Metaprogramming tools for DataFrames.jl objects to provide more convenient syntax.

DataFrames.jl has the functions select, transform, and combine, as well as the in-place select! and transform! for manipulating data frames. DataFramesMeta.jl provides the macros @select, @transform, @combine, @select!, and @transform! to mirror these functions with more convenient syntax. Inspired by dplyr in R and LINQ in C#.

In addition, DataFramesMeta provides

  • @orderby, for sorting data frames
  • @subset and @subset!, for keeping rows of a data frame matching a given condition
  • Row-wise versions of the above macros in the form of @rtransform, @rtransform!, @rselect, @rselect!, @rorderby, @rsubset, and @rsubset!.
  • @by, for grouping and combining a data frame in a single step
  • @with, for working with the columns of a data frame with high performance and convenient syntax
  • @eachrow and @eachrow! for looping through rows in data frame, again with high performance and convenient syntax.
  • @byrow for applying functions to each row of a data frame (only supported inside other macros).
  • @passmissing for propagating missing values inside row-wise DataFramesMeta.jl transformations.
  • @astable to create multiple columns within a single transformation.
  • @chain, from Chain.jl for piping the above macros together, similar to magrittr's %>% in R.

See below the convenience of DataFramesMeta compared to DataFrames.

df = DataFrame(a = [1, 2], b = [3, 4]);

# With DataFrames
transform(df, [:a, :b] => ((a, b) -> a .* b .+ first(a) .- sum(b)) => :c);

# With DataFramesMeta
@transform(df, :c = :a .* :b .+ first(:a) .- sum(:b))

To reference columns inside DataFramesMeta macros, use Symbols. For example, use :x to refer to the column df.x. To use a variable varname representing a Symbol to refer to a column, use the syntax $varname.

Use passmissing to propagate missing values more easily. See ?passmissing for details. passmissing is defined in Missings.jl but exported by DataFramesMeta for convenience.

Provided macros


Newer versions of DataFrames.jl support the operators Between, All, Cols, and Not when selecting and transforming columns. DataFramesMeta does not currently support this syntax.

@select and @select!

Column selections and transformations. Only newly created columns are kept. Operates on both a DataFrame and a GroupedDataFrame. Transformations are called with the keyword-like syntax :y = f(:x).

@select returns a new data frame with newly allocated columns, while @select! mutates the original data frame and returns it.

When given a GroupedDataFrame, performs a transformation by group and then if necessary repeats the result to have as many rows as the input data frame.

df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
gd = groupby(df, :x);
@select(df, :x, :y)
@select(df, :x2 = 2 * :x, :y)
@select(gd, :x2 = 2 .* :y .* first(:y))
@select!(df, :x, :y)
@select!(df, :x = 2 * :x, :y)
@select!(gd, :y = 2 .* :y .* first(:y))

@transform and @transform!

Add additional columns based on keyword-like arguments. Operates on both a DataFrame and a GroupedDataFrame. Transformations are called with the keyword-like syntax :y = f(:x).

@transform returns a new data frame with newly allocated columns, while @transform! mutates the original data frame and returns it.

When given a GroupedDataFrame, performs a transformation by group and then if necessary repeats the result to have as many rows as the input data frame.

df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
gd = groupby(df, :x);
@transform(df, :x2 = 2 * :x, :y)
@transform(gd, :x2 = 2 .* :y .* first(:y))
@transform!(df, :x, :y)
@transform!(df, :x = 2 * :x, :y)
@transform!(gd, :y = 2 .* :y .* first(:y))

@subset and @subset!

Select row subsets. Operates on both a DataFrame and a GroupedDataFrame. @subset always returns a freshly-allocated data frame whereas @subset! modifies the data frame in-place.

using Statistics
df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
gd = groupby(df, :x);
outside_var = 1;
@subset(df, :x .> 1)
@subset(df, :x .> outside_var)
@subset(df, :x .> outside_var, :y .< 102)  # the two expressions are "and-ed"
@subset(gd, :x .> mean(:x))


Summarize, or collapse, a grouped data frame by performing transformations at the group level and collecting the result into a single data frame. Also works on a DataFrame, which acts like a GroupedDataFrame with one group.

