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Julia

Base.Array API Reference

Julia's dense multidimensional array type — the central data structure for numerical and scientific computing, with element-wise broadcasting and rich indexing.

By EZ4Code Team

Array{T,N}

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.

Returns: Array{T,N} filled with zeros.

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

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

Returns: Array{T,N} filled with ones.

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

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

Returns: A reshaped view sharing memory with A.

transpose(A) -> AbstractArray

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

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

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

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

Returns: A new array containing the concatenated inputs.

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.

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

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.

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

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.

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

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