Frequently Asked Questions (FAQ)

The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.

Extract the axes names from a Cube

using YAXArrays
using DimensionalData

julia> c = YAXArray(rand(10, 10, 5))

╭─────────────────────────────╮
│ 10×10×5 YAXArray{Float64,3} │
├─────────────────────────────┴────────────────────────────────────────── dims ┐
  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 3.91 KB
└──────────────────────────────────────────────────────────────────────────────┘

julia> caxes(c) # former way of doing it

(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

To get the axes of a YAXArray use the dims function instead of the caxes function

julia> dims(c)

(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

INFO

Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.

rebuild

As an example let's consider the following

using YAXArrays
using DimensionalData

c = YAXArray(ones(Int, 10,10))

╭─────────────────────────╮
│ 10×10 YAXArray{Int64,2} │
├─────────────────────────┴────────────────────────────────────── dims ┐
  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────── file size ┤ 
  file size: 800.0 bytes
└──────────────────────────────────────────────────────────────────────┘

then creating a new c with the same structure (axes) but different values is done by

julia> new_c = rebuild(c, rand(10,10))

╭───────────────────────────╮
│ 10×10 YAXArray{Float64,2} │
├───────────────────────────┴──────────────────────────────────────────── dims ┐
  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 800.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly

julia> c_c = YAXArray(dims(c), rand(10,10))

╭───────────────────────────╮
│ 10×10 YAXArray{Float64,2} │
├───────────────────────────┴──────────────────────────────────────────── dims ┐
  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 800.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube

There are two options to collect values from axes. In this examples the axis ranges from 1 to 10.

These two examples bring the same result

collect(getAxis("Dim_1", c).val)
collect(c.axes[1].val)

10-element Vector{Int64}:
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10

to collect data from a cube works exactly the same as doing it from an array

julia> c[:, :, 1]

╭─────────────────────────╮
│ 10×10 YAXArray{Int64,2} │
├─────────────────────────┴────────────────────────────────────────────── dims ┐
  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 800.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes

It is possible to concatenate several cubes that shared the same dimensions using the [concatenatecubes]@ref function.

Let's create two dummy cubes

using YAXArrays
axlist = (
    Dim{:time}(range(1, 20, length=20)),
    Dim{:lon}(range(1, 10, length=10)),
    Dim{:lat}(range(1, 5, length=15))
    )

data1 = rand(20, 10, 15)
ds1 = YAXArray(axlist, data1)

data2 = rand(20, 10, 15)
ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))

╭────────────────────────────────╮
│ 20×10×15×2 YAXArray{Float64,4} │
├────────────────────────────────┴─────────────────────────────────────── dims ┐
  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
  ⬔ Variables Categorical{String} ["var1", "var2"] ForwardOrdered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 46.88 KB
└──────────────────────────────────────────────────────────────────────────────┘

How do I subset a YAXArray ( Cube ) or Dataset?

These are the three main datatypes provided by the YAXArrays libray. You can find a description of them here. A Cube is no more than a YAXArray, so, we will not explicitly tell about a Cube.

Subsetting a YAXArray

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

using YAXArrays
using Dates # To generate the dates of the time axis
using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. `a .. b`.

Define the time span of the YAXArray

t = Date("2020-01-01"):Month(1):Date("2022-12-31")

Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")

create YAXArray axes

axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))

(↓ Lon  1:10,
→ Lat  1:10,
↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))

╭────────────────────────────╮
│ 10×10×36 YAXArray{Int64,3} │
├────────────────────────────┴─────────────────────────────────────────── dims ┐
  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤ 
  file size: 28.12 KB
└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]

╭────────────────────────────╮
│ 10×10×12 YAXArray{Int64,3} │
├────────────────────────────┴─────────────────────────────────────────── dims ┐
  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤ 
  file size: 9.38 KB
└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

ytime2 = y[Time=At(Date("2021-05-01"))]