Like @select and @transform, transformations are called with the keyword-like syntax :y = f(:x).


df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
gd = groupby(df, :x);
@combine(gd, :x2 = sum(:y))
@combine(gd, :x2 = :y .- sum(:y))
@combine(gd, $AsTable = (n1 = sum(:y), n2 = first(:y)))

The last example tells the underlying DataFrames.jl function combine that the output should be a "Table" in the Tables.jl sense. For more information, see the documentation for DataFrames.combine and the section below on escaping column identifiers with $.

@combine requires a DataFrame or GroupedDataFrame as the first argument. This is unlike combine from DataFrames.jl, which can take a function as the first argument and a GroupedDataFrame as the second argument. For instance, @combine((a = sum(:x), b = sum(:y)), gd) will fail. The following, however, will work.

df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
gd = groupby(df, :x);
@combine(gd, $AsTable = (a = sum(:x), b = sum(:y)))


Sort rows in a DataFrame by values in one of several columns or a transformation of columns. Only operates on DataFrames and not GroupedDataFrames.

df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
@orderby(df, -1 .* :x)
@orderby(df, :x, :y .- mean(:y))


@with creates a scope in which all symbols that appear are aliases for the columns in a DataFrame.

df = DataFrame(x = 1:3, y = [2, 1, 2])
x = [2, 1, 0]

@with(df, :y .+ 1)
@with(df, :x + x)  # the two x's are different

x = @with df begin
    res = 0.0
    for i in 1:length(:x)
        res += :x[i] * :y[i]

@with(df, df[:x .> 1, ^(:y)]) # The ^ means leave the :y alone

@with creates a function, so scope within @with is a local scope. Variables in the parent can be read. Writing to variables in the parent scope differs depending on the type of scope of the parent. If the parent scope is a global scope, then a variable cannot be assigned without using the global keyword. If the parent scope is a local scope (inside a function or let block for example), the global keyword is not needed to assign to that parent scope.


Because @with creates a function, be careful with the use of return.

function data_transform(df; returnearly = true)
    if returnearly
        @with df begin 
            z = :x + :y
            return z
        return [1, 2, 3]

    return [4, 5, 6]

The above function will return [4, 5, 6] because the return inside the @with applies to the anonymous function created by @with.

Given that @eachrow (below) is implemented with @with, the same caveat applies to @eachrow blocks.

@eachrow and @eachrow!

Act on each row of a data frame. Includes support for control flow and begin end blocks. Since the "environment" induced by @eachrow df is implicitly a single row of df, one uses regular operators and comparisons instead of their elementwise counterparts as in @with. Does not change the input data frame argument.

@eachrow! is identical to @eachrow but acts on a data frame in-place, modifying the input.

df = DataFrame(A = 1:3, B = [2, 1, 2])
df2 = @eachrow df begin 
    :A = :B + 1

@eachrow introduces a function scope, so a let block is required here to create a scope to allow assignment of variables within @eachrow.

df = DataFrame(A = 1:3, B = [2, 1, 2], C = [-4,2,1])
let x = 0.0
    @eachrow df begin
        if :A < :B
            x += :A * :C

@eachrow also supports special syntax for allocating new columns to make @eachrow more useful for data transformations. The syntax @newcol :x::Vector{Int} allocates a new column :x with an Vector container with eltype Int. Here is an example where two new columns are added:

df = DataFrame(A = 1:3, B = [2, 1, 2])
df2 = @eachrow df begin
    @newcol :colX::Vector{Float64}
    @newcol :colY::Vector{Union{Int,Missing}}
    :colX = :B == 2 ? pi * :A : :B
    if :A > 1
        :colY = :A * :B
        :colY = missing

Row-wise transformations with @byrow and @rtransform/@rselect/etc.

@byrow provides a convenient syntax to apply operations by-row, without having to vectorize manually. Additionally, the macros @rtransform, @rtransform!, @rselect, @rselect!, @rorderby, @rsubset, and @rsubset! use @byrow by default.

DataFrames.jl provides the function wrapper ByRow. ByRow(f)(x, y) is roughly equivalent to f.(x, y). DataFramesMeta.jl allows users to construct expressions using ByRow function wrapper with the syntax @byrow or the row-wise macros @rtransform, etc.

