}) where {T}   rdiv!(A, D)
+@deprecate A_ldiv_B!(L::LowerTriangular{T,<:SparseMatrixCSCUnion{T}}, B::StridedVecOrMat) where {T}     ldiv!(L, B)
+@deprecate A_ldiv_B!(U::UpperTriangular{T,<:SparseMatrixCSCUnion{T}}, B::StridedVecOrMat) where {T}     ldiv!(U, B)
+@deprecate A_mul_Bt(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(A, transpose(B))
+@deprecate A_mul_Bc(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(A, adjoint(B))
+@deprecate At_mul_B(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(transpose(A), B)
+@deprecate Ac_mul_B(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}     (*)(adjoint(A), B)
+@deprecate At_mul_Bt(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}    (*)(transpose(A), transpose(B))
+@deprecate Ac_mul_Bc(A::SparseMatrixCSC{Tv,Ti}, B::SparseMatrixCSC{Tv,Ti}) where {Tv,Ti}    (*)(adjoint(A), adjoint(B))
+@deprecate A_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)     mul!(C, A, B)
+@deprecate Ac_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)    mul!(C, adjoint(A), B)
+@deprecate At_mul_B!(C::StridedVecOrMat, A::SparseMatrixCSC, B::StridedVecOrMat)    mul!(C, transpose(A), B)
+@deprecate A_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)   mul!(α, A, B, β, C)
+@deprecate A_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}  (*)(A, x)
+@deprecate A_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}  (*)(A, B)
+@deprecate Ac_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)  mul!(α, adjoint(A), B, β, C)
+@deprecate Ac_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}     (*)(adjoint(A), x)
+@deprecate Ac_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}     (*)(adjoint(A), B)
+@deprecate At_mul_B!(α::Number, A::SparseMatrixCSC, B::StridedVecOrMat, β::Number, C::StridedVecOrMat)  mul!(α, transpose(A), B, β, C)
+@deprecate At_mul_B(A::SparseMatrixCSC{TA,S}, x::StridedVector{Tx}) where {TA,S,Tx}     (*)(transpose(A), x)
+@deprecate At_mul_B(A::SparseMatrixCSC{TA,S}, B::StridedMatrix{Tx}) where {TA,S,Tx}     (*)(transpose(A), B)
+@deprecate A_mul_Bt(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(A, transpose(B))
+@deprecate A_mul_Bc(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(A, adjoint(B))
+@deprecate At_mul_B(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(transpose(A), B)
+@deprecate Ac_mul_B(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}   (*)(adjoint(A),B)
+@deprecate At_mul_Bt(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}  (*)(transpose(A), transpose(B))
+@deprecate Ac_mul_Bc(A::SparseMatrixCSC{TvA,TiA}, B::SparseMatrixCSC{TvB,TiB}) where {TvA,TiA,TvB,TiB}  (*)(adjoint(A), adjoint(B))
+
+# A[ct]_(mul|ldiv|rdiv)_B[ct][!] methods from base/sparse/sparsevector.jl, to deprecate
+for isunittri in (true, false), islowertri in (true, false)
+    unitstr = isunittri ? "Unit" : ""
+    halfstr = islowertri ? "Lower" : "Upper"
+    tritype = :(Base.LinAlg.$(Symbol(unitstr, halfstr, "Triangular")))
+    @eval #=Base.SparseArrays=# begin
+        using Base.LinAlg: Adjoint, Transpose
+        @deprecate At_ldiv_B(A::$tritype{TA,<:AbstractMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}   (\)(transpose(A), b)
+        @deprecate At_ldiv_B(A::$tritype{TA,<:StridedMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}    (\)(transpose(A), b)
+        @deprecate At_ldiv_B(A::$tritype, b::SparseVector)  (\)(transpose(A), b)
+        @deprecate Ac_ldiv_B(A::$tritype{TA,<:AbstractMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}   (\)(adjoint(A), b)
+        @deprecate Ac_ldiv_B(A::$tritype{TA,<:StridedMatrix}, b::SparseVector{Tb}) where {TA<:Number,Tb<:Number}    (\)(adjoint(A), b)
+        @deprecate Ac_ldiv_B(A::$tritype, b::SparseVector)  (\)(adjoint(A), b)
+        @deprecate A_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)   ldiv!(A, b)
+        @deprecate At_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)  ldiv!(transpose(A), b)
+        @deprecate Ac_ldiv_B!(A::$tritype{<:Any,<:StridedMatrix}, b::SparseVector)  ldiv!(adjoint(A), b)
+    end
+end
+
+using Base.LinAlg: Adjoint, Transpose
+@deprecate Ac_mul_B(A::SparseMatrixCSC, x::AbstractSparseVector)    (*)(adjoint(A), x)
+@deprecate At_mul_B(A::SparseMatrixCSC, x::AbstractSparseVector)    (*)(transpose(A), x)
+@deprecate Ac_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)   mul!(α, adjoint(A), x, β, y)
+@deprecate Ac_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}   mul!(y, adjoint(A), x)
+@deprecate At_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)   mul!(α, transpose(A), x, β, y)
+@deprecate At_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}   mul!(y, transpose(A), x)
+@deprecate A_mul_B!(α::Number, A::SparseMatrixCSC, x::AbstractSparseVector, β::Number, y::StridedVector)    mul!(α, A, x, β, y)
+@deprecate A_mul_B!(y::StridedVector{Ty}, A::SparseMatrixCSC, x::AbstractSparseVector{Tx}) where {Tx,Ty}    mul!(y, A, x)
+@deprecate At_mul_B!(α::Number, A::StridedMatrix, x::AbstractSparseVector, β::Number, y::StridedVector)     mul!(α, transpose(A), x, β, y)
+@deprecate At_mul_B!(y::StridedVector{Ty}, A::StridedMatrix, x::AbstractSparseVector{Tx}) where {Tx,Ty}     mul!(y, transpose(A), x)
+@deprecate At_mul_B(A::StridedMatrix{Ta}, x::AbstractSparseVector{Tx}) where {Ta,Tx}    (*)(transpose(A), x)
+@deprecate A_mul_B!(α::Number, A::StridedMatrix, x::AbstractSparseVector, β::Number, y::StridedVector)  mul!(α, A, x, β, y)
+@deprecate A_mul_B!(y::StridedVector{Ty}, A::StridedMatrix, x::AbstractSparseVector{Tx}) where {Tx,Ty}  mul!(y, A, x)
+
+# methods involving RowVector from base/sparse/linalg.jl, to deprecate
+\(::SparseMatrixCSC, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
+\(::Adjoint{<:Any,<:SparseMatrixCSC}, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
+\(::Transpose{<:Any,<:SparseMatrixCSC}, ::RowVector) = throw(DimensionMismatch("Cannot left-divide matrix by transposed vector"))
+
+# methods involving RowVector from base/sparse/higherorderfns.jl, to deprecate
+@eval SparseArrays.HigherOrderFns begin
+    BroadcastStyle(::Type{<:Base.RowVector{T,<:Vector}}) where T = Broadcast.MatrixStyle()
+end
+
+import Base: asyncmap
+@deprecate asyncmap(f, s::AbstractSparseArray...; kwargs...) sparse(asyncmap(f, map(Array, s)...; kwargs...))
+
+
+# END 0.7 deprecations
+
+# BEGIN 1.0 deprecations
+
+# END 1.0 deprecations
diff --git a/base/sparse/higherorderfns.jl b/stdlib/SparseArrays/src/higherorderfns.jl
similarity index 100%
rename from base/sparse/higherorderfns.jl
rename to stdlib/SparseArrays/src/higherorderfns.jl
diff --git a/base/sparse/linalg.jl b/stdlib/SparseArrays/src/linalg.jl
similarity index 100%
rename from base/sparse/linalg.jl
rename to stdlib/SparseArrays/src/linalg.jl
diff --git a/base/sparse/sparsematrix.jl b/stdlib/SparseArrays/src/sparsematrix.jl
similarity index 100%
rename from base/sparse/sparsematrix.jl
rename to stdlib/SparseArrays/src/sparsematrix.jl
diff --git a/base/sparse/sparsevector.jl b/stdlib/SparseArrays/src/sparsevector.jl
similarity index 99%
rename from base/sparse/sparsevector.jl
rename to stdlib/SparseArrays/src/sparsevector.jl
index 26c3288bef47e3..5008f0f8c70b13 100644
--- a/base/sparse/sparsevector.jl
+++ b/stdlib/SparseArrays/src/sparsevector.jl
@@ -896,7 +896,7 @@ vec(x::AbstractSparseVector) = x
 copy(x::AbstractSparseVector) =
     SparseVector(length(x), copy(nonzeroinds(x)), copy(nonzeros(x)))
 