╭─────────────────────────╮
│ 10×10 YAXArray{Int64,2} │
├─────────────────────────┴────────────────────────── dims ┐
  ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
├──────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────── file size ┤ 
  file size: 800.0 bytes
└──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]

╭───────────────────────────╮
│ 10×10×8 YAXArray{Int64,3} │
├───────────────────────────┴──────────────────────────────────────────── dims ┐
  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤ 
  file size: 6.25 KB
└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]

╭──────────────────────────╮
│ 5×6×36 YAXArray{Int64,3} │
├──────────────────────────┴───────────────────────────────────────────── dims ┐
  ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤ 
  file size: 8.44 KB
└──────────────────────────────────────────────────────────────────────────────┘

Subsetting a Dataset

In a dataset, we can have several variables (YAXArrays) that share some or all of their dimensions.

Subsetting a Dataset whose variables share all their dimensions

This works for YAXArrays. Let's make an example.

using YAXArrays
using Dates # To generate the dates of the time axis
using DimensionalData # To use the "Between" option for selecting data

t = Date("2020-01-01"):Month(1):Date("2022-12-31")
axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))

var1 = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
var2 = YAXArray(axes, reshape((1:3600)*5, (10, 10, 36)))

ds = Dataset(; var1=var1, var2=var2)

YAXArray Dataset
Shared Axes: 
  (↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)

Variables: 
var1, var2

ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]

YAXArray Dataset
Shared Axes: 
  (↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)

Variables: 
var1, var2

Subsetting a Dataset whose variables share some but not all of their dimensions

In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.

But we can also subset a variable by the values of another variable with which it shares some dimensions.

Warning

If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.

Let's make an example.

using YAXArrays
using Dates # To generate the dates of the time axis
using DimensionalData # To use the "Between" selector for selecting data

t = Date("2020-01-01"):Month(1):Date("2022-12-31")
common_axis = Dim{:points}(1:100)
time_axis =   Dim{:Time}(t)

# Note that longitudes and latitudes are not dimensions, but YAXArrays
longitudes = YAXArray((common_axis,), rand(1:369, 100)) # 100 random values taken from 1 to 359
latitudes  = YAXArray((common_axis,), rand(0:90, 100))  # 100 random values taken from 0 to 90
temperature = YAXArray((common_axis, time_axis), rand(-40:40, (100, 36)))

ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)

YAXArray Dataset
Shared Axes: 
  (↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)

Variables: 
latitudes, longitudes

Variables with additional axes:
  Additional Axes: 
  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
  Variables: 
  temperature

Select all points between 20ºN and 85ºN, and 0ºE to 180ºE

ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
                             ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
                             ) # Where
              ] # ds

YAXArray Dataset
Shared Axes: 
None
Variables with additional axes:
  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
  Variables: 
  longitudes

  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
  Variables: 
  temperature

  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
  Variables: 
  latitudes

If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:

latitudes_yasxa  = readcubedata(ds["latitudes"])
longitudes_yasxa = readcubedata(ds["longitudes"])
ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                             longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
                             ) # Where
              ] # ds

YAXArray Dataset
Shared Axes: 
None
Variables with additional axes:
  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
  Variables: 
  temperature

  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
  Variables: 
  latitudes

  Additional Axes: 
  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
  Variables: 
  longitudes

How do I apply map algebra?

Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:

Multiplying cubes with only spatio-temporal dimensions

julia> map((x, y) -> x * y, ds1, ds2)

╭──────────────────────────────╮
│ 20×10×15 YAXArray{Float64,3} │
├──────────────────────────────┴───────────────────────────────────────── dims ┐
  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 23.44 KB
└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])

╭──────────────────────────────╮
│ 20×10×15 YAXArray{Float64,3} │
├──────────────────────────────┴───────────────────────────────────────── dims ┐
  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 23.44 KB
└──────────────────────────────────────────────────────────────────────────────┘

To add some complexity, we will multiply each value for π and then divided for the sum of each time step. We will use the ds1 cube for this purpose.

julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
           (xin * π) ./ maximum(skipmissing(xin))
       end

╭──────────────────────────────────────────────╮
│ 10×15×20 YAXArray{Union{Missing, Float64},3} │
├──────────────────────────────────────────────┴───────────────────────── dims ┐
  ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
  → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
  ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 23.44 KB
└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function?