@byrow is not a "real" macro and cannot be used outside of DataFramesMeta.jl macros. However its behavior within DataFramesMeta.jl macros should be indistinguishable from externally defined macros. Thought of as a macro @byrow accepts a single argument and creates an anonymous function wrapped in ByRow. For example,

@transform(df, @byrow :y = :x == 1 ? true : false)

is equivalent to

transform(df, :x => ByRow(x -> x == 1 ? true : false) => :y)

The following macros accept @byrow:

  • @transform and @transform!, @select, @select!, and @combine. @byrow can be used in the left hand side of expressions, e.g. @select(df, @byrow z = :x * :y).
  • @subset, @subset! and @orderby, with syntax of the form @subset(df, @byrow :x > :y)
  • @with, where the anonymous function created by @with is wrapped in ByRow, as in @with(df, @byrow :x * :y).

To avoid writing @byrow multiple times when performing multiple operations, it is allowed to use @byrow at the beginning of a block of operations. All transformations in the block will operate by row.

julia> df = DataFrame(a = [1, 2], b = [3, 4]);

julia> @subset df @byrow begin
           :a > 1
           :b < 5
1×2 DataFrame
 Row │ a      b     
     │ Int64  Int64 
   1 │     2      4

@byrow can be used inside macros which accept GroupedDataFrames, however, like with ByRow in DataFrames.jl, when @byrow is used, functions do not take into account the grouping, so for example the result of @transform(df, @byrow :y = f(:x)) and @transform(groupby(df, :g), @byrow :y = f(:x)) is the same.

Propagating missing values with @passmissing

Many Julia functions do not automatically propagate missing values. For instance, parse(Int, missing) will error.

Missings.jl provides the passmissing function-wrapper to help get around these roadblocks: passmissing(f)(args...) will return missing if any of args is missing. Similarly, DataFramesMeta.jl provides the @passmissing function to wrap the anonymous functions created by row-wise transformations in DataFramesMeta.jl in Missings.passmissing.

The expression

@transform df @byrow @passmissing :c = f(:a, :b)

is translated to

transform(df, [:a, :b] => ByRow(passmissing(f)) => :c)

See more examples below.

julia> no_missing(x::Int, y::Int) = x + y;

julia> df = DataFrame(a = [1, 2, missing], b = [4, 5, 6])
3×2 DataFrame
 Row │ a        b
     │ Int64?   Int64
   1 │       1      4
   2 │       2      5
   3 │ missing      6

julia> @transform df @passmissing @byrow :c = no_missing(:a, :b)
3×3 DataFrame
 Row │ a        b      c
     │ Int64?   Int64  Int64?
   1 │       1      4        5
   2 │       2      5        7
   3 │ missing      6  missing

julia> df = DataFrame(x_str = ["1", "2", missing])
3×1 DataFrame
 Row │ x_str
     │ String?
   1 │ 1
   2 │ 2
   3 │ missing

julia> @rtransform df @passmissing :x = parse(Int, :x_str)
3×2 DataFrame
 Row │ x_str    x
     │ String?  Int64?
   1 │ 1              1
   2 │ 2              2
   3 │ missing  missing

Passing keyword arguments to underlying DataFrames.jl functions

All DataFramesMeta.jl macros allow passing of keyword arguments to their DataFrames.jl function equivelents. The table below describes the correspondence between DataFramesMeta.jl macros and the function that is actually called by the macro.

MacroBase DataFrames.jl function called
@orderbyNone (no keyword arguments supported)
@rorderbyNone (no keyword arguments supported)

This can be done in two ways. When inputs are given as multiple arguments, they are added at the end after a semi-colon ;, as in

julia> df = DataFrame(x = [1, 1, 2, 2], b = [5, 6, 7, 8]);

julia> @rsubset(df, :x .== 1 ; view = true)
2×2 SubDataFrame
 Row │ x      b     
     │ Int64  Int64 
   1 │     1      5
   2 │     1      6

When inputs are given in "block" format, the last lines may be written @kwarg key = value, which indicates keyword arguments to be passed to subset function.

julia> df = DataFrame(x = [1, 1, 2, 2], b = [5, 6, 7, 8]);

julia> @rsubset df begin
           :x == 1
           @kwarg view = true
2×2 SubDataFrame
 Row │ x      b     
     │ Int64  Int64 
   1 │     1      5
   2 │     1      6

Just as with Julia functions, it is possible to pass keyword arguments as Pairs programatically to DataFramesMeta.jl macros.

