Add missing triple quotes around docstrings (#3194)

Author: bkamins

src/abstractdataframe/abstractdataframe.jl

@@ -50,6 +50,7 @@ selector (this is useful in particular with regular expressions, `Cols`, `Not`,
 See also [`propertynames`](@ref) which returns a `Vector{Symbol}`.
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(x1=[1, missing, missing], x2=[3, 2, 4], x3=[3, missing, 2], x4=Union{Int, Missing}[2, 4, 4])
 3×4 DataFrame
@@ -154,6 +155,7 @@ when a column is renamed, its `:note`-style metadata becomes associated to its n
 See also: [`rename`](@ref)
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1, x=2, y=3)
 1×3 DataFrame
@@ -298,6 +300,7 @@ new name.
 See also: [`rename!`](@ref)
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1, x=2, y=3)
 1×3 DataFrame
@@ -359,6 +362,7 @@ and `2` corresponds to columns.
 See also: [`nrow`](@ref), [`ncol`](@ref)
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(a=1:3, b='a':'c');
 
@@ -610,6 +614,7 @@ access missing values.
 Metadata: this function drops all metadata.
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1:10, x=0.1:0.1:1.0, y='a':'j');
 
@@ -1087,6 +1092,7 @@ $METADATA_FIXED
 See also: [`filter!`](@ref)
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(x=[3, 1, 2, 1], y=["b", "c", "a", "b"])
 4×2 DataFrame
@@ -1216,6 +1222,7 @@ $METADATA_FIXED
 See also: [`filter`](@ref)
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(x=[3, 1, 2, 1], y=["b", "c", "a", "b"])
 4×2 DataFrame
@@ -1353,6 +1360,7 @@ See also [`unique`](@ref) and [`unique!`](@ref).
   returns at least one column if `df` has at least one column.
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1:4, x=[1, 2, 1, 2])
 4×2 DataFrame
@@ -1446,6 +1454,7 @@ $METADATA_FIXED
 See also: [`unique!`](@ref), [`nonunique`](@ref).
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1:4, x=[1, 2, 1, 2])
 4×2 DataFrame
@@ -1520,6 +1529,7 @@ $METADATA_FIXED
 See also: [`unique!`](@ref), [`nonunique`](@ref).
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(i=1:4, x=[1, 2, 1, 2])
 4×2 DataFrame
@@ -1582,6 +1592,7 @@ duplicates are allowed.
 $METADATA_FIXED
 
 # Examples
+
 ```jldoctest
 julia> df = DataFrame(x=1:2, y='a':'b', z=["x", "y"])
 2×3 DataFrame
@@ -2812,6 +2823,8 @@ $METADATA_FIXED
 Metadata having other styles is dropped (from parent data frame when `df` is a `SubDataFrame`).
 
 # Examples
+
+```jldoctest
 julia> df = DataFrame(a=1:5, b=6:10, c=11:15)
 5×3 DataFrame
  Row │ a      b      c
@@ -2833,6 +2846,7 @@ julia> permute!(df, [5, 3, 1, 2, 4])
    3 │     1      6     11
    4 │     2      7     12
    5 │     4      9     14
+```
 """
 Base.permute!(df::AbstractDataFrame, p::AbstractVector{<:Integer}) =
     _permutation_helper!(Base.permute!!, df, p)
@@ -2852,6 +2866,7 @@ Metadata having other styles is dropped (from parent data frame when `df` is a `
 
 # Examples
 
+```jldoctest
 julia> df = DataFrame(a=1:5, b=6:10, c=11:15)
 5×3 DataFrame
  Row │ a      b      c
@@ -2884,6 +2899,7 @@ julia> invpermute!(df, [5, 3, 1, 2, 4])
    3 │     3      8     13
    4 │     4      9     14
    5 │     5     10     15
+```
 """
 Base.invpermute!(df::AbstractDataFrame, p::AbstractVector{<:Integer}) =
     _permutation_helper!(Base.invpermute!!, df, p)
@@ -2898,6 +2914,9 @@ $METADATA_FIXED
 
 # Examples
 
+```jldoctest
+julia> using Random
+
 julia> rng = MersenneTwister(1234);
 
 julia> shuffle(rng, DataFrame(a=1:5, b=1:5))
@@ -2910,6 +2929,7 @@ julia> shuffle(rng, DataFrame(a=1:5, b=1:5))
    3 │     4      4
    4 │     3      3
    5 │     5      5
+```
 """
 Random.shuffle(df::AbstractDataFrame) =
     df[randperm(nrow(df)), :]
@@ -2932,6 +2952,9 @@ Metadata having other styles is dropped (from parent data frame when `df` is a `
 
 # Examples
 
+```jldoctest
+julia> using Random
+
 julia> rng = MersenneTwister(1234);
 
 julia> shuffle!(rng, DataFrame(a=1:5, b=1:5))
@@ -2944,6 +2967,7 @@ julia> shuffle!(rng, DataFrame(a=1:5, b=1:5))
    3 │     4      4
    4 │     3      3
    5 │     5      5
+```
 """
 Random.shuffle!(df::AbstractDataFrame) =
     permute!(df, randperm(nrow(df)))