The Missing Type
Missing is a type implemented by the Missings.jl package to represent missing data. missing is an instance of the type Missing used to represent a missing value.
julia> using DataFrames
julia> missing
missing
julia> typeof(missing)
Missings.Missing
The Missing type lets users create Vectors and DataFrame columns with missing values. Here we create a vector with a missing value and the element-type of the returned vector is Union{Missings.Missing, Int64}.
julia> x = [1, 2, missing]
3-element Array{Union{Missings.Missing, Int64},1}:
1
2
missing
julia> eltype(x)
Union{Missings.Missing, Int64}
julia> Union{Missing, Int}
Union{Missings.Missing, Int64}
julia> eltype(x) == Union{Missing, Int}
true
missing values can be excluded when performing operations by using skipmissing, which returns a memory-efficient iterator.
julia> skipmissing(x)
Missings.EachSkipMissing{Array{Union{$Int, Missings.Missing},1}}(Union{$Int, Missings.Missing}[1, 2, missing])
The output of skipmissing can be passed directly into functions as an argument. For example, we can find the sum of all non-missing values or collect the non-missing values into a new missing-free vector.
julia> sum(skipmissing(x))
3
julia> collect(skipmissing(x))
2-element Array{Int64,1}:
1
2
missing elements can be replaced with other values via Missings.replace.
julia> collect(Missings.replace(x, 1))
3-element Array{Int64,1}:
1
2
1
The function Missings.T returns the element-type T in Union{T, Missing}.
julia> eltype(x)
Union{Int64, Missings.Missing}
julia> Missings.T(eltype(x))
Int64
Use missings to generate Vectors and Arrays supporting missing values, using the optional first argument to specify the element-type.
julia> missings(1)
1-element Array{Missings.Missing,1}:
missing
julia> missings(3)
3-element Array{Missings.Missing,1}:
missing
missing
missing
julia> missings(1, 3)
1×3 Array{Missings.Missing,2}:
missing missing missing
julia> missings(Int, 1, 3)
1×3 Array{Union{Missings.Missing, Int64},2}:
missing missing missing