Manipulating columns of a DataFrame

Renaming columns

Let’s start with a DataFrame of Bools that has default column names.

using DataFrames

x = DataFrame(rand(Bool, 3, 4), :auto)

3×4 DataFrame

Row	x1	x2	x3	x4
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

With rename(), we create new DataFrame; here we rename the column :x1 to :A. (rename also accepts collections of Pairs.)

rename(x, :x1 => :A)

3×4 DataFrame

Row	A	x2	x3	x4
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

With rename!() we do an in place transformation. This time we’ve applied a function to every column name (note that the function gets a column names as a string).

rename!(c -> c^2, x)

3×4 DataFrame

Row	x1x1	x2x2	x3x3	x4x4
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

We can also change the name of a particular column without knowing the original. Here we change the name of the third column, creating a new DataFrame.

rename(x, 3 => :third)

3×4 DataFrame

Row	x1x1	x2x2	third	x4x4
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

If we pass a vector of names to rename!, we can change the names of all variables.

rename!(x, [:a, :b, :c, :d])

3×4 DataFrame

Row	a	b	c	d
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

In all the above examples you could have used strings instead of symbols, for example,

rename!(x, string.('a':'d'))

3×4 DataFrame

Row	a	b	c	d
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

rename! allows for circular renaming of columns:

x

3×4 DataFrame

Row	a	b	c	d
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

rename!(x, "a"=>"d", "d"=>"a")

3×4 DataFrame

Row	d	b	c	a
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

We get an error when we try to provide duplicate names

try
    rename(x, fill(:a, 4))
catch e
    show(e)
end

ArgumentError("Duplicate variable names: :a. Pass makeunique=true to make them unique using a suffix automatically.")

unless we pass makeunique=true, which allows us to handle duplicates in passed names.

rename(x, fill(:a, 4), makeunique=true)

3×4 DataFrame

Row	a	a_1	a_2	a_3
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

Reordering columns

We can reorder the names(x) vector as needed, creating a new DataFrame.

using Random
Random.seed!(1234)
x[:, shuffle(names(x))]

3×4 DataFrame

Row	c	b	a	d
	Bool	Bool	Bool	Bool
1	true	true	false	false
2	true	true	true	true
3	false	true	true	false

Also select! can be used to achieve this in place (or select to perform a copy):

x

3×4 DataFrame

Row	d	b	c	a
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	false	true	false	true

select!(x, 4:-1:1);
x

3×4 DataFrame

Row	a	c	b	d
	Bool	Bool	Bool	Bool
1	false	true	true	false
2	true	true	true	true
3	true	false	true	false

Mrging/adding columns

x = DataFrame([(i,j) for i in 1:3, j in 1:4], :auto)

3×4 DataFrame

Row	x1	x2	x3	x4
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)

With hcat we can merge two DataFrames. Also [x y] syntax is supported but only when DataFrames have unique column names.

hcat(x, x, makeunique=true)

3×8 DataFrame

Row	x1	x2	x3	x4	x1_1	x2_1	x3_1	x4_1
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 1)	(1, 2)	(1, 3)	(1, 4)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 1)	(2, 2)	(2, 3)	(2, 4)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 1)	(3, 2)	(3, 3)	(3, 4)

You can append a vector to a data frame with the following syntax:

y = [x DataFrame(A=[1,2,3])]

3×5 DataFrame

Row	x1	x2	x3	x4	A
	Tuple…	Tuple…	Tuple…	Tuple…	Int64
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	1
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	2
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	3

Here we do the same but add column :A to the front.

y = [DataFrame(A=[1,2,3]) x]

3×5 DataFrame

Row	A	x1	x2	x3	x4
	Int64	Tuple…	Tuple…	Tuple…	Tuple…
1	1	(1, 1)	(1, 2)	(1, 3)	(1, 4)
2	2	(2, 1)	(2, 2)	(2, 3)	(2, 4)
3	3	(3, 1)	(3, 2)	(3, 3)	(3, 4)

A column can also be added in the middle. Here a brute-force method is used and a new DataFrame is created.

using BenchmarkTools
@btime [$x[!, 1:2] DataFrame(A=[1,2,3]) $x[!, 3:4]]