-function reinterpret(::Type{T}, x::AbstractSparseVector{Tv}) where {T,Tv}
+function Base.reinterpret(::Type{T}, x::AbstractSparseVector{Tv}) where {T,Tv}
     sizeof(T) == sizeof(Tv) ||
         throw(ArgumentError("reinterpret of sparse vectors only supports element types of the same size."))
     SparseVector(length(x), copy(nonzeroinds(x)), reinterpret(T, nonzeros(x)))
diff --git a/test/sparse/higherorderfns.jl b/stdlib/SparseArrays/test/higherorderfns.jl
similarity index 100%
rename from test/sparse/higherorderfns.jl
rename to stdlib/SparseArrays/test/higherorderfns.jl
diff --git a/stdlib/SparseArrays/test/runtests.jl b/stdlib/SparseArrays/test/runtests.jl
new file mode 100644
index 00000000000000..e761a2fd34567c
--- /dev/null
+++ b/stdlib/SparseArrays/test/runtests.jl
@@ -0,0 +1,7 @@
+# This file is a part of Julia. License is MIT: https://julialang.org/license
+
+using Test, SparseArrays
+
+include("higherorderfns.jl")
+include("sparse.jl")
+include("sparsevector.jl")
diff --git a/test/sparse/sparse.jl b/stdlib/SparseArrays/test/sparse.jl
similarity index 90%
rename from test/sparse/sparse.jl
rename to stdlib/SparseArrays/test/sparse.jl
index 00bc183992f7d5..5de30ff9798a62 100644
--- a/test/sparse/sparse.jl
+++ b/stdlib/SparseArrays/test/sparse.jl
@@ -23,7 +23,7 @@ end
 end
 
 @testset "indtype" begin
-    @test Base.SparseArrays.indtype(sparse(Int8[1,1],Int8[1,1],[1,1])) == Int8
+    @test SparseArrays.indtype(sparse(Int8[1,1],Int8[1,1],[1,1])) == Int8
 end
 
 @testset "sparse matrix construction" begin
@@ -303,8 +303,8 @@ end
         a = sprand(10, 5, 0.7)
         b = sprand(5, 15, 0.3)
         @test maximum(abs.(a*b - Array(a)*Array(b))) < 100*eps()
-        @test maximum(abs.(Base.SparseArrays.spmatmul(a,b,sortindices=:sortcols) - Array(a)*Array(b))) < 100*eps()
-        @test maximum(abs.(Base.SparseArrays.spmatmul(a,b,sortindices=:doubletranspose) - Array(a)*Array(b))) < 100*eps()
+        @test maximum(abs.(SparseArrays.spmatmul(a,b,sortindices=:sortcols) - Array(a)*Array(b))) < 100*eps()
+        @test maximum(abs.(SparseArrays.spmatmul(a,b,sortindices=:doubletranspose) - Array(a)*Array(b))) < 100*eps()
         @test Array(kron(a,b)) == kron(Array(a), Array(b))
         @test Array(kron(Array(a),b)) == kron(Array(a), Array(b))
         @test Array(kron(a,Array(b))) == kron(Array(a), Array(b))
@@ -929,47 +929,47 @@ end
     @test nnz(A) == 19
 
     # Test argument bounds checking for dropstored!(A, i, j)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 0, 1)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 1, 0)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 1, 11)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 11, 1)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 0, 1)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 1, 0)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 1, 11)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 11, 1)
 
     # Test argument bounds checking for dropstored!(A, I, J)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 0:1, 1:1)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 1:1, 0:1)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 10:11, 1:1)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(A, 1:1, 10:11)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 0:1, 1:1)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 1:1, 0:1)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 10:11, 1:1)
+    @test_throws BoundsError SparseArrays.dropstored!(A, 1:1, 10:11)
 
     # Test behavior of dropstored!(A, i, j)
     # --> Test dropping a single stored entry
-    Base.SparseArrays.dropstored!(A, 1, 2)
+    SparseArrays.dropstored!(A, 1, 2)
     @test nnz(A) == 18
     # --> Test dropping a single nonstored entry
-    Base.SparseArrays.dropstored!(A, 2, 1)
+    SparseArrays.dropstored!(A, 2, 1)
     @test nnz(A) == 18
 
     # Test behavior of dropstored!(A, I, J) and derivs.
     # --> Test dropping a single row including stored and nonstored entries
-    Base.SparseArrays.dropstored!(A, 1, :)
+    SparseArrays.dropstored!(A, 1, :)
     @test nnz(A) == 9
     # --> Test dropping a single column including stored and nonstored entries
-    Base.SparseArrays.dropstored!(A, :, 2)
+    SparseArrays.dropstored!(A, :, 2)
     @test nnz(A) == 0
     # --> Introduce nonzeros in rows one and two and columns two and three
     A[1:2,:] = 1
     A[:,2:3] = 2
     @test nnz(A) == 36
     # --> Test dropping multiple rows containing stored and nonstored entries
-    Base.SparseArrays.dropstored!(A, 1:3, :)
+    SparseArrays.dropstored!(A, 1:3, :)
     @test nnz(A) == 14
     # --> Test dropping multiple columns containing stored and nonstored entries
-    Base.SparseArrays.dropstored!(A, :, 2:4)
+    SparseArrays.dropstored!(A, :, 2:4)
     @test nnz(A) == 0
     # --> Introduce nonzeros in every other row
     A[1:2:9, :] = 1
     @test nnz(A) == 50
     # --> Test dropping a block of the matrix towards the upper left
-    Base.SparseArrays.dropstored!(A, 2:5, 2:5)
+    SparseArrays.dropstored!(A, 2:5, 2:5)
     @test nnz(A) == 42
 end
 
