Dpylr do equivalent in query.jl standalone syntax?

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8

Another fun thing I was doing is filtering rows based on the properties of their groups and I ran into the following error:

julia> ex = DataFrame(:a=>[1,2,3,4,5,6,7,8], :b=>repeat([:a, :b, :c, :d], inner=(2)))
8×2 DataFrame
│ Row │ a     │ b      │
│     │ Int64 │ Symbol │
├─────┼───────┼────────┤
│ 1   │ 1     │ a      │
│ 2   │ 2     │ a      │
│ 3   │ 3     │ b      │
│ 4   │ 4     │ b      │
│ 5   │ 5     │ c      │
│ 6   │ 6     │ c      │
│ 7   │ 7     │ d      │
│ 8   │ 8     │ d      │

julia> ex |>
              @groupby(_.b) |>
              @map({rows=_, avg=mean(_.a)})|>
               @filter(_.avg > 2) |>
               @mapmany(_.rows, {__...}) |>
               DataFrame
ERROR: ArgumentError: unable to construct DataFrame from QueryOperators.EnumerableMapMany{Tuple{Int64,Vararg{Union{Int64, Symbol},N} where N},QueryOperators.EnumerableIterable{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableFilter{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableIterable{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableMap{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableIterable{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},QueryOperators.EnumerableGroupBy{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}},QueryOperators.EnumerableIterable{NamedTuple{(:a, :b),Tuple{Int64,Symbol}},Tables.DataValueRowIterator{NamedTuple{(:a, :b),Tuple{Int64,Symbol}},Tables.RowIterator{NamedTuple{(:a, :b),Tuple{Array{Int64,1},Array{Symbol,1}}}}}},getfield(Main, Symbol("##52#62")),getfield(Main, Symbol("##53#63"))}},getfield(Main, Symbol("##55#65"))}},getfield(Main, Symbol("##57#67"))}},getfield(Main, Symbol("##59#69")),getfield(Main, Symbol("##60#70"))}
Stacktrace:
 [1] DataFrame(::QueryOperators.EnumerableMapMany{Tuple{Int64,Vararg{Union{Int64, Symbol},N} where N},QueryOperators.EnumerableIterable{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableFilter{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableIterable{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableMap{NamedTuple{(:rows, :avg),Tuple{Grouping{Symbol,NamedTuple{(:a, :b),Tuple{Int64,Symbol}}},Float64}},QueryOperators.EnumerableIterable{Grouping{Symbol,NamedTup

This only seems to happen when I splat and don’t include any new columns because if I include a new column (like in your example) it works great:

julia> ex |>
              @groupby(_.b) |>
              @map({rows=_, avg=mean(_.a)})|>
               @filter(_.avg > 2) |>
               @mapmany(_.rows, {__..., _.avg}) |>
               DataFrame
6×3 DataFrame
│ Row │ a     │ b      │ avg     │
│     │ Int64 │ Symbol │ Float64 │
├─────┼───────┼────────┼─────────┤
│ 1   │ 3     │ b      │ 3.5     │
│ 2   │ 4     │ b      │ 3.5     │
│ 3   │ 5     │ c      │ 5.5     │
│ 4   │ 6     │ c      │ 5.5     │
│ 5   │ 7     │ d      │ 7.5     │
│ 6   │ 8     │ d      │ 7.5     │