  5.016 μs (93 allocations: 5.78 KiB)

3×5 DataFrame

Row	x1	x2	A	x3	x4
	Tuple…	Tuple…	Int64	Tuple…	Tuple…
1	(1, 1)	(1, 2)	1	(1, 3)	(1, 4)
2	(2, 1)	(2, 2)	2	(2, 3)	(2, 4)
3	(3, 1)	(3, 2)	3	(3, 3)	(3, 4)

We could also do this with a specialized in place method insertcols!. Let’s add :newcol to the DataFrame y.

insertcols!(y, 2, "newcol" => [1,2,3])

3×6 DataFrame

Row	A	newcol	x1	x2	x3	x4
	Int64	Int64	Tuple…	Tuple…	Tuple…	Tuple…
1	1	1	(1, 1)	(1, 2)	(1, 3)	(1, 4)
2	2	2	(2, 1)	(2, 2)	(2, 3)	(2, 4)
3	3	3	(3, 1)	(3, 2)	(3, 3)	(3, 4)

If you want to insert the same column name several times makeunique=true is needed as usual.

insertcols!(y, 2, :newcol => [1,2,3], makeunique=true)

3×7 DataFrame

Row	A	newcol_1	newcol	x1	x2	x3	x4
	Int64	Int64	Int64	Tuple…	Tuple…	Tuple…	Tuple…
1	1	1	1	(1, 1)	(1, 2)	(1, 3)	(1, 4)
2	2	2	2	(2, 1)	(2, 2)	(2, 3)	(2, 4)
3	3	3	3	(3, 1)	(3, 2)	(3, 3)	(3, 4)

We can see how much faster it is to insert a column with insertcols! than with hcat using @btime (note that we use here a Pair notation as an example).

@btime insertcols!(copy($x), 3, :A => [1,2,3])

  1.306 μs (26 allocations: 1.55 KiB)

3×5 DataFrame

Row	x1	x2	A	x3	x4
	Tuple…	Tuple…	Int64	Tuple…	Tuple…
1	(1, 1)	(1, 2)	1	(1, 3)	(1, 4)
2	(2, 1)	(2, 2)	2	(2, 3)	(2, 4)
3	(3, 1)	(3, 2)	3	(3, 3)	(3, 4)

Let’s use insertcols! to append a column in place (note that we dropped the index at which we insert the column)

insertcols!(x, :A => [1,2,3])

3×5 DataFrame

Row	x1	x2	x3	x4	A
	Tuple…	Tuple…	Tuple…	Tuple…	Int64
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	1
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	2
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	3

and to in place prepend a column.

insertcols!(x, 1, :B => [1,2,3])

3×6 DataFrame

Row	B	x1	x2	x3	x4	A
	Int64	Tuple…	Tuple…	Tuple…	Tuple…	Int64
1	1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	1
2	2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	2
3	3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	3

Note that insertcols! can be used to insert several columns to a data frame at once and that it performs broadcasting if needed:

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

3×1 DataFrame

Row	a
	Int64
1	1
2	2
3	3

insertcols!(df, :b => "x", :c => 'a':'c', :d => Ref([1,2,3]))

3×4 DataFrame

Row	a	b	c	d
	Int64	String	Char	Array…
1	1	x	a	[1, 2, 3]
2	2	x	b	[1, 2, 3]
3	3	x	c	[1, 2, 3]

Interestingly we can emulate hcat mutating the data frame in-place using insertcols!:

df1 = DataFrame(a=[1,2])

2×1 DataFrame

Row	a
	Int64
1	1
2	2

df2 = DataFrame(b=[2,3], c=[3,4])

2×2 DataFrame

Row	b	c
	Int64	Int64
1	2	3
2	3	4

hcat(df1, df2)

2×3 DataFrame

Row	a	b	c
	Int64	Int64	Int64
1	1	2	3
2	2	3	4

df1 is not touched

df1

2×1 DataFrame

Row	a
	Int64
1	1
2	2

insertcols!(df1, pairs(eachcol(df2))...)