@@ -1048,110 +1048,112 @@ end
     @test iA === iS === nothing
 end
 
-# findmin/findmax/minumum/maximum
+@testset "findmin/findmax/minumum/maximum" begin
+    A = sparse([1.0 5.0 6.0;
+                5.0 2.0 4.0])
+    for (tup, rval, rind) in [((1,), [1.0 2.0 4.0], [CartesianIndex(1,1) CartesianIndex(2,2) CartesianIndex(2,3)]),
+                              ((2,), reshape([1.0,2.0], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,2)], 2, 1)),
+                              ((1,2), fill(1.0,1,1),fill(CartesianIndex(1,1),1,1))]
+        @test findmin(A, tup) == (rval, rind)
+    end
 
-A = sparse([1.0 5.0 6.0;
-            5.0 2.0 4.0])
-for (tup, rval, rind) in [((1,), [1.0 2.0 4.0], [CartesianIndex(1,1) CartesianIndex(2,2) CartesianIndex(2,3)]),
-                          ((2,), reshape([1.0,2.0], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,2)], 2, 1)),
-                          ((1,2), fill(1.0,1,1),fill(CartesianIndex(1,1),1,1))]
-    @test findmin(A, tup) == (rval, rind)
-end
+    for (tup, rval, rind) in [((1,), [5.0 5.0 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
+                              ((2,), reshape([6.0,5.0], 2, 1), reshape([CartesianIndex(1,3),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(6.0,1,1),fill(CartesianIndex(1,3),1,1))]
+        @test findmax(A, tup) == (rval, rind)
+    end
 
-for (tup, rval, rind) in [((1,), [5.0 5.0 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
-                          ((2,), reshape([6.0,5.0], 2, 1), reshape([CartesianIndex(1,3),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(6.0,1,1),fill(CartesianIndex(1,3),1,1))]
-    @test findmax(A, tup) == (rval, rind)
-end
+    #issue 23209
 
-#issue 23209
+    A = sparse([1.0 5.0 6.0;
+                NaN 2.0 4.0])
+    for (tup, rval, rind) in [((1,), [NaN 2.0 4.0], [CartesianIndex(2,1) CartesianIndex(2,2) CartesianIndex(2,3)]),
+                              ((2,), reshape([1.0, NaN], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([1.0 5.0 6.0;
-            NaN 2.0 4.0])
-for (tup, rval, rind) in [((1,), [NaN 2.0 4.0], [CartesianIndex(2,1) CartesianIndex(2,2) CartesianIndex(2,3)]),
-                          ((2,), reshape([1.0, NaN], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((1,), [NaN 5.0 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
+                              ((2,), reshape([6.0, NaN], 2, 1), reshape([CartesianIndex(1,3),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((1,), [NaN 5.0 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
-                          ((2,), reshape([6.0, NaN], 2, 1), reshape([CartesianIndex(1,3),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
-    @test isequal(findmax(A, tup), (rval, rind))
-end
+    A = sparse([1.0 NaN 6.0;
+                NaN 2.0 4.0])
+    for (tup, rval, rind) in [((1,), [NaN NaN 4.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(2,3)]),
+                              ((2,), reshape([NaN, NaN], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([1.0 NaN 6.0;
-            NaN 2.0 4.0])
-for (tup, rval, rind) in [((1,), [NaN NaN 4.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(2,3)]),
-                          ((2,), reshape([NaN, NaN], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((1,), [NaN NaN 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
+                              ((2,), reshape([NaN, NaN], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((1,), [NaN NaN 6.0], [CartesianIndex(2,1) CartesianIndex(1,2) CartesianIndex(1,3)]),
-                          ((2,), reshape([NaN, NaN], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(NaN,1,1),fill(CartesianIndex(2,1),1,1))]
-    @test isequal(findmax(A, tup), (rval, rind))
-end
+    A = sparse([Inf -Inf Inf  -Inf;
+                Inf  Inf -Inf -Inf])
+    for (tup, rval, rind) in [((1,), [Inf -Inf -Inf -Inf], [CartesianIndex(1,1) CartesianIndex(1,2) CartesianIndex(2,3) CartesianIndex(1,4)]),
+                              ((2,), reshape([-Inf -Inf], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,3)], 2, 1)),
+                              ((1,2), fill(-Inf,1,1),fill(CartesianIndex(1,2),1,1))]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([Inf -Inf Inf  -Inf;
-            Inf  Inf -Inf -Inf])
-for (tup, rval, rind) in [((1,), [Inf -Inf -Inf -Inf], [CartesianIndex(1,1) CartesianIndex(1,2) CartesianIndex(2,3) CartesianIndex(1,4)]),
-                          ((2,), reshape([-Inf -Inf], 2, 1), reshape([CartesianIndex(1,2),CartesianIndex(2,3)], 2, 1)),
-                          ((1,2), fill(-Inf,1,1),fill(CartesianIndex(1,2),1,1))]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((1,), [Inf Inf Inf -Inf], [CartesianIndex(1,1) CartesianIndex(2,2) CartesianIndex(1,3) CartesianIndex(1,4)]),
+                              ((2,), reshape([Inf Inf], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,1)], 2, 1)),
+                              ((1,2), fill(Inf,1,1),fill(CartesianIndex(1,1),1,1))]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((1,), [Inf Inf Inf -Inf], [CartesianIndex(1,1) CartesianIndex(2,2) CartesianIndex(1,3) CartesianIndex(1,4)]),
-                          ((2,), reshape([Inf Inf], 2, 1), reshape([CartesianIndex(1,1),CartesianIndex(2,1)], 2, 1)),
-                          ((1,2), fill(Inf,1,1),fill(CartesianIndex(1,1),1,1))]
-    @test isequal(findmax(A, tup), (rval, rind))
-end
+    A = sparse([BigInt(10)])
+    for (tup, rval, rind) in [((2,), [BigInt(10)], [1])]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([BigInt(10)])
-for (tup, rval, rind) in [((2,), [BigInt(10)], [1])]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((2,), [BigInt(10)], [1])]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((2,), [BigInt(10)], [1])]
-    @test isequal(findmax(A, tup), (rval, rind))
-end
+    A = sparse([BigInt(-10)])
+    for (tup, rval, rind) in [((2,), [BigInt(-10)], [1])]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([BigInt(-10)])
-for (tup, rval, rind) in [((2,), [BigInt(-10)], [1])]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((2,), [BigInt(-10)], [1])]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((2,), [BigInt(-10)], [1])]
-    @test isequal(findmax(A, tup), (rval, rind))
-end
+    A = sparse([BigInt(10) BigInt(-10)])
+    for (tup, rval, rind) in [((2,), reshape([BigInt(-10)], 1, 1), reshape([CartesianIndex(1,2)], 1, 1))]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-A = sparse([BigInt(10) BigInt(-10)])
-for (tup, rval, rind) in [((2,), reshape([BigInt(-10)], 1, 1), reshape([CartesianIndex(1,2)], 1, 1))]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((2,), reshape([BigInt(10)], 1, 1), reshape([CartesianIndex(1,1)], 1, 1))]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((2,), reshape([BigInt(10)], 1, 1), reshape([CartesianIndex(1,1)], 1, 1))]
-    @test isequal(findmax(A, tup), (rval, rind))
+    A = sparse(["a", "b"])
+    @test_throws MethodError findmin(A, 1)
 end
 
