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update docs following CSV.jl 0.9 release (#2865)
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‎docs/src/man/basics.md‎

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‎docs/src/man/querying_frameworks.md‎

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@@ -24,10 +24,10 @@ The major benefit of the package is that it allows you to refer to columns of a
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expressions. Additionally you can chain a sequence of transformations of a
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`DataFrame` using the `@linq` macro.
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When a DataFramesMeta.jl macro such as `@select`, `@transform`, `@by`, `@combine`,
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When a DataFramesMeta.jl macro such as `@select`, `@transform`, `@by`, `@combine`,
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`@where`, or `@orderby` is called inside a `@linq` block, you can omit
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the `@`. Therefore `transform` inside `@linq` is not the same as `transform`
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outside of a `@linq` block.
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outside of a `@linq` block.
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Here is a minimal example of usage of the package. Observe that we refer to
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names of columns using only their names and that chaining is performed using the

‎docs/src/man/reshaping_and_pivoting.md‎

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@@ -10,7 +10,7 @@ julia> iris = CSV.read((joinpath(dirname(pathof(DataFrames)),
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DataFrame)
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 5.1 3.5 1.4 0.2 Iris-setosa
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2 │ 4.9 3.0 1.4 0.2 Iris-setosa
@@ -33,7 +33,7 @@ julia> iris = CSV.read((joinpath(dirname(pathof(DataFrames)),
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julia> stack(iris, 1:4)
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600×3 DataFrame
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Row │ Species variable value
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│ String String Float64
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│ String15 String Float64
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─────┼──────────────────────────────────────
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1 │ Iris-setosa SepalLength 5.1
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2 │ Iris-setosa SepalLength 4.9
@@ -62,7 +62,7 @@ be given:
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julia> stack(iris, [:SepalLength, :SepalWidth, :PetalLength, :PetalWidth])
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600×3 DataFrame
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Row │ Species variable value
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│ String String Float64
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│ String15 String Float64
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─────┼──────────────────────────────────────
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1 │ Iris-setosa SepalLength 5.1
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2 │ Iris-setosa SepalLength 4.9
@@ -100,7 +100,7 @@ the long format:
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julia> stack(iris, [:SepalLength, :SepalWidth], :Species)
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300×3 DataFrame
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Row │ Species variable value
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│ String String Float64
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│ String15 String Float64
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─────┼──────────────────────────────────────
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1 │ Iris-setosa SepalLength 5.1
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2 │ Iris-setosa SepalLength 4.9
@@ -127,7 +127,7 @@ If you prefer to specify the id columns then use `Not` with `stack` like this:
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julia> stack(iris, Not(:Species))
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600×3 DataFrame
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Row │ Species variable value
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│ String String Float64
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│ String15 String Float64
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─────┼──────────────────────────────────────
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1 │ Iris-setosa SepalLength 5.1
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2 │ Iris-setosa SepalLength 4.9
@@ -159,7 +159,7 @@ julia> iris.id = 1:size(iris, 1)
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julia> longdf = stack(iris, Not([:Species, :id]))
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600×4 DataFrame
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Row │ Species id variable value
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│ String Int64 String Float64
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│ String15 Int64 String Float64
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─────┼─────────────────────────────────────────────
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1 │ Iris-setosa 1 SepalLength 5.1
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2 │ Iris-setosa 2 SepalLength 4.9
@@ -209,7 +209,7 @@ If the remaining columns are unique, you can skip the id variable and use:
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julia> unstack(longdf, :variable, :value)
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150×6 DataFrame
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Row │ Species id SepalLength SepalWidth PetalLength PetalWidth ⋯
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│ String Int64 Float64? Float64? Float64? Float64? ⋯
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│ String15 Int64 Float64? Float64? Float64? Float64? ⋯
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─────┼──────────────────────────────────────────────────────────────────────────
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1 │ Iris-setosa 1 5.1 3.5 1.4 0.2 ⋯
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2 │ Iris-setosa 2 4.9 3.0 1.4 0.2
@@ -236,7 +236,7 @@ arguments, as they will be used by default, and write:
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julia> unstack(longdf)
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150×6 DataFrame
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Row │ Species id SepalLength SepalWidth PetalLength PetalWidth ⋯
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│ String Int64 Float64? Float64? Float64? Float64? ⋯
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│ String15 Int64 Float64? Float64? Float64? Float64? ⋯
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─────┼──────────────────────────────────────────────────────────────────────────
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1 │ Iris-setosa 1 5.1 3.5 1.4 0.2 ⋯
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2 │ Iris-setosa 2 4.9 3.0 1.4 0.2
@@ -264,7 +264,7 @@ the original wide data frame. Here is an example:
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julia> stack(iris, view=true)
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600×4 DataFrame
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Row │ Species id variable value
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│ String Int64 String Float64
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│ String15 Int64 String Float64
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─────┼─────────────────────────────────────────────
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1 │ Iris-setosa 1 SepalLength 5.1
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2 │ Iris-setosa 2 SepalLength 4.9
@@ -306,7 +306,7 @@ julia> using Statistics
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julia> d = stack(iris, Not(:Species))
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750×3 DataFrame
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Row │ Species variable value
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│ String String Float64
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│ String15 String Float64
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─────┼──────────────────────────────────────
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1 │ Iris-setosa SepalLength 5.1
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2 │ Iris-setosa SepalLength 4.9
@@ -329,7 +329,7 @@ julia> d = stack(iris, Not(:Species))
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julia> x = combine(groupby(d, [:variable, :Species]), :value => mean => :vsum)
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15×3 DataFrame
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Row │ variable Species vsum
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│ String String Float64
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│ String String15 Float64
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─────┼───────────────────────────────────────
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1 │ SepalLength Iris-setosa 5.006
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2 │ SepalLength Iris-versicolor 5.936