2×3 DataFrame

Row	a	b	c
	Int64	Int64	Int64
1	1	2	3
2	2	3	4

now we have changed df1

df1

2×3 DataFrame

Row	a	b	c
	Int64	Int64	Int64
1	1	2	3
2	2	3	4

Subsetting/removing columns

Let’s create a new DataFrame x and show a few ways to create DataFrames with a subset of x’s columns.

x = DataFrame([(i,j) for i in 1:3, j in 1:5], :auto)

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

First we could do this by index: You could use ! instead of : for non-copying operation

x[:, [1,2,4,5]]

3×4 DataFrame

Row	x1	x2	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 4)	(3, 5)

or by column name:

x[:, [:x1, :x4]]

3×2 DataFrame

Row	x1	x4
	Tuple…	Tuple…
1	(1, 1)	(1, 4)
2	(2, 1)	(2, 4)
3	(3, 1)	(3, 4)

We can also choose to keep or exclude columns by Bool (we need a vector whose length is the number of columns in the original DataFrame).

x[:, [true, false, true, false, true]]

3×3 DataFrame

Row	x1	x3	x5
	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 3)	(1, 5)
2	(2, 1)	(2, 3)	(2, 5)
3	(3, 1)	(3, 3)	(3, 5)

Here we create a single column DataFrame,

x[:, [:x1]]

3×1 DataFrame

Row	x1
	Tuple…
1	(1, 1)
2	(2, 1)
3	(3, 1)

and here we access the vector contained in column :x1.

x[!, :x1] ## use : instead of ! to copy

3-element Vector{Tuple{Int64, Int64}}:
 (1, 1)
 (2, 1)
 (3, 1)

x.x1 ## the same

3-element Vector{Tuple{Int64, Int64}}:
 (1, 1)
 (2, 1)
 (3, 1)

We could grab the same vector by column number

x[!, 1]

3-element Vector{Tuple{Int64, Int64}}:
 (1, 1)
 (2, 1)
 (3, 1)

Note that getting a single column returns it without copying while creating a new DataFrame performs a copy of the column

x[!, 1] === x[!, [1]]

false

you can also use Regex, All, Between and Not from InvertedIndies.jl for column selection:

x[!, r"[12]"]

3×2 DataFrame

Row	x1	x2
	Tuple…	Tuple…
1	(1, 1)	(1, 2)
2	(2, 1)	(2, 2)
3	(3, 1)	(3, 2)

x[!, Not(1)]

3×4 DataFrame

Row	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 2)	(3, 3)	(3, 4)	(3, 5)

x[!, Between(:x2, :x4)]

3×3 DataFrame

Row	x2	x3	x4
	Tuple…	Tuple…	Tuple…
1	(1, 2)	(1, 3)	(1, 4)
2	(2, 2)	(2, 3)	(2, 4)
3	(3, 2)	(3, 3)	(3, 4)

x[!, Cols(:x1, Between(:x3, :x5))]

3×4 DataFrame

Row	x1	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 3)	(3, 4)	(3, 5)

select(x, :x1, Between(:x3, :x5), copycols=false) ## the same as above

3×4 DataFrame

Row	x1	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 3)	(3, 4)	(3, 5)

you can use select and select! functions to select a subset of columns from a data frame. select creates a new data frame and select! operates in place

df = copy(x)

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

df2 = select(df, [1, 2])

3×2 DataFrame

Row	x1	x2
	Tuple…	Tuple…
1	(1, 1)	(1, 2)
2	(2, 1)	(2, 2)
3	(3, 1)	(3, 2)

select(df, Not([1, 2]))

3×3 DataFrame

Row	x3	x4	x5
	Tuple…	Tuple…	Tuple…
1	(1, 3)	(1, 4)	(1, 5)
2	(2, 3)	(2, 4)	(2, 5)
3	(3, 3)	(3, 4)	(3, 5)

by default select copies columns

df2[!, 1] === df[!, 1]

false

which can be avoided by giving copycols=false

df2 = select(df, [1, 2], copycols=false)

3×2 DataFrame

Row	x1	x2
	Tuple…	Tuple…
1	(1, 1)	(1, 2)
2	(2, 1)	(2, 2)
3	(3, 1)	(3, 2)

df2[!, 1] === df[!, 1]

true

using select! will modify the source data frame

select!(df, [1,2])

