dropmissing! fails when one column in DataFrame is of type bitvector

[KevDAll]

DataFrames.jl Version 1.5.0.
Julia Version 1.6.1

When one or more columns in an array are of type bitvector, dropmissing! will fail.

The issue appears to be caused at line 922 of dataframe.jl (_deleteat!_helper)

deleteat!(col, drop)

The problem here is that bitarray.deleteat!(B::Bitvector, inds) takes precedence over array.deleteat!(a::Vector, inds::AbstractVector{Bool}).

Version 1.7.0 of base adds the signature bitarray.deleteat!(B::Bitvector, inds::AbstractVector{Bool}) which will fix the issue.

An alternative would be update the project.toml to better indicate the compatibility with Base.

Minimal Example:

Expected Behavior (occurs if B::Vector{Bool}):

x = DataFrame(:A=>[1,2,missing], :B=>[false, false, false])
dropmissing!(x)
print(x)

2×2 DataFrame
 Row │ A      B
     │ Int64  Bool
─────┼──────────────
   1 │     1  false
   2 │     2  false

Observed Behavior (occurs if B::BitArray):

x = DataFrame(:A=>[1,2,missing], :B=>falses(3))
dropmissing!(x)
print(x)

ERROR: BoundsError: attempt to access 3-element BitVector at index [false]
Stacktrace:
 [1] throw_boundserror(A::BitVector, I::Tuple{Bool})
   @ Base ./abstractarray.jl:651
 [2] checkbounds
   @ ./abstractarray.jl:616 [inlined]
 [3] deleteat!(B::BitVector, inds::BitVector)
   @ Base ./bitarray.jl:989
 [4] _deleteat!_helper(df::DataFrame, drop::BitVector)
   @ DataFrames ~/.julia/packages/DataFrames/LteEl/src/dataframe/dataframe.jl:922
 [5] deleteat!
   @ ~/.julia/packages/DataFrames/LteEl/src/dataframe/dataframe.jl:894 [inlined]
 [6] dropmissing!(df::DataFrame, cols::Colon; disallowmissing::Bool)
   @ DataFrames ~/.julia/packages/DataFrames/LteEl/src/abstractdataframe/abstractdataframe.jl:1081
 [7] dropmissing! (repeats 2 times)
   @ ~/.julia/packages/DataFrames/LteEl/src/abstractdataframe/abstractdataframe.jl:1079 [inlined]
 [8] top-level scope
   @ REPL[12]:1

[bkamins]

The issue is that JuliaLang/julia#42144 was not backported to 1.6 LTS. Let us wait for Julia maintainers to decide what to do about it (in general - you are hitting a bug in Julia not in DataFrames.jl).

CC @KristofferC