Improve docs for vcat, cat, etc.

base/abstractarray.jl

@@ -1813,111 +1813,110 @@ end
 """
  vcat(A...)
 
-Concatenate along dimension 1. To efficiently concatenate a large vector of arrays,
-use `reduce(vcat, x)`.
+Concatenate arrays or numbers vertically. Equivalent to [`cat`](@ref)`(A...; dims=1)`,
+and to the syntax `[a; b; c]`.
+
+To concatenate a large vector of arrays, `reduce(vcat, A)` calls an efficient method
+when `A isa AbstractVector{<:AbstractVecOrMat}`, rather than working pairwise.
+
+See also [`hcat`](@ref), [`Iterators.flatten`](@ref), [`stack`](@ref).
 
 # Examples
 ```jldoctest
-julia> a = [1 2 3 4 5]
-1×5 Matrix{Int64}:
- 1 2 3 4 5
+julia> v = vcat([1,2], [3,4])
+4-element Vector{Int64}:
+ 1
+ 2
+ 3
+ 4
 
-julia> b = [6 7 8 9 10; 11 12 13 14 15]
-2×5 Matrix{Int64}:
- 6 7 8 9 10
- 11 12 13 14 15
+julia> v == vcat(1, 2, [3,4]) # accepts numbers
+true
 
-julia> vcat(a,b)
-3×5 Matrix{Int64}:
- 1 2 3 4 5
- 6 7 8 9 10
- 11 12 13 14 15
+julia> v == [1; 2; [3,4]] # syntax for the same operation
+true
 
-julia> c = ([1 2 3], [4 5 6])
-([1 2 3], [4 5 6])
+julia> summary(ComplexF64[1; 2; [3,4]]) # syntax for supplying the element type
+"4-element Vector{ComplexF64}"
 
-julia> vcat(c...)
-2×3 Matrix{Int64}:
- 1 2 3
- 4 5 6
+julia> vcat(range(1, 2, length=3)) # collects lazy ranges
+3-element Vector{Float64}:
+ 1.0
+ 1.5
+ 2.0
+
+julia> two = ([10, 20, 30]', Float64[4 5 6; 7 8 9]) # row vector and a matrix
+([10 20 30], [4.0 5.0 6.0; 7.0 8.0 9.0])
 
-julia> vs = [[1, 2], [3, 4], [5, 6]]
-3-element Vector{Vector{Int64}}:
- [1, 2]
- [3, 4]
- [5, 6]
+julia> vcat(two...)
+3×3 Matrix{Float64}:
+ 10.0 20.0 30.0
+  4.0 5.0 6.0
+  7.0 8.0 9.0
 
-julia> reduce(vcat, vs)
+julia> vs = [[1, 2], [3, 4], [5, 6]];
+
+julia> reduce(vcat, vs) # more efficient than vcat(vs...)
 6-element Vector{Int64}:
  1
  2
  3
  4
  5
  6
+
+julia> ans == collect(Iterators.flatten(vs))
+true
 ```
 """
 vcat(X...) = cat(X...; dims=Val(1))
 """
  hcat(A...)
 
-Concatenate along dimension 2. To efficiently concatenate a large vector of arrays,
-use `reduce(hcat, x)`.
+Concatenate arrays or numbers horizontally. Equivalent to [`cat`](@ref)`(A...; dims=2)`,
+and to the syntax `[a b c]` or `[a;; b;; c]`.
+
+For a large vector of arrays, `reduce(hcat, A)` calls an efficient method
+when `A isa AbstractVector{<:AbstractVecOrMat}`.
+For a vector of vectors, this can also be written [`stack`](@ref)`(A)`.
+
+See also [`vcat`](@ref), [`hvcat`](@ref).
 