3×2 DataFrame

Row	x1	x2
	Tuple…	Tuple…
1	(1, 1)	(1, 2)
2	(2, 1)	(2, 2)
3	(3, 1)	(3, 2)

df == df2

true

Here we create a copy of x and delete the 3rd column from the copy with select! and Not

z = copy(x)
select!(z, Not(3))

3×4 DataFrame

Row	x1	x2	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 4)	(3, 5)

alternatively we can achieve the same by using the select function

select(x, Not(3))

3×4 DataFrame

Row	x1	x2	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 4)	(3, 5)

x stays unchanged

x

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

Views

Note, that you can also create a view of a DataFrame when we want a subset of its columns:

@btime x[:, [1,3,5]]

  1.185 μs (25 allocations: 1.50 KiB)

3×3 DataFrame

Row	x1	x3	x5
	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 3)	(1, 5)
2	(2, 1)	(2, 3)	(2, 5)
3	(3, 1)	(3, 3)	(3, 5)

@btime @view x[:, [1,3,5]]

  416.232 ns (5 allocations: 240 bytes)

3×3 SubDataFrame

Row	x1	x3	x5
	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 3)	(1, 5)
2	(2, 1)	(2, 3)	(2, 5)
3	(3, 1)	(3, 3)	(3, 5)

(Right now creation of the view is slow, but in the coming releases of the DataFrames.jl package it will become significantly faster)

Modify column by name

x = DataFrame([(i,j) for i in 1:3, j in 1:5], :auto)

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

With the following syntax, the existing column is modified without performing any copying (this is discouraged as it creates column alias).

x[!, :x1] = x[!, :x2]
x

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 2)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 2)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 2)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

this syntax is safer since it performs copy

x[!, :x1] = x[:, :x2]

3-element Vector{Tuple{Int64, Int64}}:
 (1, 2)
 (2, 2)
 (3, 2)

We can also use the following syntax to add a new column at the end of a DataFrame.

x[!, :A] = [1,2,3]
x

3×6 DataFrame

Row	x1	x2	x3	x4	x5	A
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…	Int64
1	(1, 2)	(1, 2)	(1, 3)	(1, 4)	(1, 5)	1
2	(2, 2)	(2, 2)	(2, 3)	(2, 4)	(2, 5)	2
3	(3, 2)	(3, 2)	(3, 3)	(3, 4)	(3, 5)	3

A new column name will be added to our DataFrame with the following syntax as well:

x.B = 11:13
x

3×7 DataFrame

Row	x1	x2	x3	x4	x5	A	B
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…	Int64	Int64
1	(1, 2)	(1, 2)	(1, 3)	(1, 4)	(1, 5)	1	11
2	(2, 2)	(2, 2)	(2, 3)	(2, 4)	(2, 5)	2	12
3	(3, 2)	(3, 2)	(3, 3)	(3, 4)	(3, 5)	3	13

Find column name

x = DataFrame([(i,j) for i in 1:3, j in 1:5], :auto)

3×5 DataFrame

Row	x1	x2	x3	x4	x5
	Tuple…	Tuple…	Tuple…	Tuple…	Tuple…
1	(1, 1)	(1, 2)	(1, 3)	(1, 4)	(1, 5)
2	(2, 1)	(2, 2)	(2, 3)	(2, 4)	(2, 5)
3	(3, 1)	(3, 2)	(3, 3)	(3, 4)	(3, 5)

We can check if a column with a given name exists via hasproperty

hasproperty(x, :x1)

true

and determine its index via columnindex

columnindex(x, :x2)

2

Advanced ways of column selection

these are most useful for non-standard column names (e.g. containing spaces)

df = DataFrame()
df.x1 = 1:3
df[!, "column 2"] = 4:6
df

3×2 DataFrame

Row	x1	column 2
	Int64	Int64
1	1	4
2	2	5
3	3	6

df."column 2"

3-element Vector{Int64}:
 4
 5
 6

df[:, "column 2"]

3-element Vector{Int64}:
 4
 5
 6

or you can interpolate column names using the :() syntax

for n in names(df)
    println(n, "\n", df.:($n), "\n")
end

x1
[1, 2, 3]

column 2
[4, 5, 6]