-A = sparse(["a", "b"])
-@test_throws MethodError findmin(A, 1)
-
 # Support the case when user defined `zero` and `isless` for non-numerical type
 struct CustomType
     x::String
 end
 Base.zero(::Type{CustomType}) = CustomType("")
 Base.isless(x::CustomType, y::CustomType) = isless(x.x, y.x)
-A = sparse([CustomType("a"), CustomType("b")])
+@testset "findmin/findmax for non-numerical type" begin
+    A = sparse([CustomType("a"), CustomType("b")])
 
-for (tup, rval, rind) in [((1,), [CustomType("a")], [1])]
-    @test isequal(findmin(A, tup), (rval, rind))
-end
+    for (tup, rval, rind) in [((1,), [CustomType("a")], [1])]
+        @test isequal(findmin(A, tup), (rval, rind))
+    end
 
-for (tup, rval, rind) in [((1,), [CustomType("b")], [2])]
-    @test isequal(findmax(A, tup), (rval, rind))
+    for (tup, rval, rind) in [((1,), [CustomType("b")], [2])]
+        @test isequal(findmax(A, tup), (rval, rind))
+    end
 end
 
 @testset "findn" begin
@@ -1190,9 +1192,9 @@ function test_getindex_algs(A::SparseMatrixCSC{Tv,Ti}, I::AbstractVector, J::Abs
         ((minj < 1) || (maxj > n)) && BoundsError()
     end
 
-    (alg == 0) ? Base.SparseArrays.getindex_I_sorted_bsearch_A(A, I, J) :
-    (alg == 1) ? Base.SparseArrays.getindex_I_sorted_bsearch_I(A, I, J) :
-    Base.SparseArrays.getindex_I_sorted_linear(A, I, J)
+    (alg == 0) ? SparseArrays.getindex_I_sorted_bsearch_A(A, I, J) :
+    (alg == 1) ? SparseArrays.getindex_I_sorted_bsearch_I(A, I, J) :
+    SparseArrays.getindex_I_sorted_linear(A, I, J)
 end
 
 @testset "test_getindex_algs" begin
@@ -1396,8 +1398,8 @@ end
     local A = guardsrand(1234321) do
         triu(sprand(10, 10, 0.2))
     end
-    @test Base.droptol!(A, 0.01).colptr == [1,1,1,2,2,3,4,6,6,7,9]
-    @test isequal(Base.droptol!(sparse([1], [1], [1]), 1), SparseMatrixCSC(1, 1, Int[1, 1], Int[], Int[]))
+    @test SparseArrays.droptol!(A, 0.01).colptr == [1,1,1,2,2,3,4,6,6,7,9]
+    @test isequal(SparseArrays.droptol!(sparse([1], [1], [1]), 1), SparseMatrixCSC(1, 1, Int[1, 1], Int[], Int[]))
 end
 
 @testset "dropzeros[!]" begin
@@ -1487,10 +1489,10 @@ end
 
 @testset "expandptr" begin
     local A = sparse(1.0I, 5, 5)
-    @test Base.SparseArrays.expandptr(A.colptr) == 1:5
+    @test SparseArrays.expandptr(A.colptr) == 1:5
     A[1,2] = 1
-    @test Base.SparseArrays.expandptr(A.colptr) == [1; 2; 2; 3; 4; 5]
-    @test_throws ArgumentError Base.SparseArrays.expandptr([2; 3])
+    @test SparseArrays.expandptr(A.colptr) == [1; 2; 2; 3; 4; 5]
+    @test_throws ArgumentError SparseArrays.expandptr([2; 3])
 end
 
 @testset "triu/tril" begin
@@ -1697,13 +1699,13 @@ end
     Ari = ceil.(Int64, 100*Ar)
     if Base.USE_GPL_LIBS
         # NOTE: normestinv is probabilistic, so requires a fixed seed (set above in srand(1234))
-        @test Base.SparseArrays.normestinv(Ac,3) ≈ norm(inv(Array(Ac)),1) atol=1e-4
-        @test Base.SparseArrays.normestinv(Aci,3) ≈ norm(inv(Array(Aci)),1) atol=1e-4
-        @test Base.SparseArrays.normestinv(Ar) ≈ norm(inv(Array(Ar)),1) atol=1e-4
-        @test_throws ArgumentError Base.SparseArrays.normestinv(Ac,0)
-        @test_throws ArgumentError Base.SparseArrays.normestinv(Ac,21)
+        @test SparseArrays.normestinv(Ac,3) ≈ norm(inv(Array(Ac)),1) atol=1e-4
+        @test SparseArrays.normestinv(Aci,3) ≈ norm(inv(Array(Aci)),1) atol=1e-4
+        @test SparseArrays.normestinv(Ar) ≈ norm(inv(Array(Ar)),1) atol=1e-4
+        @test_throws ArgumentError SparseArrays.normestinv(Ac,0)
+        @test_throws ArgumentError SparseArrays.normestinv(Ac,21)
     end
-    @test_throws DimensionMismatch Base.SparseArrays.normestinv(sprand(3,5,.9))
+    @test_throws DimensionMismatch SparseArrays.normestinv(sprand(3,5,.9))
 end
 
 @testset "issue #13008" begin
@@ -1852,7 +1854,7 @@ end
 # Test temporary fix for issue #16548 in PR #16979. Somewhat brittle. Expect to remove with `\` revisions.
 @testset "issue #16548" begin
     ms = methods(\, (SparseMatrixCSC, AbstractVecOrMat)).ms
-    @test all(m -> m.module == Base.SparseArrays, ms)
+    @test all(m -> m.module == SparseArrays, ms)
 end
 
 @testset "row indexing a SparseMatrixCSC with non-Int integer type" begin
@@ -1912,7 +1914,7 @@ end
 
 # Check calling of unary minus method specialized for SparseMatrixCSCs
 @testset "issue #19503" begin
-    @test which(-, (SparseMatrixCSC,)).module == Base.SparseArrays
+    @test which(-, (SparseMatrixCSC,)).module == SparseArrays
 end
 
