JuliaData · bkamins · Aug 30, 2026 · Aug 30, 2026 · Aug 30, 2026 · Aug 30, 2026
diff --git a/docs/src/man/basics.md b/docs/src/man/basics.md
diff --git a/docs/src/man/comparisons.md b/docs/src/man/comparisons.md
@@ -146,7 +146,7 @@ Name: x, dtype: int64
 
 For DataFrames.jl, it looks like this:
 
-```julia
+```julia-repl
 julia> combine(groupby(df, :grp), :x => mean)
 2×2 DataFrame
  Row │ grp    x_mean

diff --git a/docs/src/man/customizing_output.md b/docs/src/man/customizing_output.md
@@ -40,9 +40,7 @@ julia> df = DataFrame(
            c = ["short", "a very very very very very long string", "ok"]
        );
 
-julia> # This is the default output.
-
-julia> df
+julia> df   # This is the default output.
 3×3 DataFrame
  Row │ a      b        c
      │ Int64  Float64  String
@@ -62,9 +60,7 @@ julia> show(df; truncate = 0)
    2 │     2    -1.2   a very very very very very long string
    3 │     3    42.0   ok
 
-julia> # Hide row numbers.
-
-julia> show(df; show_row_number = false)
+julia> show(df; show_row_number = false)    # Hide row numbers.
 3×3 DataFrame
  a      b        c
  Int64  Float64  String
@@ -73,9 +69,7 @@ julia> show(df; show_row_number = false)
      2    -1.2   a very very very very very long …
      3    42.0   ok
 
-julia> # Hide the column element types in text output.
-
-julia> show(df; eltypes = false)
+julia> show(df; eltypes = false)    # Hide the column element types in text output.
 3×3 DataFrame
  Row │ a  b      c
 ─────┼─────────────────────────────────────────────
@@ -119,9 +113,7 @@ julia> df = DataFrame(
            E = [ 0.26, -1.67,  2.22, -0.75,  1.05, -0.48, -2.93]
        );
 
-julia> # This is the default output.
-
-julia> df
+julia> df   # This is the default output.
 7×5 DataFrame
  Row │ A        B        C        D        E
      │ Float64  Float64  Float64  Float64  Float64 
@@ -157,15 +149,10 @@ julia> show(df; formatters = [parentheses_fmt])
 The color of the cells can be changed using highlighters. The following example shows how to
 highlight negative values in red in HTML output (e.g. in Jupyter).
 
-```julia
+```julia-repl
 julia> hl = HtmlHighlighter((data, i, j) -> data[i, j] < 0, ["color" => "red"]);
 
-julia> show(
-           stdout,
-           MIME("text/html"),
-           df;
-           highlighters = [hl]
-       )
+julia> show(stdout, MIME("text/html"), df; highlighters = [hl])
 ```
 
 You can also add summary rows at the bottom of a table using PrettyTables.jl keywords.  Pass
@@ -211,4 +198,4 @@ julia> show(
 ```
 
 For more customization options, check the
-[PrettyTables.jl documentation](https://ronisbr.github.io/PrettyTables.jl/stable/).
+[PrettyTables.jl documentation](https://ronisbr.github.io/PrettyTables.jl/stable/).
diff --git a/docs/src/man/joins.md b/docs/src/man/joins.md
@@ -481,7 +481,7 @@ Note that in this case the order and number of rows in the left table is not
 changed. Therefore, in particular, it is not allowed to have duplicate keys
 in the right table:
 
-```
+```julia-repl
 julia> leftjoin!(main, DataFrame(id=[2, 2], info_bad=["a", "b"]), on=:id)
 ERROR: ArgumentError: duplicate rows found in right table
 ```
diff --git a/docs/src/man/split_apply_combine.md b/docs/src/man/split_apply_combine.md
@@ -323,7 +323,7 @@ julia> combine(iris_gdf, :PetalLength => (x -> [extrema(x)]) => [:min, :max])
 ```
 
 To get row number for each observation within each group use the `eachindex` function:
-```
+```jldoctest sac
 julia> combine(iris_gdf, eachindex)
 150×2 DataFrame
  Row │ Species         eachindex
@@ -332,48 +332,74 @@ julia> combine(iris_gdf, eachindex)
    1 │ Iris-setosa             1
    2 │ Iris-setosa             2
    3 │ Iris-setosa             3
+   4 │ Iris-setosa             4
+   5 │ Iris-setosa             5
+   6 │ Iris-setosa             6
+   7 │ Iris-setosa             7
+   8 │ Iris-setosa             8
   ⋮  │       ⋮             ⋮
+ 144 │ Iris-virginica         44
+ 145 │ Iris-virginica         45
+ 146 │ Iris-virginica         46
+ 147 │ Iris-virginica         47
  148 │ Iris-virginica         48
  149 │ Iris-virginica         49
  150 │ Iris-virginica         50
-                 144 rows omitted
+                 135 rows omitted
 ```
 
 Contrary to `combine`, the `select` and `transform` functions always return
 a data frame with the same number and order of rows as the source.
 In the example below
 the return values in columns `:SepalLength_SepalWidth_cor` and `:nrow` are
 broadcasted to match the number of elements in each group:
-```
+
+```jldoctest sac
 julia> select(iris_gdf, 1:2 => cor)
 150×2 DataFrame
  Row │ Species         SepalLength_SepalWidth_cor
-     │ String          Float64
+     │ String15        Float64
 ─────┼────────────────────────────────────────────
    1 │ Iris-setosa                       0.74678
    2 │ Iris-setosa                       0.74678
    3 │ Iris-setosa                       0.74678
    4 │ Iris-setosa                       0.74678
+   5 │ Iris-setosa                       0.74678
+   6 │ Iris-setosa                       0.74678
+   7 │ Iris-setosa                       0.74678
+   8 │ Iris-setosa                       0.74678
   ⋮  │       ⋮                     ⋮
+ 144 │ Iris-virginica                    0.457228
+ 145 │ Iris-virginica                    0.457228
+ 146 │ Iris-virginica                    0.457228
+ 147 │ Iris-virginica                    0.457228
  148 │ Iris-virginica                    0.457228
  149 │ Iris-virginica                    0.457228
  150 │ Iris-virginica                    0.457228
-                                  143 rows omitted
+                                  135 rows omitted
 
 julia> transform(iris_gdf, :Species => x -> chop.(x, head=5, tail=0))
 150×6 DataFrame
- Row │ SepalLength  SepalWidth  PetalLength  PetalWidth  Species         Species_function
-     │ Float64      Float64     Float64      Float64     String          SubString…
-─────┼────────────────────────────────────────────────────────────────────────────────────
-   1 │         5.1         3.5          1.4         0.2  Iris-setosa     setosa
-   2 │         4.9         3.0          1.4         0.2  Iris-setosa     setosa
-   3 │         4.7         3.2          1.3         0.2  Iris-setosa     setosa
-   4 │         4.6         3.1          1.5         0.2  Iris-setosa     setosa
-  ⋮  │      ⋮           ⋮            ⋮           ⋮             ⋮                ⋮
- 148 │         6.5         3.0          5.2         2.0  Iris-virginica  virginica
- 149 │         6.2         3.4          5.4         2.3  Iris-virginica  virginica
- 150 │         5.9         3.0          5.1         1.8  Iris-virginica  virginica
-                                                                          143 rows omitted
+ Row │ SepalLength  SepalWidth  PetalLength  PetalWidth  Species         Speci ⋯
+     │ Float64      Float64     Float64      Float64     String15        Strin ⋯
+─────┼──────────────────────────────────────────────────────────────────────────
+   1 │         5.1         3.5          1.4         0.2  Iris-setosa     setos ⋯
+   2 │         4.9         3.0          1.4         0.2  Iris-setosa     setos
+   3 │         4.7         3.2          1.3         0.2  Iris-setosa     setos
+   4 │         4.6         3.1          1.5         0.2  Iris-setosa     setos
+   5 │         5.0         3.6          1.4         0.2  Iris-setosa     setos ⋯
+   6 │         5.4         3.9          1.7         0.4  Iris-setosa     setos
+   7 │         4.6         3.4          1.4         0.3  Iris-setosa     setos
+   8 │         5.0         3.4          1.5         0.2  Iris-setosa     setos
+  ⋮  │      ⋮           ⋮            ⋮           ⋮             ⋮               ⋱
+ 144 │         6.8         3.2          5.9         2.3  Iris-virginica  virgi ⋯
+ 145 │         6.7         3.3          5.7         2.5  Iris-virginica  virgi
+ 146 │         6.7         3.0          5.2         2.3  Iris-virginica  virgi
+ 147 │         6.3         2.5          5.0         1.9  Iris-virginica  virgi
+ 148 │         6.5         3.0          5.2         2.0  Iris-virginica  virgi ⋯
+ 149 │         6.2         3.4          5.4         2.3  Iris-virginica  virgi
+ 150 │         5.9         3.0          5.1         1.8  Iris-virginica  virgi
+                                                   1 column and 135 rows omitted
 ```
 
 All functions also support the `do` block form. However, as noted above,
@@ -730,7 +756,7 @@ syntax in `combine`, `select` or `transform` will usually be faster for large
 `GroupedDataFrame` objects than iterating them, with the difference that they
 produce a data frame. An operation corresponding to the example above is:
 
-```
+```julia-repl
 julia> combine(iris_gdf, nrow)
 3×2 DataFrame
  Row │ Species          nrow
@@ -1311,7 +1337,7 @@ julia> keys(groupby(df, :volume))
 
 If you want to have them sorted in ascending order pass `sort=true`:
 
-```
+```julia-repl
 julia> keys(groupby(df, :volume, sort=true))
 7-element DataFrames.GroupKeys{GroupedDataFrame{DataFrame}}:
  GroupKey: (volume = 1,)
@@ -1328,7 +1354,7 @@ by or pass a named tuple as `sort` keyword argument containing one or more of
 `alg`, `lt`, `by`, `rev`, and `order` fields that will be treated just like in
 [`sortperm`](@ref):
 
-```
+```julia-repl
 julia> keys(groupby(df, [:customer_id, order(:volume, rev=true)]))
 6-element DataFrames.GroupKeys{GroupedDataFrame{DataFrame}}:
  GroupKey: (customer_id = "a", volume = 2)

diff --git a/docs/src/man/working_with_dataframes.md b/docs/src/man/working_with_dataframes.md
@@ -603,7 +603,7 @@ julia> df[:, :x1]
 
 By default `select` copies columns of a passed source data frame.
 In order to avoid copying, pass `copycols=false`:
-```
+```jldoctest dataframe
 julia> df2 = select(df, :x1)
 2×1 DataFrame
  Row │ x1
@@ -668,7 +668,7 @@ julia> transform(df, All() => +)
 Using the `ByRow` wrapper, we can easily compute for each row the name of column
 with the highest score:
 
-```
+```julia-repl
 julia> using Random
 
 julia> Random.seed!(1);
@@ -709,8 +709,8 @@ julia> transform(df, AsTable(:) => ByRow(argmax) => :prediction)
 In the most complex example below we compute row-wise sum, number of
 elements, and mean, while ignoring missing values.
 
-```
-julia> using Statistics
+```jldoctest
+julia> using DataFrames, Statistics
 
 julia> df = DataFrame(x=[1, 2, missing], y=[1, missing, missing])
 3×2 DataFrame
@@ -844,7 +844,7 @@ want to focus on the most common usage patterns.
 A `DataFrame` can store values of any type as its columns, for example
 below we show how one can store a `Tuple`:
 
-```
+```jldoctest dataframe
 julia> df2 = combine(df, All() .=> extrema)
 1×2 DataFrame
  Row │ A_extrema  B_extrema
@@ -858,7 +858,7 @@ minima and maxima. This can be achieved by passing multiple columns for the outp
 Here is an example of how this can be done by writing the column names by-hand for a single
 input column:
 
-```
+```jldoctest dataframe
 julia> combine(df2, "A_extrema" => identity => ["A_min", "A_max"])
 1×2 DataFrame
  Row │ A_min  A_max
@@ -869,7 +869,7 @@ julia> combine(df2, "A_extrema" => identity => ["A_min", "A_max"])
 
 You can extend it to handling all columns in `df2` using broadcasting:
 
-```
+```jldoctest dataframe
 julia> combine(df2, All() .=> identity .=> [["A_min", "A_max"], ["B_min", "B_max"]])
 1×4 DataFrame
  Row │ A_min  A_max  B_min    B_max
@@ -882,7 +882,7 @@ This approach works, but can be improved. Instead of writing all the column name
 manually we can instead use a function as a way to specify target column names
 based on source column names:
 
-```
+```jldoctest dataframe
 julia> combine(df2, All() .=> identity .=> c -> first(c) .* ["_min", "_max"])
 1×4 DataFrame
  Row │ A_min  A_max  B_min    B_max
@@ -898,7 +898,7 @@ treated as a transformation and not as a rule for target column names generation
 You might want to perform the transformation of the source data frame into the result
 we have just shown in one step. This can be achieved with the following expression:
 
