Declaring Missings
Impute.declaremissings
— FunctionImpute.declaremissings(data; values)
DeclareMissings (or replace) various missing data representations with missing
.
Keyword Arguments
value::Tuple
: A tuple of values that should be consideredmissing
Example
julia> using DataFrames, Impute
julia> df = DataFrame(
:a => [1.1, 2.2, NaN, NaN, 5.5],
:b => [1, 2, 3, -9999, 5],
:c => ["v", "w", "x", "y", "NULL"],
)
5×3 DataFrame
Row │ a b c
│ Float64 Int64 String
─────┼────────────────────────
1 │ 1.1 1 v
2 │ 2.2 2 w
3 │ NaN 3 x
4 │ NaN -9999 y
5 │ 5.5 5 NULL
julia> Impute.declaremissings(df; values=(NaN, -9999, "NULL"))
5×3 DataFrame
Row │ a b c
│ Float64? Int64? String?
─────┼─────────────────────────────
1 │ 1.1 1 v
2 │ 2.2 2 w
3 │ missing 3 x
4 │ missing missing y
5 │ 5.5 5 missing
Impute.DeclareMissings
— TypeDeclareMissings(; values)
DeclareMissings (or replace) various missing data values with missing
. This is useful for downstream imputation methods that assume missing data is represented by a missing
.
!!! In-place methods are only applicable for datasets which already allowmissing
.
Keyword Arguments
value::Tuple
: A tuple of values that should be consideredmissing
Example
julia> using Impute: DeclareMissings, apply
julia> M = [1.0 2.0 -9999.0 NaN 5.0; 1.1 2.2 3.3 0.0 5.5]
2×5 Matrix{Float64}:
1.0 2.0 -9999.0 NaN 5.0
1.1 2.2 3.3 0.0 5.5
julia> apply(M, DeclareMissings(; values=(NaN, -9999.0, 0.0)))
2×5 Matrix{Union{Missing, Float64}}:
1.0 2.0 missing missing 5.0
1.1 2.2 3.3 missing 5.5