Julia DataFrames - How to do one-hot encoding?

It is easy enough to do it with basic functions we provide though:

julia> df = DataFrame(x=rand([1:3;missing], 20))
20×1 DataFrame
│ Row │ x       │
│     │ Int64?  │
├─────┼─────────┤
│ 1   │ 1       │
│ 2   │ 2       │
│ 3   │ missing │
│ 4   │ 1       │
│ 5   │ 3       │
│ 6   │ missing │
│ 7   │ 3       │
│ 8   │ 3       │
│ 9   │ 3       │
│ 10  │ 3       │
│ 11  │ missing │
│ 12  │ 1       │
│ 13  │ 3       │
│ 14  │ 3       │
│ 15  │ 3       │
│ 16  │ 1       │
│ 17  │ missing │
│ 18  │ 1       │
│ 19  │ 1       │
│ 20  │ missing │

julia> ux = unique(df.x); transform(df, @. :x => ByRow(isequal(ux)) .=> Symbol(:x_, ux))
20×5 DataFrame
│ Row │ x       │ x_1  │ x_2  │ x_missing │ x_3  │
│     │ Int64?  │ Bool │ Bool │ Bool      │ Bool │
├─────┼─────────┼──────┼──────┼───────────┼──────┤
│ 1   │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 2   │ 2       │ 0    │ 1    │ 0         │ 0    │
│ 3   │ missing │ 0    │ 0    │ 1         │ 0    │
│ 4   │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 5   │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 6   │ missing │ 0    │ 0    │ 1         │ 0    │
│ 7   │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 8   │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 9   │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 10  │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 11  │ missing │ 0    │ 0    │ 1         │ 0    │
│ 12  │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 13  │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 14  │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 15  │ 3       │ 0    │ 0    │ 0         │ 1    │
│ 16  │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 17  │ missing │ 0    │ 0    │ 1         │ 0    │
│ 18  │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 19  │ 1       │ 1    │ 0    │ 0         │ 0    │
│ 20  │ missing │ 0    │ 0    │ 1         │ 0    │

EDIT:

Another example:

julia> df = DataFrame(col1=102:104, col2=[["a"], ["a","b"], ["c","b"]])
3×2 DataFrame
│ Row │ col1  │ col2       │
│     │ Int64 │ Array…     │
├─────┼───────┼────────────┤
│ 1   │ 102   │ ["a"]      │
│ 2   │ 103   │ ["a", "b"] │
│ 3   │ 104   │ ["c", "b"] │

julia> ux = unique(reduce(vcat, df.col2))
3-element Array{String,1}:
 "a"
 "b"
 "c"

julia> transform(df, :col2 .=> [ByRow(v -> x in v) for x in ux] .=> Symbol.(:col2_, ux))
3×5 DataFrame
│ Row │ col1  │ col2       │ col2_a │ col2_b │ col2_c │
│     │ Int64 │ Array…     │ Bool   │ Bool   │ Bool   │
├─────┼───────┼────────────┼────────┼────────┼────────┤
│ 1   │ 102   │ ["a"]      │ 1      │ 0      │ 0      │
│ 2   │ 103   │ ["a", "b"] │ 1      │ 1      │ 0      │
│ 3   │ 104   │ ["c", "b"] │ 0      │ 1      │ 1      │

There is indeed no one-hot encoding function in DataFrames.jl - I would argue that this is sensible, as this is a particular machine learning transformation that should live in a an ML package rather than in a basic DataFrames package.

You've got two options I think:

1. Use an ML package that does this for you, e.g. MLJ.jl. In MLJ, the OneHotEncoder is a model that transforms any table with Finite features in it into a one-hot encoded version of itself, see the docs here

2. Use a regression package that automatically generates dummy columns for categorical variables using the StatsModels @formula API - if you fit a regression with e.g. GLM.jl and your formula is @formula(y ~ x) where x is a a categorical variable, the model matrix will automatically be constructed by contrast coding x, i.e. having binary dummy columns for all but one level of x

For the second option, you ideally want your data to be categorical (although strings will work as well), and for this DataFrames.jl includes the categorical! function.

EDIT 17/11/2021: There has since been a definitive thread on this on the Julia Discourse which contains an extensive list of suggestions for doing one-hot encoding: https://discourse.julialang.org/t/all-the-ways-to-do-one-hot-encoding/

Sharing my favourite from there:

julia> x = [1, 2, 1, 3, 2];

julia> unique(x) .== permutedims(x)
3×5 BitMatrix:
 1  0  1  0  0
 0  1  0  0  1
 0  0  0  1  0