# Run multiple instances of transform on specific column combinations of a GroupedDataFrame in DataFrames mini language

**URL:** <https://discourse.julialang.org/t/run-multiple-instances-of-transform-on-specific-column-combinations-of-a-groupeddataframe-in-dataframes-mini-language/91736>\
**Category:** New to Julia\
**Tags:** question, dataframes\
**Created:** [December 16, 2022, 2:08pm UTC](https://discourse.julialang.org/t/run-multiple-instances-of-transform-on-specific-column-combinations-of-a-groupeddataframe-in-dataframes-mini-language/91736 "2022-12-16T14:08:32Z")\
**Posts on this page:** 1\
**Showing post:** 22

<div class="post-metadata">

**Author:** ![rocco\_sprmnt21](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rocco_sprmnt21/32/20127_2.png) [@rocco\_sprmnt21](https://discourse.julialang.org/u/rocco_sprmnt21)\
**Post date:** [December 23, 2022, 10:57am UTC](https://discourse.julialang.org/t/run-multiple-instances-of-transform-on-specific-column-combinations-of-a-groupeddataframe-in-dataframes-mini-language/91736/22 "2022-12-23T10:57:41Z")

</div>

Here my log, starting from a clean session.  
See if you can see any difference.

```julia
julia> using DataFrames

julia> df = DataFrame(id=rand(1:3,10), A=rand(11:13,10),
       B=rand(101:103,10), C = rand(25:27,10), D = rand(32:34,10))
10×5 DataFrame
 Row │ id A B C D     
     │ Int64 Int64 Int64 Int64 Int64 
─────┼───────────────────────────────────
   1 │ 1 13 103 26 32
   2 │ 2 13 101 26 33
   3 │ 3 13 103 25 32
   4 │ 2 11 101 26 32
   5 │ 2 12 102 25 34
   6 │ 1 11 102 25 34
   7 │ 2 11 103 26 32
   8 │ 3 13 102 25 32
   9 │ 1 12 103 25 33
  10 │ 3 11 101 26 32

julia> gdf = groupby(df, :id)
GroupedDataFrame with 3 groups based on key: id
First Group (3 rows): id = 1
 Row │ id A B C D     
     │ Int64 Int64 Int64 Int64 Int64 
─────┼───────────────────────────────────
   1 │ 1 13 103 26 32
   2 │ 1 11 102 25 34
   3 │ 1 12 103 25 33
⋮
Last Group (3 rows): id = 3
 Row │ id A B C D     
     │ Int64 Int64 Int64 Int64 Int64 
─────┼───────────────────────────────────
   1 │ 3 13 103 25 32
   2 │ 3 13 102 25 32
   3 │ 3 11 101 26 32

julia> function funnynt(id,Cols...; p1=1,p2=2,p3=3)
           m= Matrix{Float64}(undef, length(id),length(Cols))   
           for i in eachindex(Cols)
               if p3>0
                   m[:,i]= (id .+p1).*(Cols[i].+p2*p3)
               else
                   m[:,i]= (id .+p1).*(Cols[i].+p2)
               end
           end
           #ncols=Symbol.("id_c",1:length(Cols))
           ncols=Symbol.("id_",names(df,Not(:id)))

           return (;zip(ncols,eachcol(m))...)
       end
funnynt (generic function with 1 method)

julia> transform(gdf,Cols(:) => funnynt=>AsTable)
10×9 DataFrame
 Row │ id A B C D id_A id_B id_C id_D ⋯
     │ Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Floa ⋯
─────┼─────────────────────────────────────────────────────────────────────
   1 │ 1 13 103 26 32 38.0 218.0 64.0 7 ⋯
   2 │ 2 13 101 26 33 57.0 321.0 96.0 11  
   3 │ 3 13 103 25 32 76.0 436.0 124.0 15  
   4 │ 2 11 101 26 32 51.0 321.0 96.0 11  
   5 │ 2 12 102 25 34 54.0 324.0 93.0 12 ⋯
   6 │ 1 11 102 25 34 34.0 216.0 62.0 8  
   7 │ 2 11 103 26 32 51.0 327.0 96.0 11  
   8 │ 3 13 102 25 32 76.0 432.0 124.0 15  
   9 │ 1 12 103 25 33 36.0 218.0 62.0 7 ⋯
  10 │ 3 11 101 26 32 68.0 428.0 128.0 15  
                                                           1 column omitted

julia> transform(gdf,Cols(:) =>((x...)->funnynt(x...,p1=0;p3=0))=>AsTable) 
10×9 DataFrame
 Row │ id A B C D id_A id_B id_C id_D ⋯
     │ Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Floa ⋯
─────┼─────────────────────────────────────────────────────────────────────
   1 │ 1 13 103 26 32 15.0 105.0 28.0 3 ⋯
   2 │ 2 13 101 26 33 30.0 206.0 56.0 7  
   3 │ 3 13 103 25 32 45.0 315.0 81.0 10  
   4 │ 2 11 101 26 32 26.0 206.0 56.0 6  
   5 │ 2 12 102 25 34 28.0 208.0 54.0 7 ⋯
   6 │ 1 11 102 25 34 13.0 104.0 27.0 3  
   7 │ 2 11 103 26 32 26.0 210.0 56.0 6  
   8 │ 3 13 102 25 32 45.0 312.0 81.0 10  
   9 │ 1 12 103 25 33 14.0 105.0 27.0 3 ⋯
  10 │ 3 11 101 26 32 39.0 309.0 84.0 10  
                                                           1 column omitted

```

As for

> [@phantom](#):
>
> how the `zip` and `...` commands are working here

I don’t know if I’m able to clarify what actually happens behind the scene.  
I go a bit for analogies in my experience with Julia (I don’t read much documentation, I must confess).

```julia

nt=(f1=1,f2=2,f3=3) # basic definition of a named tuple

nt=(;(f1=1,f2=2,f3=3)...) # the function of the splatting operator is to "open" the collection

# If you need to define a namedtuple dynamically, you need to follow these steps

fields=Symbol.('f',1:3) # a sequence of symbols

# and

values=[1,2,3] # a sequence of values

# below various ways to input it to the syntax (;kwargs...)
# which should be treated by the parser (?) in the same way as non-positional parameters of functions!?
# But here perhaps it is better for someone who knows about these things to be more specific.
(;zip(fields,values)...)
(;Pair.(fields,values)...)
(;tuple.(fields,values)...)

```

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