arraytable (objecttable) fails when the argument is a nested table

[sprmnt21]

I wanted to see the json file corresponding to a dataframe that has dataframes as elements in one of the columns.

3×3 DataFrame
 Row │ f       oc     sdf
     │ String  Int64  DataFrame
─────┼──────────────────────────────
   1 │ f1          1  3×2 DataFrame
   2 │ f2          2  2×2 DataFrame
   3 │ f3          3  3×2 DataFrame

I have previously verified that

julia> Tables.istable(dfn)
true

but I get this result

julia> objecttable(dfn)
ERROR: ArgumentError: DataFrame doesn't have a defined `StructTypes.StructType`
Stacktrace:
...

I wonder if it is possible to manage this type of structures

if i use jsontable on this string (http://bl.ocks.org/nautat/4085017)

jdata = "[
    {
        \"name\":\"bob\",
        \"salary\":13000,
        \"friends\":[
            {
                \"name\": \"sarah\",
                \"salary\":10000
            },
            {
                \"name\": \"bill\",
                \"salary\":5000
            }
        ]
    },
    {
        \"name\":\"marge\",
        \"salary\":10000,
        \"friends\":[
            {
                \"name\": \"rhonda\",
                \"salary\":10000
            },
            {
                \"name\": \"mike\",
                \"salary\":5000,
                \"hobbies\":[
                    {
                        \"name\":\"surfing\",
                        \"frequency\":10
                    },
                    {
                        \"name\":\"surfing\",
                        \"frequency\":15
                    }
                ]
            }
        ]
    },
    {
        \"name\":\"joe\",
        \"salary\":10000,
        \"friends\":[
            {
                \"name\": \"harry\",
                \"salary\":10000
            },
            {
                \"name\": \"sally\",
                \"salary\":5000
            }
        ]
    }
]"

and then i try to get the dataframe

julia> jsontable(jdata)
JSONTables.Table{false, JSON3.Array{JSON3.Object, Base.CodeUnits{UInt8, String}, Vector{UInt64}}}([:name, :salary, :friends], Dict{Symbol, Type}(:name => String, :salary => Int64, :friends => JSON3.Array{JSON3.Object, Base.CodeUnits{UInt8, String}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}}), JSON3.Object[{
...



julia> DataFrame(jsontable(jdata))
3×3 DataFrame
 Row │ name    salary  friends
     │ String  Int64   Array…
─────┼───────────────────────────────────────────────────
   1 │ bob      13000  JSON3.Object[{\n     "name": "sa…
   2 │ marge    10000  JSON3.Object[{\n     "name": "rh…
   3 │ joe      10000  JSON3.Object[{\n     "name": "ha…

[quinnj]

Yeah, it's true that the machinery in JSONTables.jl isn't set up very well right now for the nested table case. We'll have to think through what makes sense here; maybe we try to apply the sink recursively?

[sprmnt21]

... or put a kwargs to choose to flatten everything.
By obtaining, in the example considered, such a thing

julia> CSV.read("tabintabm.csv", DataFrame)
7×6 DataFrame
 Row │ name     salary  friends.name  friends.salary  friends.hobbies.name  friends.hobbies.frequency      
     │ String7  Int64   String7       Int64           String7?              Union{Missing, Int64}
─────┼────────────────────────────────────────────────────────────────────────────────────────────────     
   1 │ bob       13000  sarah                  10000  missing                                 missing      
   2 │ bob       13000  bill                    5000  missing                                 missing      
   3 │ marge     10000  rhonda                 10000  missing                                 missing      
   4 │ marge     10000  mike                    5000  surfing                                      10      
   5 │ marge     10000  mike                    5000  surfing                                      15      
   6 │ joe       10000  harry                  10000  missing                                 missing      
   7 │ joe       10000  sally                   5000  missing                                 missing