 @testset "issue #14398" begin
@@ -1921,16 +1923,16 @@ end
 
 @testset "dropstored issue #20513" begin
     x = sparse(rand(3,3))
-    Base.SparseArrays.dropstored!(x, 1, 1)
+    SparseArrays.dropstored!(x, 1, 1)
     @test x[1, 1] == 0.0
     @test x.colptr == [1, 3, 6, 9]
-    Base.SparseArrays.dropstored!(x, 2, 1)
+    SparseArrays.dropstored!(x, 2, 1)
     @test x.colptr == [1, 2, 5, 8]
     @test x[2, 1] == 0.0
-    Base.SparseArrays.dropstored!(x, 2, 2)
+    SparseArrays.dropstored!(x, 2, 2)
     @test x.colptr == [1, 2, 4, 7]
     @test x[2, 2] == 0.0
-    Base.SparseArrays.dropstored!(x, 2, 3)
+    SparseArrays.dropstored!(x, 2, 3)
     @test x.colptr == [1, 2, 4, 6]
     @test x[2, 3] == 0.0
 end
@@ -1949,47 +1951,47 @@ end
 @testset "show" begin
     io = IOBuffer()
     show(io, MIME"text/plain"(), sparse(Int64[1], Int64[1], [1.0]))
-    @test String(take!(io)) == "1×1 SparseMatrixCSC{Float64,Int64} with 1 stored entry:\n  [1, 1]  =  1.0"
+    @test String(take!(io)) == "1×1 SparseArrays.SparseMatrixCSC{Float64,Int64} with 1 stored entry:\n  [1, 1]  =  1.0"
     show(io, MIME"text/plain"(), spzeros(Float32, Int64, 2, 2))
-    @test String(take!(io)) == "2×2 SparseMatrixCSC{Float32,Int64} with 0 stored entries"
+    @test String(take!(io)) == "2×2 SparseArrays.SparseMatrixCSC{Float32,Int64} with 0 stored entries"
 
     ioc = IOContext(io, :displaysize => (5, 80), :limit => true)
     show(ioc, MIME"text/plain"(), sparse(Int64[1], Int64[1], [1.0]))
-    @test String(take!(io)) == "1×1 SparseMatrixCSC{Float64,Int64} with 1 stored entry:\n  [1, 1]  =  1.0"
+    @test String(take!(io)) == "1×1 SparseArrays.SparseMatrixCSC{Float64,Int64} with 1 stored entry:\n  [1, 1]  =  1.0"
     show(ioc, MIME"text/plain"(), sparse(Int64[1, 1], Int64[1, 2], [1.0, 2.0]))
-    @test String(take!(io)) == "1×2 SparseMatrixCSC{Float64,Int64} with 2 stored entries:\n  ⋮"
+    @test String(take!(io)) == "1×2 SparseArrays.SparseMatrixCSC{Float64,Int64} with 2 stored entries:\n  ⋮"
 
     # even number of rows
     ioc = IOContext(io, :displaysize => (8, 80), :limit => true)
     show(ioc, MIME"text/plain"(), sparse(Int64[1,2,3,4], Int64[1,1,2,2], [1.0,2.0,3.0,4.0]))
-    @test String(take!(io)) == string("4×2 SparseMatrixCSC{Float64,Int64} with 4 stored entries:\n  [1, 1]",
+    @test String(take!(io)) == string("4×2 SparseArrays.SparseMatrixCSC{Float64,Int64} with 4 stored entries:\n  [1, 1]",
                                       "  =  1.0\n  [2, 1]  =  2.0\n  [3, 2]  =  3.0\n  [4, 2]  =  4.0")
 
     show(ioc, MIME"text/plain"(), sparse(Int64[1,2,3,4,5], Int64[1,1,2,2,3], [1.0,2.0,3.0,4.0,5.0]))
-    @test String(take!(io)) ==  string("5×3 SparseMatrixCSC{Float64,Int64} with 5 stored entries:\n  [1, 1]",
+    @test String(take!(io)) ==  string("5×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 5 stored entries:\n  [1, 1]",
                                        "  =  1.0\n  ⋮\n  [5, 3]  =  5.0")
 
     show(ioc, MIME"text/plain"(), sparse(fill(1.,5,3)))
-    @test String(take!(io)) ==  string("5×3 SparseMatrixCSC{Float64,$Int} with 15 stored entries:\n  [1, 1]",
+    @test String(take!(io)) ==  string("5×3 SparseArrays.SparseMatrixCSC{Float64,$Int} with 15 stored entries:\n  [1, 1]",
                                        "  =  1.0\n  ⋮\n  [5, 3]  =  1.0")
 
     # odd number of rows
     ioc = IOContext(io, :displaysize => (9, 80), :limit => true)
     show(ioc, MIME"text/plain"(), sparse(Int64[1,2,3,4,5], Int64[1,1,2,2,3], [1.0,2.0,3.0,4.0,5.0]))
-    @test String(take!(io)) == string("5×3 SparseMatrixCSC{Float64,Int64} with 5 stored entries:\n  [1, 1]",
+    @test String(take!(io)) == string("5×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 5 stored entries:\n  [1, 1]",
                                       "  =  1.0\n  [2, 1]  =  2.0\n  [3, 2]  =  3.0\n  [4, 2]  =  4.0\n  [5, 3]  =  5.0")
 
     show(ioc, MIME"text/plain"(), sparse(Int64[1,2,3,4,5,6], Int64[1,1,2,2,3,3], [1.0,2.0,3.0,4.0,5.0,6.0]))
-    @test String(take!(io)) ==  string("6×3 SparseMatrixCSC{Float64,Int64} with 6 stored entries:\n  [1, 1]",
+    @test String(take!(io)) ==  string("6×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 6 stored entries:\n  [1, 1]",
                                        "  =  1.0\n  [2, 1]  =  2.0\n  ⋮\n  [5, 3]  =  5.0\n  [6, 3]  =  6.0")
 
     show(ioc, MIME"text/plain"(), sparse(fill(1.,6,3)))
-    @test String(take!(io)) ==  string("6×3 SparseMatrixCSC{Float64,$Int} with 18 stored entries:\n  [1, 1]",
+    @test String(take!(io)) ==  string("6×3 SparseArrays.SparseMatrixCSC{Float64,$Int} with 18 stored entries:\n  [1, 1]",
                                        "  =  1.0\n  [2, 1]  =  1.0\n  ⋮\n  [5, 3]  =  1.0\n  [6, 3]  =  1.0")
 
     ioc = IOContext(io, :displaysize => (9, 80))
     show(ioc, MIME"text/plain"(), sparse(Int64[1,2,3,4,5,6], Int64[1,1,2,2,3,3], [1.0,2.0,3.0,4.0,5.0,6.0]))
-    @test String(take!(io)) ==  string("6×3 SparseMatrixCSC{Float64,Int64} with 6 stored entries:\n  [1, 1]  =  1.0\n",
+    @test String(take!(io)) ==  string("6×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 6 stored entries:\n  [1, 1]  =  1.0\n",
         "  [2, 1]  =  2.0\n  [3, 2]  =  3.0\n  [4, 2]  =  4.0\n  [5, 3]  =  5.0\n  [6, 3]  =  6.0")
 end
 
