# Is it possible to apply some pipeline whether it'd be MLJ, Scikitlearn or FeatureTransform by group?

**URL:** <https://discourse.julialang.org/t/is-it-possible-to-apply-some-pipeline-whether-itd-be-mlj-scikitlearn-or-featuretransform-by-group/64548>\
**Category:** General Usage\
**Created:** [July 13, 2021, 4:23am UTC](https://discourse.julialang.org/t/is-it-possible-to-apply-some-pipeline-whether-itd-be-mlj-scikitlearn-or-featuretransform-by-group/64548 "2021-07-13T04:23:55Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![xiaodai](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xiaodai/32/15937_2.png) [@xiaodai](https://discourse.julialang.org/u/xiaodai)\
**Post date:** [July 13, 2021, 4:23am UTC](https://discourse.julialang.org/t/is-it-possible-to-apply-some-pipeline-whether-itd-be-mlj-scikitlearn-or-featuretransform-by-group/64548/1 "2021-07-13T04:23:56Z")

</div>

I have asked about this on about scikitlearn. But I can’t find a good solution.

E.g. if I want to perform pca or standardscaler, I might want to do it within each group?

But there is no easy way to do that in a pipeline, like one that remembers what groups were there etc.

I can do it like this using the `.` broadcast, but I feel like it’s missing something. Feels there should be a `GroupBy` operator in the pipeline.

Like `@pipeline GroupBy(StandardScaler(), PCA(), groupby = [:grp1, :grp2])`

but this is the best I came up with so far.

```julia
using MLJ: Standardizer
import MLJ

nest(df, by) = combine(groupby(df, by), sdf->[sdf])
gdf = sort!(nest(phone_info_df5, :year), :year)
l = nrow(gdf)
stds = [Standardizer() for _ in 1:l]
machs = MLJ.machine.(stds, gdf.x1)
MLJ.fit!.(machs)
MLJ.transform.(machs, gdf.x1)

```