 # Examples
 ```jldoctest
-julia> a = [1; 2; 3; 4; 5]
-5-element Vector{Int64}:
- 1
- 2
- 3
- 4
- 5
+julia> hcat([1,2], [3,4], [5,6])
+2×3 Matrix{Int64}:
+ 1 3 5
+ 2 4 6
 
-julia> b = [6 7; 8 9; 10 11; 12 13; 14 15]
-5×2 Matrix{Int64}:
- 6 7
- 8 9
- 10 11
- 12 13
- 14 15
-
-julia> hcat(a,b)
-5×3 Matrix{Int64}:
- 1 6 7
- 2 8 9
- 3 10 11
- 4 12 13
- 5 14 15
-
-julia> c = ([1; 2; 3], [4; 5; 6])
-([1, 2, 3], [4, 5, 6])
-
-julia> hcat(c...)
-3×2 Matrix{Int64}:
- 1 4
- 2 5
- 3 6
+julia> hcat(1, 2, [30 40], [5, 6, 7]') # accepts numbers
+1×7 Matrix{Int64}:
+ 1 2 30 40 5 6 7
 
-julia> x = Matrix(undef, 3, 0) # x = [] would have created an Array{Any, 1}, but need an Array{Any, 2}
-3×0 Matrix{Any}
+julia> ans == [1 2 [30 40] [5, 6, 7]'] # syntax for the same operation
+true
 
-julia> hcat(x, [1; 2; 3])
-3×1 Matrix{Any}:
- 1
- 2
- 3
+julia> Float32[1 2 [30 40] [5, 6, 7]'] # syntax for supplying the eltype
+1×7 Matrix{Float32}:
+ 1.0 2.0 30.0 40.0 5.0 6.0 7.0
 
-julia> vs = [[1, 2], [3, 4], [5, 6]]
-3-element Vector{Vector{Int64}}:
- [1, 2]
- [3, 4]
- [5, 6]
+julia> ms = [zeros(2,2), [1 2; 3 4], [50 60; 70 80]];
 
-julia> reduce(hcat, vs)
-2×3 Matrix{Int64}:
- 1 3 5
- 2 4 6
+julia> reduce(hcat, ms) # more efficient than hcat(ms...)
+2×6 Matrix{Float64}:
+ 0.0 0.0 1.0 2.0 50.0 60.0
+ 0.0 0.0 3.0 4.0 70.0 80.0
+
+julia> stack(ms) |> summary # disagrees on a vector of matrices
+"2×2×3 Array{Float64, 3}"
+
+julia> hcat(Int[], Int[], Int[]) # empty vectors, each of size (0,)
+0×3 Matrix{Int64}
+
+julia> hcat([1.1, 9.9], Matrix(undef, 2, 0)) # hcat with empty 2×0 Matrix
+2×1 Matrix{Any}:
+ 1.1
+ 9.9
 ```
 """
 hcat(X...) = cat(X...; dims=Val(2))
@@ -1928,34 +1927,45 @@ typed_hcat(::Type{T}, X...) where T = _cat_t(Val(2), T, X...)
 """
  cat(A...; dims)
 
-Concatenate the input arrays along the specified dimensions in the iterable `dims`. For
-dimensions not in `dims`, all input arrays should have the same size, which will also be the
-size of the output array along that dimension. For dimensions in `dims`, the size of the
-output array is the sum of the sizes of the input arrays along that dimension. If `dims` is
-a single number, the different arrays are tightly stacked along that dimension. If `dims` is
-an iterable containing several dimensions, this allows one to construct block diagonal
-matrices and their higher-dimensional analogues by simultaneously increasing several
-dimensions for every new input array and putting zero blocks elsewhere. For example,
-`cat(matrices...; dims=(1,2))` builds a block diagonal matrix, i.e. a block matrix with
-`matrices[1]`, `matrices[2]`, ... as diagonal blocks and matching zero blocks away from the
-diagonal.
+Concatenate the input arrays along the dimensions specified in `dims`.
+
+Along a dimension `d in dims`, the size of the output array is `sum(size(a,d) for
+a in A)`.
+Along other dimensions, all input arrays should have the same size,
+which will also be the size of the output array along those dimensions.
+
+If `dims` is a single number, the different arrays are tightly packed along that dimension.
+If `dims` is an iterable containing several dimensions, the positions along these dimensions
+are increased simultaneously for each input array, filling with zero elsewhere.
+This allows one to construct block-diagonal matrices as `cat(matrices...; dims=(1,2))`,
+and their higher-dimensional analogues.
+
+The special case `dims=1` is [`vcat`](@ref), and `dims=2` is [`hcat`](@ref).
+See also [`hvcat`](@ref), [`stack`](@ref), [`repeat`](@ref).
 
-See also [`hcat`](@ref), [`vcat`](@ref), [`hvcat`](@ref), [`repeat`](@ref).
+The keyword also accepts `Val(dims)`.
+
+!!! compat "Julia 1.8"
+ For multiple dimensions `dims = Val(::Tuple)` was added in Julia 1.8.
 