@@ -2079,7 +2081,7 @@ end
     a = sparse(rand(3,3) .+ 0.1)
     b = similar(a, Float32, Int32)
     c = similar(b, Float32, Int32)
-    Base.SparseArrays.dropstored!(b, 1, 1)
+    SparseArrays.dropstored!(b, 1, 1)
     @test length(c.rowval) == 9
     @test length(c.nzval) == 9
 end
diff --git a/test/sparse/sparsevector.jl b/stdlib/SparseArrays/test/sparsevector.jl
similarity index 98%
rename from test/sparse/sparsevector.jl
rename to stdlib/SparseArrays/test/sparsevector.jl
index 721bcf6f6f54d2..5558e466e2c90d 100644
--- a/test/sparse/sparsevector.jl
+++ b/stdlib/SparseArrays/test/sparsevector.jl
@@ -260,13 +260,13 @@ end
 @testset "dropstored!" begin
     x = SparseVector(10, [2, 7, 9], [2.0, 7.0, 9.0])
     # Test argument bounds checking for dropstored!(x, i)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(x, 0)
-    @test_throws BoundsError Base.SparseArrays.dropstored!(x, 11)
+    @test_throws BoundsError SparseArrays.dropstored!(x, 0)
+    @test_throws BoundsError SparseArrays.dropstored!(x, 11)
     # Test behavior of dropstored!(x, i)
     # --> Test dropping a single stored entry
-    @test Base.SparseArrays.dropstored!(x, 2) == SparseVector(10, [7, 9], [7.0, 9.0])
+    @test SparseArrays.dropstored!(x, 2) == SparseVector(10, [7, 9], [7.0, 9.0])
     # --> Test dropping a single nonstored entry
-    @test Base.SparseArrays.dropstored!(x, 5) == SparseVector(10, [7, 9], [7.0, 9.0])
+    @test SparseArrays.dropstored!(x, 5) == SparseVector(10, [7, 9], [7.0, 9.0])
 end
 
 @testset "find and findnz" begin
@@ -680,7 +680,7 @@ end
             @test spresvec == op.(densevec)
             @test all(!iszero, spresvec.nzval)
             resvaltype = typeof(op(zero(eltype(spvec))))
-            resindtype = Base.SparseArrays.indtype(spvec)
+            resindtype = SparseArrays.indtype(spvec)
             @test isa(spresvec, SparseVector{resvaltype,resindtype})
         end
     end
@@ -696,7 +696,7 @@ end
         spresvec = op.(spvec)
         @test spresvec == op.(densevec)
         resvaltype = typeof(op(zero(eltype(spvec))))
-        resindtype = Base.SparseArrays.indtype(spvec)
+        resindtype = SparseArrays.indtype(spvec)
         @test isa(spresvec, SparseVector{resvaltype,resindtype})
     end
 end
@@ -1020,17 +1020,17 @@ end
 @testset "fkeep!" begin
     x = sparsevec(1:7, [3., 2., -1., 1., -2., -3., 3.], 7)
     # droptol
-    xdrop = Base.droptol!(copy(x), 1.5)
+    xdrop = SparseArrays.droptol!(copy(x), 1.5)
     @test exact_equal(xdrop, SparseVector(7, [1, 2, 5, 6, 7], [3., 2., -2., -3., 3.]))
-    Base.droptol!(xdrop, 2.5)
+    SparseArrays.droptol!(xdrop, 2.5)
     @test exact_equal(xdrop, SparseVector(7, [1, 6, 7], [3., -3., 3.]))
-    Base.droptol!(xdrop, 3.)
+    SparseArrays.droptol!(xdrop, 3.)
     @test exact_equal(xdrop, SparseVector(7, Int[], Float64[]))
 
     xdrop = copy(x)
     # This will keep index 1, 3, 4, 7 in xdrop
     f_drop(i, x) = (abs(x) == 1.) || (i in [1, 7])
-    Base.SparseArrays.fkeep!(xdrop, f_drop)
+    SparseArrays.fkeep!(xdrop, f_drop)
     @test exact_equal(xdrop, SparseVector(7, [1, 3, 4, 7], [3., -1., 1., 3.]))
 end
 @testset "dropzeros[!]" begin
@@ -1063,7 +1063,7 @@ end
         # original dropzeros! test
         xdrop = sparsevec(1:7, [3., 2., -1., 1., -2., -3., 3.], 7)
         xdrop.nzval[[2, 4, 6]] = 0.0
-        Base.SparseArrays.dropzeros!(xdrop)
+        SparseArrays.dropzeros!(xdrop)
         @test exact_equal(xdrop, SparseVector(7, [1, 3, 5, 7], [3, -1., -2., 3.]))
     end
 end
@@ -1161,9 +1161,9 @@ mutable struct t20488 end
 @testset "show" begin
     io = IOBuffer()
     show(io, MIME"text/plain"(), sparsevec(Int64[1], [1.0]))
-    @test String(take!(io)) == "1-element SparseVector{Float64,Int64} with 1 stored entry:\n  [1]  =  1.0"
+    @test String(take!(io)) == "1-element SparseArrays.SparseVector{Float64,Int64} with 1 stored entry:\n  [1]  =  1.0"
     show(io, MIME"text/plain"(),  spzeros(Float64, Int64, 2))
-    @test String(take!(io)) == "2-element SparseVector{Float64,Int64} with 0 stored entries"
+    @test String(take!(io)) == "2-element SparseArrays.SparseVector{Float64,Int64} with 0 stored entries"
     show(io, similar(sparsevec(rand(3) .+ 0.1), t20488))
     @test String(take!(io)) == "  [1]  =  #undef\n  [2]  =  #undef\n  [3]  =  #undef"
 end
diff --git a/stdlib/SuiteSparse/src/cholmod.jl b/stdlib/SuiteSparse/src/cholmod.jl
index 183010b62dc98f..8d65ceafc014e2 100644
--- a/stdlib/SuiteSparse/src/cholmod.jl
+++ b/stdlib/SuiteSparse/src/cholmod.jl
@@ -9,7 +9,7 @@ import Base.LinAlg: (\),
                  cholfact, cholfact!, det, diag, ishermitian, isposdef,
                  issuccess, issymmetric, ldltfact, ldltfact!, logdet
 
-using ..SparseArrays
+using SparseArrays
 using Base.Printf.@printf
 
 export
@@ -17,7 +17,7 @@ export
     Factor,
     Sparse
 
-import ..SparseArrays: AbstractSparseMatrix, SparseMatrixCSC, indtype, sparse, spzeros, nnz
+import SparseArrays: AbstractSparseMatrix, SparseMatrixCSC, indtype, sparse, spzeros, nnz
 
 import ..increment, ..increment!, ..decrement, ..decrement!
 
diff --git a/stdlib/SuiteSparse/src/spqr.jl b/stdlib/SuiteSparse/src/spqr.jl
index e844a66a9688e3..acd3d37bbd2c7d 100644
--- a/stdlib/SuiteSparse/src/spqr.jl
+++ b/stdlib/SuiteSparse/src/spqr.jl
@@ -21,7 +21,7 @@ const ORDERING_BESTAMD = Int32(9) # try COLAMD and AMD; pick best#
 # tried.  If there is a high fill-in with AMD then try METIS(A'A) and take
 # the best of AMD and METIS. METIS is not tried if it isn't installed.
 