The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.

Here we will use the ds1 Cube defined previously and we create a mask for data classification.

Cube containing a mask with classes 1, 2 and 3.

julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))

╭─────────────────────────╮
│ 10×15 YAXArray{Int64,2} │
├─────────────────────────┴────────────────────────────────────────────── dims ┐
  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 1.17 KB
└──────────────────────────────────────────────────────────────────────────────┘

using GLMakie
GLMakie.activate!()
# This is how our classification map looks like
fig, ax, obj = heatmap(classes;
    colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
cbar = Colorbar(fig[1,2], obj)
fig

Now we define the input cubes that will be considered for the iterable table

t = CubeTable(values=ds1, classes=classes)

Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)

using DataFrames
using OnlineStats
## visualization of the CubeTable
c_tbl = DataFrame(t[1])
first(c_tbl, 5)

In this line we calculate the Mean for each class

julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))

╭───────────────────────────────────────────────╮
│ 3-element YAXArray{Union{Missing, Float64},1} │
├───────────────────────────────────────────────┴──────────────────────── dims ┐
  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 24.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.

julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))

╭──────────────────────────────────────────╮
│ 3×20 YAXArray{Union{Missing, Float64},2} │
├──────────────────────────────────────────┴───────────────────────────── dims ┐
  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
  → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├─────────────────────────────────────────────────────────────────── file size ┤
  file size: 480.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset

One variable name

julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)

YAXArray Dataset
Shared Axes:
  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)

Variables: 
a

Multiple variable names

keylist = (:a, :b, :c)
varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))

julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)

YAXArray Dataset
Shared Axes:
None
Variables with additional axes:
  Additional Axes: 
  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
  Variables: 
  a

  Additional Axes: 
  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
  Variables: 
  c

  Additional Axes: 
  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
  Variables: 
  b

Ho do I construct a Dataset from a TimeArray

In this section we will use MarketData.jl and TimeSeries.jl to simulate some stocks.

using YAXArrays, DimensionalData
using MarketData, TimeSeries

stocks = Dict(:Stock1 => random_ohlcv(), :Stock2 => random_ohlcv(), :Stock3 => random_ohlcv())
d_keys = keys(stocks)

KeySet for a Dict{Symbol, TimeSeries.TimeArray{Float64, 2, DateTime, Matrix{Float64}}} with 3 entries. Keys:
  :Stock3
  :Stock1
  :Stock2

currently there is not direct support to obtain dims from a TimeArray, but we can code a function for it

getTArrayAxes(ta::TimeArray) = (Dim{:time}(timestamp(ta)), Dim{:variable}(colnames(ta)), );

then, we create the YAXArrays as

yax_list = [YAXArray(getTArrayAxes(stocks[k]), values(stocks[k])) for k in d_keys];

and a Dataset with all stocks names

julia> ds = Dataset(; (d_keys .=> yax_list)...)

YAXArray Dataset
Shared Axes:
None
Variables with additional axes:
  Additional Axes: 
  (↓ time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
  → variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
  Variables: 
  Stock1

  Additional Axes: 
  (↓ time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
  → variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
  Variables: 
  Stock2

  Additional Axes: 
  (↓ time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
  → variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
  Variables: 
  Stock3

and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others

yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];

and voilà

julia> ds = Dataset(; (d_keys .=> yax_list)...)

YAXArray Dataset
Shared Axes:
  (↓ time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
  → variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)

Variables: 
Stock1, Stock2, Stock3

now they are printed together, showing that is exactly the same axis structure for all variables.