 # Examples
 ```jldoctest
-julia> cat([1 2; 3 4], [pi, pi], fill(10, 2,3,1); dims=2)
+julia> cat([1 2; 3 4], [pi, pi], fill(10, 2,3,1); dims=2) # same as hcat
 2×6×1 Array{Float64, 3}:
 [:, :, 1] =
  1.0 2.0 3.14159 10.0 10.0 10.0
  3.0 4.0 3.14159 10.0 10.0 10.0
 
-julia> cat(true, trues(2,2), trues(4)', dims=(1,2))
+julia> cat(true, trues(2,2), trues(4)', dims=(1,2)) # block-diagonal
 4×7 Matrix{Bool}:
  1 0 0 0 0 0 0
  0 1 1 0 0 0 0
  0 1 1 0 0 0 0
  0 0 0 1 1 1 1
+
+julia> cat(1, [2], [3;;]; dims=Val(2))
+1×3 Matrix{Int64}:
+ 1 2 3
 ```
 """
 @inline cat(A...; dims) = _cat(dims, A...)
@@ -3064,7 +3074,7 @@ concatenated along the remaining dimensions.
 For example, if `dims = [1,2]` and `A` is 4-dimensional, then `f` is called on `x = A[:,:,i,j]`
 for all `i` and `j`, and `f(x)` becomes `R[:,:,i,j]` in the result `R`.
 
-See also [`eachcol`](@ref), [`eachslice`](@ref), [`mapreduce`](@ref).
+See also [`eachcol`](@ref) or [`eachslice`](@ref), used with [`map`](@ref) or [`stack`](@ref).
 
 # Examples
 ```jldoctest
@@ -3084,7 +3094,7 @@ julia> A = reshape(1:30,(2,5,3))
 
 julia> f(x::Matrix) = fill(x[1,1], 1,4); # returns a 1×4 matrix
 
-julia> mapslices(f, A, dims=(1,2))
+julia> B = mapslices(f, A, dims=(1,2))
 1×4×3 Array{$Int, 3}:
 [:, :, 1] =
  1 1 1 1
@@ -3095,6 +3105,11 @@ julia> mapslices(f, A, dims=(1,2))
 [:, :, 3] =
  21 21 21 21
 
+julia> f2(x::AbstractMatrix) = fill(x[1,1], 1,4);
+
+julia> B == stack(f2, eachslice(A, dims=3))
+true
+
 julia> g(x) = x[begin] // x[end-1]; # returns a number
 
 julia> mapslices(g, A, dims=[1,3])

base/reduce.jl

@@ -459,8 +459,10 @@ For empty collections, providing `init` will be necessary, except for some speci
 neutral element of `op`.
 
 Reductions for certain commonly-used operators may have special implementations, and
-should be used instead: `maximum(itr)`, `minimum(itr)`, `sum(itr)`, `prod(itr)`,
- `any(itr)`, `all(itr)`.
+should be used instead: [`maximum`](@ref)`(itr)`, [`minimum`](@ref)`(itr)`, [`sum`](@ref)`(itr)`,
+[`prod`](@ref)`(itr)`, [`any`](@ref)`(itr)`, [`all`](@ref)`(itr)`.
+There are efficient methods for concatenating certain arrays of arrays
+by calling `reduce(`[`vcat`](@ref)`, arr)` or `reduce(`[`hcat`](@ref)`, arr)`.
 
 The associativity of the reduction is implementation dependent. This means that you can't
 use non-associative operations like `-` because it is undefined whether `reduce(-,[1,2,3])`

base/slicearray.jl

@@ -85,6 +85,8 @@ the ordering of the dimensions will match those in `dims`. If `drop = false`, th
 `Slices` will have the same dimensionality as the underlying array, with inner
 dimensions having size 1.
 
+See [`stack`](@ref)`(slices; dims)` for the inverse of `eachcol(A; dims::Integer, drop=true)`.
+
 See also [`eachrow`](@ref), [`eachcol`](@ref), [`mapslices`](@ref) and [`selectdim`](@ref).
 
 !!! compat "Julia 1.1"
@@ -131,6 +133,8 @@ end
 Create a [`RowSlices`](@ref) object that is a vector of rows of matrix or vector `A`.
 Row slices are returned as `AbstractVector` views of `A`.
 
+For the inverse, see [`stack`](@ref)`(rows; dims=1)`.
+
 See also [`eachcol`](@ref), [`eachslice`](@ref) and [`mapslices`](@ref).
 
 !!! compat "Julia 1.1"
@@ -167,6 +171,8 @@ eachrow(A::AbstractVector) = eachrow(reshape(A, size(A,1), 1))
 Create a [`ColumnSlices`](@ref) object that is a vector of columns of matrix or vector `A`.
 Column slices are returned as `AbstractVector` views of `A`.
 
+For the inverse, see [`stack`](@ref)`(cols)` or `reduce(`[`hcat`](@ref)`, cols)`.
+
 See also [`eachrow`](@ref), [`eachslice`](@ref) and [`mapslices`](@ref).
 
 !!! compat "Julia 1.1"