julia> df = DataFrame(x = [1, 1, 2, 2], b = [5, 6, 7, 8]);

julia> my_kwargs = [:view => true, :skipmissing => false];

julia> @rsubset(df, :x .== 1; my_kwargs...)
2×2 SubDataFrame
 Row │ x      b     
     │ Int64  Int64 
   1 │     1      5
   2 │     1      6

julia> @rsubset df begin 
           :x .== 1
           @kwarg my_kwargs...
2×2 SubDataFrame
 Row │ x      b     
     │ Int64  Int64 
   1 │     1      5
   2 │     1      6

Creating multiple columns at once with @astable

Often new variables may depend on the same intermediate calculations. @astable makes it easy to create multiple new variables in the same operation, yet have them share information.

In a single block, all assignments of the form :y = f(:x) or $y = f(:x) at the top-level generate new columns. In the second form, y must be a string or Symbol.

julia> df = DataFrame(a = [1, 2, 3], b = [400, 500, 600]);

julia> @transform df @astable begin 
           ex = extrema(:b)
           :b_first = :b .- first(ex)
           :b_last = :b .- last(ex)
3×4 DataFrame
 Row │ a      b      b_first  b_last 
     │ Int64  Int64  Int64    Int64  
   1 │     1    400        0    -200
   2 │     2    500      100    -100
   3 │     3    600      200       0

Operations with multiple columns at once using AsTable inside operations

In operations, it is also allowed to use AsTable(cols) to work with multiple columns at once, where the columns are grouped together in a NamedTuple. When AsTable(cols) appears in a operation, no other columns may be referenced in the block.

AsTable on the right-hand side also allows the use of the special column selectors Not, Between, and regular expressions, as well as working with lists of variables programmatically.

For example, consider a collection of column names vars, such that

df = DataFrame(a = [11, 14], b = [17, 10], c = [12, 5]);
vars = ["a", "b"];

To make a new column which is the sum of vars, write

julia> @rtransform df :y = sum(AsTable(vars))
2×4 DataFrame
 Row │ a      b      c      y     
     │ Int64  Int64  Int64  Int64 
   1 │    11     17     12     28
   2 │    14     10      5     24

Of course, you can also use AsTable on the right-hand side using Symbols as column selectors

julia> @rtransform df :y = sum(AsTable([:a, :b]))
2×4 DataFrame
 Row │ a      b      c      y     
     │ Int64  Int64  Int64  Int64 
   1 │    11     17     12     28
   2 │    14     10      5     24

AsTable on the right-hand side also allows operations which can use the names of the variables.

julia> function fun_with_new_name(x::NamedTuple)
           nms = string.(propertynames(x))
           new_name = Symbol(join(nms, "_"), "_sum")
           s = sum(x)
           (; new_name => s)

julia> @rtransform df $AsTable = fun_with_new_name(AsTable([:a, :b]))
2×4 DataFrame
 Row │ a      b      c      a_b_sum 
     │ Int64  Int64  Int64  Int64   
   1 │    11     17     12       28
   2 │    14     10      5       24

To subset all rows where the sum is greater than 25, write

julia> @rsubset df sum(AsTable(vars)) > 25
1×3 DataFrame
 Row │ a      b      c     
     │ Int64  Int64  Int64 
   1 │    11     17     12

To understand the how this works, recall that DataFrames.jl allows for AsTable(cols) to be a source in a source => fun => dest mini-language expression. As a consequence, the transformation call

:y = f(AsTable(cols)) 


AsTable(cols) => f => :y

Note that DataFrames does not allow source => fun => dest commands to be of the form

[AsTable(cols), :x] => f => :y

As a consequence, DataFramesMeta.jl does not allow any other column selectors to appear inside the expression. The command

:y = sum(AsTable(cols)) + :d

will fail.

Finally, note that everyting inside AsTable is escaped by default. There is no ned to use $ inside AsTable on the right-hand side. For example

:y = first(AsTable("a"))

will work as expected.

AsTable and @astable, explained

At this point we have seen AsTable appear in three places:

  1. AsTable on the left-hand side of transformations: $AsTable = f(:a, :b)
  2. The macro-flag @astable within the transformation.
  3. AsTable(cols) on the right-hand side for multi-column transformations.

