# DataFramesMeta.jl version 0.11.0 Release

**URL:** https://discourse.julialang.org/t/dataframesmeta-jl-version-0-11-0-release/79648
**Category:** Package Announcements
**Tags:** dataframesmeta
**Created:** [April 18, 2022, 7:47pm UTC](https://discourse.julialang.org/t/dataframesmeta-jl-version-0-11-0-release/79648 "2022-04-18T19:47:16Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![pdeffebach](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pdeffebach/32/10320_2.png) [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [April 18, 2022, 7:47pm UTC](https://discourse.julialang.org/t/dataframesmeta-jl-version-0-11-0-release/79648/1 "2022-04-18T19:47:17Z")

</div>

Hi all,

I’m excited to announce a new release of [DataFramesMeta.jl](https://github.com/JuliaData/DataFramesMeta.jl).

This feature adds one major new feature, which is the ability to use `AsTable` on the right-hand-side of transformations. This makes it easy to work with many columns at once programatically. In particular, it allows one to emulate Stata’s `rowmean` function.

```julia
julia> using DataFramesMeta, Statistics;

julia> df = DataFrame(rand(10, 100), :auto); # A wide data frame

julia> @rselect df :row_mean = mean(AsTable(:))
10×1 DataFrame
 Row │ row_mean 
     │ Float64  
─────┼──────────
   1 │ 0.495727
   2 │ 0.478012
   3 │ 0.449286
   4 │ 0.457304
   5 │ 0.508363
   6 │ 0.470989
   7 │ 0.49183
   8 │ 0.450141
   9 │ 0.489021
  10 │ 0.484617

```

Here `AsTable` works just the same as `AsTable`in DataFrames.jl. Behind the scenes in a `transform` call, we pass a `NamedTuple` of vectors (or in the row-wise case, a plain old `NamedTuple`) to the underlying function.

In the above example, it would appear I the mean of a `100`-element `NamedTuple`, which normally carries with it large compilation costs. But thanks to great work by @bkamins and @nalimilan, DataFrames.jl uses a faster path which never materializes the named tuples, see [#2869](https://github.com/JuliaData/DataFrames.jl/pull/2869) for more details. Thanks to Julia’s modularity, DataFramesMeta.jl benefits from this excellent work.

In the future, I plan to add a `@collect` macro-flag (similar to `@passmissing` and `@byrow`) to let end-users take advantage of this fast path themselves.

There are additional compilation improvements in this release. For example, `:y = f(g(:x))` used to expand to an anonymous function, meaning the same transformation twice in separate places would incur a compilation cost each time. Now, howver, `:y = f(g(:x))` get’s expanded to `:y = (f ∘ g)(:x)`, whose compilation is re-used.

This will make DataFramesMeta.jl feel more snappy with long `@chain`s of operations.

Setup:

```julia
julia> using DataFrames, DataFramesMeta

julia> df = DataFrame(x = [1, 2]);

julia> function inner(x)
           t = (x .- mean(x) .+ std(x)) .^2 
           t ./ t[1]
       end;

julia> function outer(x)
           @. (x + 1) * 100 + 60
       end;

julia> @select df :y = outer(inner(:x)); # TTFP compilation

```

Before:

```julia
julia> @time @select df :y = outer(inner(:x)); # First try
  0.024464 seconds (17.83 k allocations: 1.045 MiB, 97.90% compilation time)

julia> @time @select df :y = outer(inner(:x)); # Second try
  0.025492 seconds (17.82 k allocations: 1.041 MiB, 97.88% compilation time)

```

After:

```julia
julia> @select df :y = outer(inner(:x)); # TTFP compilation

julia> @time @select df :y = outer(inner(:x)); # First try
  0.000110 seconds (121 allocations: 6.531 KiB)

julia> @time @select df :y = outer(inner(:x)); # Second try
  0.000107 seconds (121 allocations: 6.531 KiB)

```

And of course plenty of docs fixes. Thank you to everyone who helped out!

See the [News.md](https://github.com/JuliaData/DataFramesMeta.jl/blob/master/NEWS.md) here.

In the next release we will add:

1. A `@collect` macro-flag for even faster row-wise operations
2. Keyword arguments. I’ve been procrastinating finishing up the PR for it. If you want to help, the PR is [here](https://github.com/JuliaData/DataFramesMeta.jl/pull/323). Please let me know if you would like to assist!

I think we are getting closer to a 1.0 release.