# Dpylr do equivalent in query.jl standalone syntax?

**URL:** <https://discourse.julialang.org/t/dpylr-do-equivalent-in-query-jl-standalone-syntax/24745>\
**Category:** Data\
**Created:** [May 29, 2019, 8:23pm UTC](https://discourse.julialang.org/t/dpylr-do-equivalent-in-query-jl-standalone-syntax/24745 "2019-05-29T20:23:25Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![tlnagy](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tlnagy/32/5815_2.png) [@tlnagy](https://discourse.julialang.org/u/tlnagy)\
**Post date:** [June 2, 2019, 7:40pm UTC](https://discourse.julialang.org/t/dpylr-do-equivalent-in-query-jl-standalone-syntax/24745/8 "2019-06-02T19:40:30Z")

</div>

> [@davidanthoff](#):
>
> ```julia-auto
> @mapmany(_.rows, { __..., _.avg, normed =__.a/_.avg})
> 
> ```

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

```julia
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
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 │

```

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