-using ..SparseArrays: SparseMatrixCSC
+using SparseArrays: SparseMatrixCSC
 using ..SuiteSparse.CHOLMOD
 using ..SuiteSparse.CHOLMOD: change_stype!, free!
 
diff --git a/stdlib/SuiteSparse/src/umfpack.jl b/stdlib/SuiteSparse/src/umfpack.jl
index 3647445da3098f..371f1dc5d91d7b 100644
--- a/stdlib/SuiteSparse/src/umfpack.jl
+++ b/stdlib/SuiteSparse/src/umfpack.jl
@@ -7,8 +7,8 @@ export UmfpackLU
 import Base: (\), findnz, getproperty, show, size
 import Base.LinAlg: Factorization, det, lufact, ldiv!
 
-using ..SparseArrays
-import ..SparseArrays: nnz
+using SparseArrays
+import SparseArrays: nnz
 
 import ..increment, ..increment!, ..decrement, ..decrement!
 
diff --git a/stdlib/SuiteSparse/test/runtests.jl b/stdlib/SuiteSparse/test/runtests.jl
index f27f76068991a7..4cf807452f474d 100644
--- a/stdlib/SuiteSparse/test/runtests.jl
+++ b/stdlib/SuiteSparse/test/runtests.jl
@@ -1,7 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 using Test
-using SuiteSparse
+using SuiteSparse, SparseArrays
 
 if Base.USE_GPL_LIBS
    include("umfpack.jl")
diff --git a/test/abstractarray.jl b/test/abstractarray.jl
index 0fc998af8b13cf..9a6b4c1fd65e68 100644
--- a/test/abstractarray.jl
+++ b/test/abstractarray.jl
@@ -1,5 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
+using SparseArrays
+
 A = rand(5,4,3)
 @testset "Bounds checking" begin
     @test checkbounds(Bool, A, 1, 1, 1) == true
diff --git a/test/ambiguous.jl b/test/ambiguous.jl
index b9532891ca5588..d47d0082e5c03f 100644
--- a/test/ambiguous.jl
+++ b/test/ambiguous.jl
@@ -9,6 +9,8 @@ ambig(x::Int, y::Int) = 4
 ambig(x::Number, y) = 5
 # END OF LINE NUMBER SENSITIVITY
 
+using SparseArrays
+
 # For curmod_*
 include("testenv.jl")
 
@@ -278,9 +280,9 @@ end
         pop!(need_to_handle_undef_sparam, which(Base.LinAlg.promote_leaf_eltypes, (Union{AbstractArray{T}, Tuple{Vararg{T}}} where T<:Number,)))
         pop!(need_to_handle_undef_sparam, which(Base.LinAlg.promote_leaf_eltypes,
                                                 (Union{AbstractArray{T}, Tuple{Vararg{T}}} where T<:(AbstractArray{<:Number}),)))
-        pop!(need_to_handle_undef_sparam, which(Base.SparseArrays._absspvec_vcat, (AbstractSparseArray{Tv, Ti, 1} where {Tv, Ti},)))
-        pop!(need_to_handle_undef_sparam, which(Base.SparseArrays._absspvec_hcat, (AbstractSparseArray{Tv, Ti, 1} where {Tv, Ti},)))
-        pop!(need_to_handle_undef_sparam, which(Base.cat, (Any, Base.SparseArrays._TypedDenseConcatGroup{T} where T)))
+        # pop!(need_to_handle_undef_sparam, which(SparseArrays._absspvec_vcat, (AbstractSparseArray{Tv, Ti, 1} where {Tv, Ti},)))
+        # pop!(need_to_handle_undef_sparam, which(SparseArrays._absspvec_hcat, (AbstractSparseArray{Tv, Ti, 1} where {Tv, Ti},)))
+        pop!(need_to_handle_undef_sparam, which(Base.cat, (Any, SparseArrays._TypedDenseConcatGroup{T} where T)))
         pop!(need_to_handle_undef_sparam, which(Base.float, Tuple{AbstractArray{Union{Missing, T},N} where {T, N}}))
         pop!(need_to_handle_undef_sparam, which(Base.convert, Tuple{Type{Union{Missing, T}} where T, Any}))
         pop!(need_to_handle_undef_sparam, which(Base.promote_rule, Tuple{Type{Union{Missing, S}} where S, Type{T} where T}))
diff --git a/test/arrayops.jl b/test/arrayops.jl
index 79a91b9a46405e..10afe8788b0b36 100644
--- a/test/arrayops.jl
+++ b/test/arrayops.jl
@@ -3,6 +3,7 @@
 # Array test
 isdefined(Main, :TestHelpers) || @eval Main include("TestHelpers.jl")
 using Main.TestHelpers.OAs
+using SparseArrays
 
 @testset "basics" begin
     @test length([1, 2, 3]) == 3
diff --git a/test/choosetests.jl b/test/choosetests.jl
index 21b8585bcbbd21..eca802ee99d281 100644
--- a/test/choosetests.jl
+++ b/test/choosetests.jl
@@ -36,7 +36,7 @@ function choosetests(choices = [])
         "char", "strings", "triplequote", "unicode", "intrinsics",
         "dict", "hashing", "iobuffer", "staged", "offsetarray",
         "arrayops", "tuple", "reduce", "reducedim", "random", "abstractarray",
-        "intfuncs", "simdloop", "vecelement", "sparse",
+        "intfuncs", "simdloop", "vecelement",
         "bitarray", "copy", "math", "fastmath", "functional", "iterators",
         "operators", "path", "ccall", "parse", "loading", "bigint",
         "bigfloat", "sorting", "statistics", "spawn", "backtrace",
@@ -101,15 +101,6 @@ function choosetests(choices = [])
         prepend!(tests, stringtests)
     end
 
