add :sum to describe

NEWS.md

@@ -13,6 +13,8 @@
   treated as if they were wrapped in `Cols` and does not throw an error
   when a vector of duplicate indices is passed when doing column selection
   ([#3302](https://github.com/JuliaData/DataFrames.jl/pull/3302))
+* `describe` now has `:sum` available as a descriptive statistic.
+  ([#3303](https://github.com/JuliaData/DataFrames.jl/pull/3303))
 
 # DataFrames.jl v1.5 Release Notes
 

src/abstractdataframe/abstractdataframe.jl

@@ -577,11 +577,11 @@ where each row represents a variable and each column a summary statistic.
 - `stats::Union{Symbol, Pair}...` : the summary statistics to report.
   Arguments can be:
     - A symbol from the list `:mean`, `:std`, `:min`, `:q25`,
-      `:median`, `:q75`, `:max`, `:eltype`, `:nunique`, `:nuniqueall`, `:first`,
+      `:median`, `:q75`, `:max`, `:sum`, `:eltype`, `:nunique`, `:nuniqueall`, `:first`,
       `:last`, `:nnonmissing`, and `:nmissing`. The default statistics used are
       `:mean`, `:min`, `:median`, `:max`, `:nmissing`, and `:eltype`.
     - `:detailed` as the only `Symbol` argument to return all statistics
-      except `:first`, `:last`, `:nuniqueall`, and `:nnonmissing`.
+      except `:first`, `:last`, `:sum`, `:nuniqueall`, and `:nnonmissing`.
     - `:all` as the only `Symbol` argument to return all statistics.
     - A `function => name` pair where `name` is a `Symbol` or string. This will
       create a column of summary statistics with the provided name.
@@ -664,7 +664,7 @@ DataAPI.describe(df::AbstractDataFrame; cols=:) =
 function _describe(df::AbstractDataFrame, stats::AbstractVector)
     predefined_funs = Symbol[s for s in stats if s isa Symbol]
 
-    allowed_fields = [:mean, :std, :min, :q25, :median, :q75, :max,
+    allowed_fields = [:mean, :std, :min, :q25, :median, :q75, :max, :sum,
                       :nunique, :nuniqueall, :nmissing, :nnonmissing,
                       :first, :last, :eltype]
 
@@ -799,6 +799,10 @@ function get_stats(@nospecialize(col::Union{AbstractVector, Base.SkipMissing}),
         d[:nuniqueall] = try length(Set(col)) catch end
     end
 
+    if :sum in stats
+        d[:sum] = try sum(col) catch end
+    end
+
     return d
 end
 

test/dataframe.jl

@@ -548,6 +548,7 @@ end
                                 median=[2.5, 2.0, nothing, nothing, VERSION >= v"1.7.0-beta1.2" ? Date(2002) : nothing, nothing],
                                 q75=[3.25, 2.5, nothing, nothing, nothing, nothing],
                                 max=[4.0, 3.0, "d", "c", Date(2004), 2],
+                                sum=[10, 6, nothing, nothing, nothing, nothing],
                                 nunique=[nothing, nothing, 4, 3, 4, 2],
                                 nuniqueall=[4, 3, 4, 3, 4, 2],
                                 nmissing=[0, 1, 0, 1, 0, 0],
@@ -594,8 +595,8 @@ end
     @test describe(df, cols=Not(1)) ≅ describe(select(df, Not(1)))
     @test describe(df, cols=Not("a")) ≅ describe(select(df, Not(1)))
 
-    @test describe(DataFrame(a=[1, 2]), cols=:a, :min, minimum => :min2, maximum => "max2", :max) ==
-          DataFrame(variable=:a, min=1, min2=1, max2=2, max=2)
+    @test describe(DataFrame(a=[1, 2]), cols=:a, :min, minimum => :min2, maximum => "max2", :max, :sum) ==
+          DataFrame(variable=:a, min=1, min2=1, max2=2, max=2, sum=3)
 
     @test_throws ArgumentError describe(df, :mean, :all)
     @test_throws MethodError describe(DataFrame(a=[1, 2]), cols=:a, "max2" => maximum)