# Performance optimization of a custom binning function

**URL:** <https://discourse.julialang.org/t/performance-optimization-of-a-custom-binning-function/91616>\
**Category:** Performance\
**Created:** [December 13, 2022, 6:50pm UTC](https://discourse.julialang.org/t/performance-optimization-of-a-custom-binning-function/91616 "2022-12-13T18:50:35Z")\
**Posts on this page:** 1\
**Showing post:** 35

<div class="post-metadata">

**Author:** ![Dan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dan/32/42581_2.png) [@Dan](https://discourse.julialang.org/u/Dan)\
**Post date:** [December 15, 2022, 12:31am UTC](https://discourse.julialang.org/t/performance-optimization-of-a-custom-binning-function/91616/35 "2022-12-15T00:31:24Z")

</div>

Turns out, I had only to give up on using clever BlockedArrays, and the compact function actually benchmarked faster than `bin5`:

```julia
function bin4(x, y, b, f = mean)
    p = sortperm(vcat(x,b))
    bins = findall(p.>length(x))
    x_binned = [f(x[p[k]] for k in bins[i]+1:bins[i+1]-1)
                  for i in 1:length(b)-1]
    y_binned = [f(y[p[k],t] for k in bins[i]+1:bins[i+1]-1)
                  for i in 1:length(b)-1, t in 1:size(y,2)]
    return x_binned, y_binned, diff(bins).-1
end

```

(and it doesn’t need sorted bin\_edges)

```julia
julia> @btime bin4($x, $y, $xbin_edges);
  41.895 μs (14 allocations: 21.52 KiB)

julia> @btime bin5($x, $y, $xbin_edges);
  46.254 μs (118 allocations: 40.41 KiB)

```

Edit: Sacrificing clarity, the function can be made to fit into the 7-line category of this thread [https://discourse.julialang.org/t/seven-lines-of-julia-examples-sought/50416/1](https://discourse.julialang.org/t/seven-lines-of-julia-examples-sought/50416/1) :

```julia
function bin6line(x, y, b, f = mean)
    p = sortperm(vcat(x,b))
    bins = findall(p.>length(x))
    aggr = @views [f(z[p[bins[i]+1:bins[i+1]-1]]) for i in 1:length(b)-1, z in (x, eachcol(y)...) ]
    return @view(aggr[:,1]), @view(aggr[:,2:end]), diff(bins).-1
end

```

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