Fix NaN and missing detection in quantile() (#72)

When `sort=false`, we only partially sort the input, so `NaN`/`missing`
is not guaranteed to be in the last position. Also avoid throwing errors for
non-`Number` types, for which `isnan` may not be defined.

src/Statistics.jl

@@ -971,8 +971,10 @@ function _quantilesort!(v::AbstractArray, sorted::Bool, minp::Real, maxp::Real)
         # only need to perform partial sort
         sort!(v, 1, lv, Base.Sort.PartialQuickSort(lo:hi), Base.Sort.Forward)
     end
-    ismissing(v[end]) && throw(ArgumentError("quantiles are undefined in presence of missing values"))
-    isnan(v[end]) && throw(ArgumentError("quantiles are undefined in presence of NaNs"))
+    if (sorted && (ismissing(v[end]) || (v[end] isa Number && isnan(v[end])))) ||
+        any(x -> ismissing(x) || (x isa Number && isnan(x)), v)
+        throw(ArgumentError("quantiles are undefined in presence of NaNs or missing values"))
+    end
     return v
 end
 

test/runtests.jl

@@ -571,8 +571,15 @@ end
     @test quantile(Any[1, Float16(2), 3], Float16(0.5)) isa Float16
     @test quantile(Any[1, big(2), 3], Float16(0.5)) isa BigFloat
 
-    @test_throws ArgumentError quantile([1, missing], 0.5)
-    @test_throws ArgumentError quantile([1, NaN], 0.5)
+    # Need a large vector to actually check consequences of partial sorting
+    x = rand(50)
+    for sorted in (false, true)
+        x[10] = NaN
+        @test_throws ArgumentError quantile(x, 0.5, sorted=sorted)
+        x = Vector{Union{Float64, Missing}}(x)
+        x[10] = missing
+        @test_throws ArgumentError quantile(x, 0.5, sorted=sorted)
+    end
     @test quantile(skipmissing([1, missing, 2]), 0.5) === 1.5
     @test quantile([1], 0.5) === 1.0