Rationale for dropmissing vs skipmissing

General Usage
,
1

As outlined in How to remove rows containing missing from DataFrame? the way to get rid of missings in a DataFrame is to use dropmissing. This works really well and I use it all the time, but it bugs me that I cannot use what would to me be more natural, i.e., skipmissing. Is there a logical reason I’m missing for why dropmissing had to be introduced instead of a specialized implementation for skipmissing for a DataFrame?

2

skipmissing has a different indexing rule:

julia> x = [1, 2, missing, 4, 5]
5-element Vector{Union{Missing, Int64}}:
 1
 2
  missing
 4
 5

julia> y = skipmissing(x)
skipmissing(Union{Missing, Int64}[1, 2, missing, 4, 5])

julia> y[2]
2

julia> y[3]
ERROR: MissingException: the value at index (3,) is missing

julia> y[4]
4
5 Likes
3

Thanks for the explanation. :pray:t2: