DimensionalData.jl provides tools and abstractions for working with datasets that have named dimensions, and optionally a lookup index. It provides no-cost abstractions for named indexing, and fast index lookups.

DimensionalData is a pluggable, generalised version of AxisArrays.jl with a cleaner syntax, and additional functionality found in NamedDims.jl. It has similar goals to pythons xarray, and is primarily written for use with spatial data in Rasters.jl.

The basic syntax is:

julia> using DimensionalData

julia> A = rand(X(50), Y(10.0:40.0))
50×31 DimArray{Float64,2} with dimensions: 
  X,
  Y Sampled{Float64} 10.0:1.0:40.0 ForwardOrdered Regular Points
 10.0         11.0       12.0       13.0       14.0        15.0       16.0        17.0       …  32.0       33.0        34.0       35.0       36.0        37.0       38.0        39.0       40.0
  0.293347     0.737456   0.986853   0.780584   0.707698    0.804148   0.632667    0.780715      0.767575   0.555214    0.872922   0.808766   0.880933    0.624759   0.803766    0.796118   0.696768
  0.199599     0.290297   0.791926   0.564099   0.0241986   0.239102   0.0169679   0.186455      0.644238   0.467091    0.524335   0.42627    0.982347    0.324083   0.0356058   0.306446   0.117187
  ⋮                                                         ⋮                                ⋱                                     ⋮                                                        ⋮
  0.720404     0.388392   0.635609   0.430277   0.943823    0.661993   0.650442    0.91391   …   0.299713   0.518607    0.411973   0.410308   0.438817    0.580232   0.751231    0.519257   0.598583
  0.00602102   0.270036   0.696129   0.139551   0.924883    0.190963   0.164888    0.13436       0.717962   0.0452556   0.230943   0.848782   0.0362465   0.363868   0.709489    0.644131   0.801824

julia> A[Y=1:10, X=1]
10-element DimArray{Float64,1} with dimensions: 
  Y Sampled{Float64} 10.0:1.0:19.0 ForwardOrdered Regular Points
and reference dimensions: X
 10.0  0.293347
 11.0  0.737456
 12.0  0.986853
 13.0  0.780584
  ⋮    
 17.0  0.780715
 18.0  0.472306
 19.0  0.20442

See the docs for more details

Some properties of DimensionalData.jl objects:

• broadcasting and most Base methods maintain and sync dimension context.
• comprehensive plot recipes for Plots.jl.
• a Tables.jl interface with DimTable
• multi-layered DimStacks that can be indexed together, and have base methods applied to all layers.
• the Adapt.jl interface for use on GPUs, even as GPU kernel arguments.
• traits for handling a wide range of spatial data types accurately.

getindex, setindex! view

• size, axes, firstindex, lastindex
• cat, reverse, dropdims
• reduce, mapreduce
• sum, prod, maximum, minimum,
• mean, median, extrema, std, var, cor, cov
• permutedims, adjoint, transpose, Transpose
• mapslices, eachslice

• fill, ones, zeros, falses, trues, rand

Previously exported methods can me brought into global scope by using the sub-modules they have been moved to - LookupArrays and Dimensions:

using DimensionalData
using DimensionalData.LookupArrays, DimensionalData.Dimensions

There are a lot of similar Julia packages in this space. AxisArrays.jl, NamedDims.jl, NamedArrays.jl are registered alternative that each cover some of the functionality provided by DimensionalData.jl. DimensionalData.jl should be able to replicate most of their syntax and functionality.

AxisKeys.jl and AbstractIndices.jl are some other interesting developments. For more detail on why there are so many similar options and where things are headed, read this thread.

The main functionality is explained here, but the full list of features is listed at the API page.