The differences between the three is summarized below

$AsTable on LHSCreate multiple columns at once, whose column names are only known programmaticallyRequires escaping with $ until deprecation period ends for unquoted column names on LHS.
@astableCreate multiple columns at once where number of columns is known in advance
AsTable on RHSWork with multiple columns at onceRequires input columns, unlike on LHS

Working with column names programmatically with $

DataFramesMeta provides the special syntax $ for referring to columns in a data frame via a Symbol, string, or column position as either a literal or a variable.

df = DataFrame(A = 1:3, :B = [2, 1, 2])

nameA = :A
df2 = @transform(df, :C = :B - $nameA)

nameA_string = "A"
df3 = @transform(df, :C = :B - $nameA_string)

nameB = "B"
df4 = @eachrow df begin 
    :A = $nameB

$ can also be used to create new columns in a data frame.

df = DataFrame(A = 1:3, B = [2, 1, 2])

newcol = "C"
@select(df, $newcol = :A + :B)

@by(df, :B, $("A complicated" * " new name") = first(:A))

nameC = "C"
df3 = @eachrow df begin 
    @newcol $nameC::Vector{Int}
    $nameC = :A

DataFramesMeta macros do not allow mixing of integer column references with references of other types. This means @transform(df, :y = :A + $2), attempting to add the columns df[!, :A] and df[!, 2], will fail. This is because in DataFrames, the command

transform(df, [:A, 2] => (+) => :y)

will fail, as DataFrames requires the "source" column identifiers in a source => fun => dest pair to all have the same type. DataFramesMeta adds one exception to this rule. Symbols and strings are allowed to be mixed inside DataFramesMeta macros. Consequently,

@transform(df, :y = :A + $"B")

will not error even though

transform(df, [:A, "B"] => (+) => :y)

will error in DataFrames.

For consistency, this restriction in the input column types also applies to @with and @eachrow. You cannot mix integer column references with Symbol or string column references in @with and @eachrow in any part of the expression, but you can mix Symbols and strings. The following will fail:

df = DataFrame(A = 1:3, B = [2, 1, 2])
@eachrow df begin 
    :A = $2

@with df begin 
    $1 + $"A"

while the following will work without error

@eachrow df begin 
    $1 + $2

@with df begin 
    $1 + $2

To reference columns with more complicated expressions, you must wrap column references in parentheses.

@transform df :a + $("a column name" * " in two parts")
@transform df :a + $(get_column_name(x))

Using src => fun => dest calls using $

If an argument is entirely wrapped in $(), the result bypasses the anonymous function creation of DataFramesMeta.jl and is passed to the underling DataFrames.jl function directly. Importantly, this allows for src => fun => dest calls from the DataFrames.jl "mini-language" directly. One example where this is useful is calling multiple functions across multiple input parameters. For instance, the Pair

[:a, :b] .=> [sum mean]

takes the sum and mean of both columns :a and :b separately. It is not possible to express this with DataFramesMeta.jl. But the operation can easily be performed with $

julia> using Statistics

julia> df = DataFrame(a = [1, 2], b = [30, 40]);

julia> @transform df $([:a, :b] .=> [sum mean])
2×6 DataFrame
 Row │ a      b      a_sum  b_sum  a_mean   b_mean  
     │ Int64  Int64  Int64  Int64  Float64  Float64 
   1 │     1     30      3     70      1.5     35.0
   2 │     2     40      3     70      1.5     35.0

Multi-argument column selection

To refer to multiple columns in DataFrames.jl, one can write

select(df, [:a, :b])

which selects the columns :a and :b in the data frame. We can generate this command in DataFramesMeta.jl with

@select df $[:a, :b]

Similarly, to select all columns beginning with the letter "a", wrap a regular expression in $(). As mentioned above, because the regex is a complicated syntax, we need to wrap it in parentheses, so that

@select df $(r"^a")

will construct the command select(df, r"^a").

Multi-argument selectors may only be used when an entire argument is wrapped in $(). For example

@select df :y = f($[:a, :b])

will fail.

