Comments (2)
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?
from jsontables.jl.
... 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
from jsontables.jl.
Related Issues (15)
- Export policy HOT 1
- http://jsonlines.org/ support HOT 1
- Defining additional methods
- Define `filter`
- Support for categorical values HOT 1
- Unexpected result on heterogeneous data HOT 2
- TagBot trigger issue HOT 3
- JSON writing API HOT 1
- Heterogeneous data sometimes detect wrong columns type HOT 2
- Escape sequences HOT 1
- Problem with missing/nothing and copying
- Recovering the input table from a JSONified table HOT 1
- Error converting from JSONTables to DataFrame HOT 3
- arraytable significantly slows down when passed table with heterogenous columns
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from jsontables.jl.