‎docs/src/man/sorting.md‎

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@@ -10,7 +10,7 @@ julia> iris = CSV.read((joinpath(dirname(pathof(DataFrames)),
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DataFrame)
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 5.1 3.5 1.4 0.2 Iris-setosa
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2 │ 4.9 3.0 1.4 0.2 Iris-setosa
@@ -33,7 +33,7 @@ julia> iris = CSV.read((joinpath(dirname(pathof(DataFrames)),
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julia> sort!(iris)
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 4.3 3.0 1.1 0.1 Iris-setosa
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2 │ 4.4 2.9 1.4 0.2 Iris-setosa
@@ -65,7 +65,7 @@ Here are some examples showing most of the possible options:
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julia> sort!(iris, rev = true)
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 7.9 3.8 6.4 2.0 Iris-virginica
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2 │ 7.7 3.8 6.7 2.2 Iris-virginica
@@ -88,7 +88,7 @@ julia> sort!(iris, rev = true)
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julia> sort!(iris, [:Species, :SepalWidth])
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 4.5 2.3 1.3 0.3 Iris-setosa
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2 │ 4.4 2.9 1.4 0.2 Iris-setosa
@@ -111,7 +111,7 @@ julia> sort!(iris, [:Species, :SepalWidth])
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julia> sort!(iris, [order(:Species, by=length), order(:SepalLength, rev=true)])
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼───────────────────────────────────────────────────────────────────
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1 │ 5.8 4.0 1.2 0.2 Iris-setosa
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2 │ 5.7 3.8 1.7 0.3 Iris-setosa
@@ -148,7 +148,7 @@ rows will be sorted by increasing `:PetalLength`:
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julia> sort!(iris, [:Species, :PetalLength], rev=(true, false))
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 4.9 2.5 4.5 1.7 Iris-virginica
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2 │ 6.2 2.8 4.8 1.8 Iris-virginica
@@ -171,7 +171,7 @@ julia> sort!(iris, [:Species, :PetalLength], rev=(true, false))
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julia> sort!(iris, [order(:Species, rev=true), :PetalLength])
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150×5 DataFrame
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Row │ SepalLength SepalWidth PetalLength PetalWidth Species
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│ Float64 Float64 Float64 Float64 String
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│ Float64 Float64 Float64 Float64 String15
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─────┼──────────────────────────────────────────────────────────────────
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1 │ 4.9 2.5 4.5 1.7 Iris-virginica
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2 │ 6.2 2.8 4.8 1.8 Iris-virginica

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