Julia Base.Array API Reference

`Array{T,N}`

8 methods

A dense N-dimensional array of element type T. Backed by contiguous memory in column-major order; passed by reference and mutated in place with bang-suffixed functions.

zeros([T=Float64], dims...) -> Array{T,N}

Create an N-dimensional array filled with zero(T), with the given shape.

Parameters

Name	Type	Description
T	Type	Element type (defaults to Float64).
dims	Int...	Dimensions of the array; number of dims sets N.

Returns

Array{T,N} filled with zeros.

Example

julia

zeros(3)            # 3-element Vector{Float64} of 0.0
zeros(Int, 2, 4)    # 2x4 Matrix{Int} of 0
zeros(Float32, 2, 2, 2)  # 2x2x2 Array{Float32,3}

ones([T=Float64], dims...) -> Array{T,N}

Create an N-dimensional array filled with one(T).

Parameters

Name	Type	Description
T	Type	Element type (defaults to Float64).
dims	Int...	Dimensions of the array.

Returns

Array{T,N} filled with ones.

Example

julia

ones(3)            # [1.0, 1.0, 1.0]
ones(Int, 2, 2)    # 2x2 matrix of 1
3.0 .* ones(5)     # vector of 3.0 (broadcast)

reshape(A, dims...) -> AbstractArray

Return a view of A with the same data but a different shape. The total length must match.

Parameters

Name	Type	Description
A	AbstractArray	Source array (data is shared, not copied).
dims	Int... or Tuple	New dimensions; product must equal length(A).

Returns

A reshaped view sharing memory with A.

Example

julia

v = 1:12
M = reshape(v, 3, 4)      # 3x4 view into 1:12
reshape(M, 2, 6)          # 2x6 view of the same data
reshape(1:6, 2, :)        # colon infers last dim = 3

transpose(A) -> AbstractArray

Return the transpose of a 2-D matrix (rows <-> columns). For complex matrices use adjoint for conjugate transpose.

Parameters

Name	Type	Description
A	AbstractMatrix	Input matrix.

Returns

A lazy transpose view; use copy(transpose(A)) for a materialized copy.

Example

julia

A = [1 2 3; 4 5 6]     # 2x3
transpose(A)            # 3x2 view:
                       #  [1 4; 2 5; 3 6]
adjoint(A)              # conjugate transpose (same for reals)
A'                     # shorthand for adjoint(A)

cat(A...; dims) -> Array

Concatenate arrays along dimension dims. vcat, hcat, and hvcat are common shortcuts.

Parameters

Name	Type	Description
A...	AbstractArray...	Arrays to concatenate; shapes must agree on non-cat dims.
dims	Int or Tuple	Dimension(s) along which to concatenate.

Returns

A new array containing the concatenated inputs.

Example

julia

vcat([1,2], [3,4])               # [1,2,3,4]
hcat([1 2], [3 4])               # [1 2 3 4]
cat([1 2; 3 4], [5 6; 7 8], dims=2)  # horizontal stack
hvcat((2,2), 1,2,3,4)            # 2x2 matrix

broadcast(f, As...) -> Array (or f.(As...))

Apply f element-wise over arrays, expanding singleton dimensions. The dot syntax f.(x, y) is the idiomatic form.

Parameters

Name	Type	Description
f	Function	Function to apply element-wise.
As...	Any...	Inputs; arrays broadcast over singleton dims.

Returns

Array of results; fusing broadcasts (@.) avoids temporaries.

Example

julia

sin.([0, π/2, π])        # [0.0, 1.0, ~0]
[1,2,3] .+ [10,20,30]    # [11,22,33]
[1,2,3] .+ 10            # scalar broadcast -> [11,12,13]
@. sin(x)^2 + cos(x)^2   # fused, single loop

mapreduce(f, op, A; [init]) -> Any

Apply f to each element of A, then reduce with op. Equivalent to op(f(A[1]), op(f(A[2]), ...)) but fused into one pass.

Parameters

Name	Type	Description
f	Function	Map function applied per element.
op	Function	Associative binary operator for reduction.
A	Iterable	Input collection.
init	Any (kw)	Optional initial value for op.

Returns

The reduced result. Type matches init / op's output.

Example

julia

mapreduce(abs, +, [-1, 2, -3])     # 6  (sum of abs)
mapreduce(uppercase, *, "abc")      # "ABC"
sum(x -> x^2, 1:10)                 # 385 (sum of squares)
mapreduce(sin, +, 1:1000; init=0.0)

sort(A; [dims=1], [alg], [lt=isless], [by], [rev=false]) -> Array

Return a sorted copy of A (or sort along dims). Use sort! for in-place sorting of a Vector.

Parameters

Name	Type	Description
A	Array	Input array.
dims	Int (kw)	Dimension to sort along (default 1).
alg	Algorithm (kw)	Sort algorithm: InsertionSort, QuickSort, MergeSort, RadixSort.
lt	Function (kw)	Custom less-than comparator.
by	Function (kw)	Transform applied before comparison (e.g. by=abs).
rev	Bool (kw)	Reverse the sort order.

Returns

A new sorted array (or sorted view of the same data).

Example

julia

sort([3, 1, 2])                # [1, 2, 3]
sort([3, -1, 2]; by=abs)        # [-1, 2, 3]
sort([1,2,3]; rev=true)         # [3, 2, 1]
sort!([3,1,2])                  # in-place sort
sortperm([30, 10, 20])          # [2, 3, 1] (indices)

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