Missing Values

Julia has several different ways of representing missing data. If a column of data may contain missing values, JuliaDB supports both missing value representations of Union{T, Missing} and DataValue{T}.

While Union{T, Missing} is the default representation, functions that generate missing values (join) have a missingtype = Missing keyword argument that can be set to DataValue.

• The convertmissing function is used to switch the representation of missing values.

julia> using DataValues

julia> convertmissing(table([1, NA]), Missing)
Table with 2 rows, 1 columns:
1
───────
1
missing

julia> convertmissing(table([1, missing]), DataValue)
Table with 2 rows, 1 columns:
1
───
1
#NA

• The dropmissing function will remove rows that contain Missing or missing DataValues.

julia> dropmissing(table([1, NA]))
Table with 1 rows, 1 columns:
1
─
1

julia> dropmissing(table([1, missing]))
Table with 1 rows, 1 columns:
1
─
1