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[BREAKING] Return 0 missing count for columns of nonmissing type #2360

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Aug 13, 2020
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[BREAKING] Return 0 missing count for columns of nonmissing type #2360

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Return 0 missing count for columns of nonmissing type
nilshg Aug 11, 2020
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Change dosctring
nilshg Aug 11, 2020
ffda21b
Delete settings.json
nilshg Aug 11, 2020
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3 changes: 3 additions & 0 deletions .vscode/settings.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,3 @@
{
"julia.environmentPath": "c:\\Users\\ngudat\\.julia\\dev\\DataFrames.jl"
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}
15 changes: 6 additions & 9 deletions src/abstractdataframe/abstractdataframe.jl
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Expand Up @@ -476,10 +476,7 @@ number of unique values in a column. If a column's base type derives from `Real`
`:nunique` will return `nothing`s.

Missing values are filtered in the calculation of all statistics, however the
column `:nmissing` will report the number of missing values of that variable. If
the column does not allow missing values, `nothing` is returned. Consequently,
`nmissing = 0` indicates that the column allows missing values, but does not
currently contain any.
column `:nmissing` will report the number of missing values of that variable.

If custom functions are provided, they are called repeatedly with the vector
corresponding to each column as the only argument. For columns allowing for
Expand All @@ -494,11 +491,11 @@ julia> df = DataFrame(i=1:10, x=0.1:0.1:1.0, y='a':'j');
julia> describe(df)
3×7 DataFrame
│ Row │ variable │ mean │ min │ median │ max │ nmissing │ eltype │
│ │ Symbol │ Union… │ Any │ Union… │ Any │ Nothing │ DataType │
│ │ Symbol │ Union… │ Any │ Union… │ Any │ Int64 │ DataType │
├─────┼──────────┼────────┼─────┼────────┼─────┼──────────┼──────────┤
│ 1 │ i │ 5.5 │ 1 │ 5.5 │ 10 │ │ Int64 │
│ 2 │ x │ 0.55 │ 0.1 │ 0.55 │ 1.0 │ │ Float64 │
│ 3 │ y │ │ 'a' │ │ 'j' │ │ Char │
│ 1 │ i │ 5.5 │ 1 │ 5.5 │ 10 │ 0 │ Int64 │
│ 2 │ x │ 0.55 │ 0.1 │ 0.55 │ 1.0 │ 0 │ Float64 │
│ 3 │ y │ │ 'a' │ │ 'j' │ 0 │ Char │

julia> describe(df, :min, :max)
3×3 DataFrame
Expand Down Expand Up @@ -580,7 +577,7 @@ function _describe(df::AbstractDataFrame, stats::AbstractVector)
end

if :nmissing in predefined_funs
d[:nmissing] = eltype(col) >: Missing ? count(ismissing, col) : nothing
d[:nmissing] = count(ismissing, col)
end

if :first in predefined_funs
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2 changes: 1 addition & 1 deletion test/dataframe.jl
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Expand Up @@ -640,7 +640,7 @@ end
q75 = [3.25, 2.5, nothing, nothing, nothing, nothing],
max = [4.0, 3.0, "d", "c", Date(2004), 2],
nunique = [nothing, nothing, 4, 3, 4, 2],
nmissing = [nothing, 1, nothing, 1, nothing, nothing],
nmissing = [0, 1, 0, 1, 0, 0],
first = [1, 1, "a", "a", Date(2000), 1],
last = [4, missing, "d", missing, Date(2004), 2],
eltype = [Int, Union{Missing, Int}, String,
Expand Down