-    sparsetests = ["sparse/sparse", "sparse/sparsevector", "sparse/higherorderfns"]
-    if "sparse" in skip_tests
-        filter!(x -> (x != "sparse" && !(x in sparsetests)), tests)
-    elseif "sparse" in tests
-        # specifically selected case
-        filter!(x -> x != "sparse", tests)
-        prepend!(tests, sparsetests)
-    end
-
     # do subarray before sparse but after linalg
     if "subarray" in skip_tests
         filter!(x -> x != "subarray", tests)
diff --git a/test/compile.jl b/test/compile.jl
index 2fc3702c2885e8..d4fa1e4a5b46e4 100644
--- a/test/compile.jl
+++ b/test/compile.jl
@@ -220,7 +220,7 @@ try
             Dict(s => Base.module_uuid(Base.root_module(s)) for s in
                 [:Base64, :CRC32c, :Dates, :DelimitedFiles, :FileWatching, :Future,
                  :IterativeEigensolvers, :Logging, :Mmap, :Printf, :Profile, :SharedArrays,
-                 :SuiteSparse, :Test, :Unicode, :Distributed]))
+                 :SparseArrays, :SuiteSparse, :Test, :Unicode, :Distributed]))
         @test discard_module.(deps) == deps1
 
         @test current_task()(0x01, 0x4000, 0x30031234) == 2
diff --git a/test/core.jl b/test/core.jl
index 7b470ec4113a08..aeb3c509670714 100644
--- a/test/core.jl
+++ b/test/core.jl
@@ -3,6 +3,8 @@
 # test core language features
 const Bottom = Union{}
 
+using SparseArrays
+
 # For curmod_*
 include("testenv.jl")
 
diff --git a/test/generic_map_tests.jl b/test/generic_map_tests.jl
index 6de24a5f5f42f3..5c79ea08f909b9 100644
--- a/test/generic_map_tests.jl
+++ b/test/generic_map_tests.jl
@@ -76,7 +76,6 @@ end
 
 function run_map_equivalence_tests(mapf)
     testmap_equivalence(mapf, identity, (1,2,3,4))
-    testmap_equivalence(mapf, x->x>0 ? 1.0 : 0.0, sparse(sparse(1.0I, 5, 5)))
     testmap_equivalence(mapf, (x,y,z)->x+y+z, 1,2,3)
     testmap_equivalence(mapf, x->x ? false : true, BitMatrix(uninitialized, 10,10))
     testmap_equivalence(mapf, x->"foobar", BitMatrix(uninitialized, 10,10))
diff --git a/test/hashing.jl b/test/hashing.jl
index b414c05c52ceb0..afb020127e6387 100644
--- a/test/hashing.jl
+++ b/test/hashing.jl
@@ -1,5 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
+using SparseArrays
+
 types = Any[
     Bool,
     Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64, UInt64, Float32, Float64,
diff --git a/test/linalg/adjtrans.jl b/test/linalg/adjtrans.jl
index c84a551a84c332..8db9afb903e21d 100644
--- a/test/linalg/adjtrans.jl
+++ b/test/linalg/adjtrans.jl
@@ -4,6 +4,7 @@
 
 using Test
 using Base.LinAlg: Adjoint, Transpose
+using SparseArrays
 
 @testset "Adjoint and Transpose inner constructor basics" begin
     intvec, intmat = [1, 2], [1 2; 3 4]
diff --git a/test/linalg/bidiag.jl b/test/linalg/bidiag.jl
index 017aa2cdcdef0f..b1eedd0b9ff7ad 100644
--- a/test/linalg/bidiag.jl
+++ b/test/linalg/bidiag.jl
@@ -3,6 +3,7 @@
 using Test
 using Base.LinAlg: mul!
 import Base.LinAlg: BlasReal, BlasFloat
+using SparseArrays
 
 n = 10 #Size of test matrix
 srand(1)
diff --git a/test/linalg/diagonal.jl b/test/linalg/diagonal.jl
index 70e14dfbea6af7..9d5aa25e6b84cf 100644
--- a/test/linalg/diagonal.jl
+++ b/test/linalg/diagonal.jl
@@ -3,6 +3,7 @@
 using Test
 using Base.LinAlg: mul!, ldiv!, rdiv!
 import Base.LinAlg: BlasFloat, BlasComplex, SingularException
+using SparseArrays
 
 n=12 #Size of matrix problem to test
 srand(1)
diff --git a/test/linalg/special.jl b/test/linalg/special.jl
index 1c50bde7a24d6f..85239901e0379d 100644
--- a/test/linalg/special.jl
+++ b/test/linalg/special.jl
@@ -1,6 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 using Test
+using SparseArrays
 
 using Base.LinAlg: mul!
 
diff --git a/test/linalg/symmetric.jl b/test/linalg/symmetric.jl
index 92fa6bde38c303..9a59addca3e617 100644
--- a/test/linalg/symmetric.jl
+++ b/test/linalg/symmetric.jl
@@ -1,6 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 using Test
+using SparseArrays
 
 srand(101)
 
diff --git a/test/linalg/triangular.jl b/test/linalg/triangular.jl
index f532fe7468234e..91f046d8c44637 100644
--- a/test/linalg/triangular.jl
+++ b/test/linalg/triangular.jl
@@ -5,6 +5,7 @@ using Test
 using Base.LinAlg: BlasFloat, errorbounds, full!, naivesub!, transpose!,
                     UnitUpperTriangular, UnitLowerTriangular,
                     mul!, rdiv!
+using SparseArrays
 
 debug && println("Triangular matrices")
 
diff --git a/test/linalg/tridiag.jl b/test/linalg/tridiag.jl
index 2fd6b4a4ca5a0e..7c09384d2eff35 100644
--- a/test/linalg/tridiag.jl
+++ b/test/linalg/tridiag.jl
@@ -1,5 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
+using SparseArrays
+
 #Test equivalence of eigenvectors/singular vectors taking into account possible phase (sign) differences
 function test_approx_eq_vecs(a::StridedVecOrMat{S}, b::StridedVecOrMat{T}, error=nothing) where {S<:Real,T<:Real}
     n = size(a, 1)
diff --git a/test/linalg/uniformscaling.jl b/test/linalg/uniformscaling.jl
index b163144ab7b993..ed4472dc18b1f7 100644
--- a/test/linalg/uniformscaling.jl
+++ b/test/linalg/uniformscaling.jl
@@ -1,6 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 using Test
+using SparseArrays
 
 srand(123)
 
diff --git a/test/random.jl b/test/random.jl
index e2ca41ef438877..5a2443ef4933c0 100644
--- a/test/random.jl
+++ b/test/random.jl
@@ -2,6 +2,7 @@
 
 isdefined(Main, :TestHelpers) || @eval Main include(joinpath(dirname(@__FILE__), "TestHelpers.jl"))
 using Main.TestHelpers.OAs
+using SparseArrays
 
 using Base.Random.dSFMT
 using Base.Random: Sampler, SamplerRangeFast, SamplerRangeInt, MT_CACHE_F, MT_CACHE_I
diff --git a/test/show.jl b/test/show.jl
index 8403706b7342a2..4ccbe595fce6d3 100644
--- a/test/show.jl
+++ b/test/show.jl
@@ -1,5 +1,7 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
+using SparseArrays
+
 # For curmod_*
 include("testenv.jl")