Working on a collection of columns

When using eachcol of a data frame the resulting object retains reference to its parent and e.g. can be queried with getproperty

df = DataFrame(reshape(1:12, 3, 4), :auto)

3×4 DataFrame

Row	x1	x2	x3	x4
	Int64	Int64	Int64	Int64
1	1	4	7	10
2	2	5	8	11
3	3	6	9	12

ec_df = eachcol(df)

3×4 DataFrameColumns

Row	x1	x2	x3	x4
	Int64	Int64	Int64	Int64
1	1	4	7	10
2	2	5	8	11
3	3	6	9	12

ec_df[1]

3-element Vector{Int64}:
 1
 2
 3

ec_df.x1

3-element Vector{Int64}:
 1
 2
 3

Transforming columns

We will get to this subject later in 10_transforms.ipynb notebook, but here let us just note that select, select!, transform, transform! and combine functions allow to generate new columns based on the old columns of a data frame.

The general rules are the following:

• select and transform always return the number of rows equal to the source data frame, while combine returns any number of rows (combine is allowed to combine rows of the source data frame)

• transform retains columns from the old data frame

• select! and transform! are in-place versions of select and transform

df = DataFrame(reshape(1:12, 3, 4), :auto)

3×4 DataFrame

Row	x1	x2	x3	x4
	Int64	Int64	Int64	Int64
1	1	4	7	10
2	2	5	8	11
3	3	6	9	12

Here we add a new column :res that is a sum of columns :x1 and :x2. A general syntax of transformations of this kind is:

source_columns => function_to_apply => target_column_name

then function_to_apply gets columns selected by source_columns as positional arguments.

transform(df, [:x1, :x2] => (+) => :res)

3×5 DataFrame

Row	x1	x2	x3	x4	res
	Int64	Int64	Int64	Int64	Int64
1	1	4	7	10	5
2	2	5	8	11	7
3	3	6	9	12	9

One can omit passing target_column_name in which case it is automatically generated:

using Statistics

combine(df, [:x1, :x2] => cor)

1×1 DataFrame

Row	x1_x2_cor
	Float64
1	1.0

Note that combine allowed the number of columns in the resulting data frame to be changed. If we used select instead it would automatically broadcast the return value to match the number of rows of the source:

select(df, [:x1, :x2] => cor)

3×1 DataFrame

Row	x1_x2_cor
	Float64
1	1.0
2	1.0
3	1.0

If you want to apply some function on each row of the source wrap it in ByRow:

select(df, :x1, :x2, [:x1, :x2] => ByRow(string))

3×3 DataFrame

Row	x1	x2	x1_x2_string
	Int64	Int64	String
1	1	4	14
2	2	5	25
3	3	6	36

Also if you want columns to be passed as a NamedTuple to a function (instead of being positional arguments) wrap them in AsTable:

select(df, :x1, :x2, AsTable([:x1, :x2]) => x -> x.x1 + x.x2)

3×3 DataFrame

Row	x1	x2	x1_x2_function
	Int64	Int64	Int64
1	1	4	5
2	2	5	7
3	3	6	9

For simplicity (as this functionality is often needed) there is a special treatment of nrow function that can be just passed as a transformation (without specifying of column selector):

select(df, :x1, nrow => "number_of_rows")

3×2 DataFrame

Row	x1	number_of_rows
	Int64	Int64
1	1	3
2	2	3
3	3	3

(note that in select the number of rows is automatically broadcasted to match the number of rows of the source data frame) Finally you can conveniently create multiple columns with one function, :

select(df, :x1, :x1 => ByRow(x -> [x ^ 2, x ^ 3]) => ["x1²", "x1³"])

3×3 DataFrame

Row	x1	x1²	x1³
	Int64	Int64	Int64
1	1	1	1
2	2	4	8
3	3	9	27

or (this produces the same result)

select(df, :x1, :x1 => (x -> DataFrame("x1²" => x .^ 2, "x1³" => x .^ 3)) => AsTable)

3×3 DataFrame

Row	x1	x1²	x1³
	Int64	Int64	Int64
1	1	1	1
2	2	4	8
3	3	9	27

Note that since DataFrames.jl row aggregation for wide tables is efficient. Here is an example of a wide table with sum (other standard reductions are similarly supported):

large_df = DataFrame(rand(1000, 10000), :auto)