Not all functions in DataFrames.jl allow for multi-column selectors, so detailed knowledge of the underlying functions in DataFrames.jl may be required. For example, the call

subset(df, [:a, :b])

will fail in DataFrames.jl, bcause DataFrames.subset does not support vectors of column names. Likewise, @subset df $[:a, :b] will fail. The macros which support multi-column selectors are

  • @select
  • @transform (multi-argument selectors have no effect)
  • @combine
  • @by

Since arguments wrapped entirely in $() get passed directly to underlying DataFrames.jl functions, this allows the use of the DataFrames.jl "mini-language" consisting of src => fun => dest pairs inside DataFramesMeta.jl macros. For example, you can do the following:

julia> df = DataFrame(a = [1, 2], b = [3, 4]);

julia> my_transformation = :a => (t -> t .+ 100) => :c;

julia> @transform df begin 
           :d = :b .+ 200
2×4 DataFrame
 Row │ a      b      c      d     
     │ Int64  Int64  Int64  Int64 
   1 │     1      3    101    203
   2 │     2      4    102    204

or with @subset

julia> @subset df $(:a => t -> t .>= 2)
1×2 DataFrame
 Row │ a      b     
     │ Int64  Int64 
   1 │     2      4

The macros @orderby and @with do not transparently call underlying DataFrames.jl functions. Escaping entire transformations should be considered unstable and may change in future versions.


Row-wise macros such as @rtransform and @rsubset will not automatically wrap functions in src => fun => dest in ByRow.

In summary

  • All arguments that are not entirely escaped with $ or $() construct anonymous functions. Inside these expressions only single-column selectors are allowed. This includes

    • Symbols, i.e. :x and :y
    • Strings, escaped with $, i.e. $"A string" or $("A string with many" * "parts")
    • Integers, escaped with $, i.e. $1
    • Any single-column variable representing one of the above, escaped with $, i.e. $x

    In transformation operations, i.e. @transform :y = f(:x), the same rules on the right hand side also apply to the left hand side. For example, @transform $"y" = f(:x) will work.

  • Arguments wrapped entirely in $ or $() are passed directly to the underlying DataFrames.jl functions. Because of this, in addition to the single-column selectors listed above, multi-argument selectors are allowed. These include, but are not limited to

    • Vectors of Symbols, $[:x, :y], strings, $["x", "y"], or integers $[1, 2]
    • Regular expressions, $(r"^a")
    • Filtering column selectors, such as $(Not(:x)) and $(Between(:a, :z))

    The macros @with, @subset, and @orderby do not support multi-column selectors.

  • Advanced users of DataFramesMeta.jl and DataFrames.jl may wrap an argument entirely in $() to pass src => fun => dest pairs directly to DataFrames.jl functions. However this is discouraged and it's behavior may change in future versions.

Working with Symbols without referring to columns

To refer to Symbols without aliasing the column in a data frame, use ^.

df = DataFrame(x = [1, 1, 2, 2], y = [1, 2, 101, 102]);
@select(df, :x2 = :x, :x3 = ^(:x))

This rule applies to all DataFramesMeta macros.

Comparison with dplyr and LINQ

A number of functions for operations on DataFrames have been defined. Here is a table of equivalents for Hadley's dplyr and common LINQ functions.

Julia             dplyr            LINQ
@subset           filter           Where
@transform        mutate           Select (?)
@by                                GroupBy
groupby           group_by         GroupBy
@combine          summarise/do
@orderby          arrange          OrderBy
@select           select           Select

Chaining operations together with @chain

To enable connecting multiple commands together in a pipe, DataFramesMeta.jl re-exports the @chain macro from Chain.jl.

using Statistics 

df = DataFrame(a = repeat(1:5, outer = 20),
               b = repeat(["a", "b", "c", "d"], inner = 25),
               x = repeat(1:20, inner = 5))

x_thread = @chain df begin
    @transform(:y = 10 * :x)
    @subset(:a .> 2)
    @by(:b, :meanX = mean(:x), :meanY = mean(:y))
    @select(:meanX, :meanY, :var = :b)

By default, @chain places the value of the previous expression into the first argument of the current expression. The placeholder _ is used to break that convention and refer to the argument returned from the previous expression.

# Get the sum of all columns after 
# a few transformations
@chain df begin 
    @transform(:y = 10 .* :x)
    @subset(:a .> 2)
    @select(:a, :y, :x)
    reduce(+, eachcol(_))

@chain also provides the @aside macro-flag to perform operations in the middle of a @chain block.

@chain df begin 
    @transform :y = 10 .* :x
    @aside y_mean = mean(_.y) # From Chain.jl, not DataFramesMeta.jl
    @select :y_standardize = :y .- y_mean