-```
+```jldoctest dataframe
 julia> combine(df, All() .=> Ref∘extrema .=> c -> c .* ["_min", "_max"])
 1×4 DataFrame
  Row │ A_min  A_max  B_min    B_max
@@ -912,10 +912,9 @@ Without `Ref`, `combine` iterates the contents of the value returned by the oper
 which in our case is a tuple of numbers, and tries to expand it assuming that each produced value represents one row,
 so one gets an error:
 
-```
+```jldoctest dataframe
 julia> combine(df, All() .=> extrema .=> [c -> c .* ["_min", "_max"]])
-ERROR: ArgumentError: 'Tuple{Int64, Int64}' iterates 'Int64' values,
-which doesn't satisfy the Tables.jl `AbstractRow` interface
+ERROR: ArgumentError: 'Tuple{Int64, Int64}' iterates 'Int64' values, which doesn't satisfy the Tables.jl `AbstractRow` interface
 ```
 
 Note that we used `Ref` as it is a container that is typically used in DataFrames.jl when one

diff --git a/src/abstractdataframe/io.jl b/src/abstractdataframe/io.jl
@@ -23,9 +23,9 @@ implicit row ID column contained in every `AbstractDataFrame`.
 # Arguments
 - `df::AbstractDataFrame`: The data frame whose columns will be printed.
 - `io::IO`: The `IO` to which `df` is to be printed
-- `rowindices1::AbstractVector{Int}: A set of indices of the first
+- `rowindices1::AbstractVector{Int}`: A set of indices of the first
   chunk of the AbstractDataFrame that would be rendered to IO.
-- `rowindices2::AbstractVector{Int}: A set of indices of the second
+- `rowindices2::AbstractVector{Int}`: A set of indices of the second
   chunk of the AbstractDataFrame that would be rendered to IO. Can
   be empty if the AbstractDataFrame would be printed without any
   ellipses.

diff --git a/src/abstractdataframe/selectionfast.jl b/src/abstractdataframe/selectionfast.jl
@@ -43,7 +43,7 @@ scenarios even standard aggregation functions should not be considered to
 provide reliable output, and users are recommended to switch to higher precision
 calculations. An example of a case when standard `sum` is affected by the
 situation discussed is:
-```
+```julia-repl
 julia> sum(Any[typemax(Int), typemax(Int), 1.0])
 -1.0
 

diff --git a/src/other/metadata.jl b/src/other/metadata.jl
@@ -97,7 +97,6 @@ See also: [`metadatakeys`](@ref), [`metadata!`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $TABLEMETA_EXAMPLE
-```
 """
 function metadata(df::DataFrame, key::AbstractString,
                   default=MetadataMissingDefault(); style::Bool=false)
@@ -186,7 +185,6 @@ See also: [`metadata`](@ref), [`metadatakeys`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $TABLEMETA_EXAMPLE
-```
 """
 function metadata!(df::DataFrame, key::AbstractString, value::Any;
                    style::Symbol=:default)
@@ -245,7 +243,6 @@ See also: [`metadata`](@ref), [`metadatakeys`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $TABLEMETA_EXAMPLE
-```
 """
 function deletemetadata!(df::DataFrame, key::AbstractString)
     meta = getfield(df, :metadata)
@@ -355,7 +352,6 @@ See also: [`metadata`](@ref), [`metadatakeys`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $COLMETADATA_EXAMPLE
-```
 """
 function colmetadata(df::DataFrame, col::ColumnIndex, key::AbstractString,
                      default=MetadataMissingDefault(); style::Bool=false)
@@ -421,7 +417,6 @@ See also: [`metadata`](@ref), [`metadatakeys`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $COLMETADATA_EXAMPLE
-```
 """
 function colmetadatakeys(df::DataFrame, col::ColumnIndex)
     idx = index(df)[col] # check if column exists and get its integer index
@@ -483,7 +478,6 @@ See also: [`metadata`](@ref), [`metadatakeys`](@ref),
 [`deletecolmetadata!`](@ref), [`emptycolmetadata!`](@ref).
 
 $COLMETADATA_EXAMPLE
-```
 """
 function colmetadata!(df::DataFrame, col::ColumnIndex, key::AbstractString, value::Any;
                       style::Symbol=:default)