1000×10000 DataFrame

9900 columns and 975 rows omitted

Row	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11	x12	x13	x14	x15	x16	x17	x18	x19	x20	x21	x22	x23	x24	x25	x26	x27	x28	x29	x30	x31	x32	x33	x34	x35	x36	x37	x38	x39	x40	x41	x42	x43	x44	x45	x46	x47	x48	x49	x50	x51	x52	x53	x54	x55	x56	x57	x58	x59	x60	x61	x62	x63	x64	x65	x66	x67	x68	x69	x70	x71	x72	x73	x74	x75	x76	x77	x78	x79	x80	x81	x82	x83	x84	x85	x86	x87	x88	x89	x90	x91	x92	x93	x94	x95	x96	x97	x98	x99	x100	⋯
	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	Float64	⋯
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998	0.70703	0.166208	0.45823	0.0682235	0.458422	0.836939	0.58208	0.721586	0.171453	0.353899	0.922517	0.394151	0.661091	0.381341	0.558679	0.847687	0.671701	0.373212	0.196267	0.353311	0.813622	0.192031	0.911645	0.452465	0.604791	0.743038	0.776148	0.00348142	0.332103	0.903827	0.88745	0.891842	0.203428	0.259077	0.243465	0.877412	0.614433	0.766943	0.22814	0.302435	0.849376	0.194502	0.903999	0.0363242	0.536268	0.886766	0.491461	0.87315	0.0148271	0.257042	0.210517	0.570115	0.684481	0.466046	0.367677	0.692357	0.922489	0.0315743	0.964974	0.523279	0.338024	0.509674	0.163316	0.362407	0.301272	0.879559	0.499213	0.631802	0.331658	0.370499	0.450381	0.604831	0.125916	0.934434	0.306163	0.207142	0.471049	0.0668867	0.0232546	0.212272	0.267954	0.398719	0.695908	0.850557	0.0875997	0.421732	0.330967	0.784816	0.0130859	0.341161	0.396292	0.412785	0.302764	0.849904	0.175216	0.687009	0.909296	0.692519	0.244417	0.0393655	⋯
999	0.82876	0.526196	0.298927	0.639369	0.261559	0.979035	0.934639	0.121346	0.251586	0.393869	0.15334	0.151882	0.640286	0.00827146	0.326673	0.125069	0.569502	0.227871	0.581491	0.429951	0.879147	0.0513471	0.962291	0.230274	0.564554	0.165625	0.0164029	0.631812	0.656675	0.723722	0.759431	0.701123	0.579314	0.81696	0.962671	0.829105	0.720869	0.506352	0.57007	0.91433	0.497342	0.189965	0.476128	0.19098	0.887968	0.0811497	0.397893	0.947995	0.433129	0.77806	0.625571	0.716419	0.553762	0.680455	0.776873	0.947365	0.368453	0.908491	0.337273	0.470834	0.857936	0.587565	0.246281	0.5715	0.475906	0.305129	0.460335	0.271297	0.222014	0.304069	0.736394	0.265666	0.74351	0.0904265	0.579612	0.621476	0.574681	0.427771	0.935226	0.533929	0.540672	0.458916	0.519859	0.0649	0.727934	0.952153	0.628522	0.77493	0.951054	0.372006	0.468555	0.787296	0.263424	0.944512	0.888987	0.0766307	0.78849	0.49701	0.570614	0.781092	⋯
1000	0.660552	0.435304	0.69959	0.118418	0.512571	0.389409	0.532196	0.0679856	0.704732	0.75332	0.37092	0.881958	0.73611	0.771431	0.812656	0.246535	0.872655	0.382756	0.359612	0.178767	0.769021	0.869384	0.138377	0.0559011	0.208121	0.106749	0.291899	0.160197	0.911127	0.376926	0.455296	0.860396	0.324969	0.653302	0.642319	0.813912	0.534663	0.921934	0.494164	0.275349	0.800289	7.54625e-5	0.912104	0.614587	0.0111345	0.550388	0.389368	0.126729	0.995746	0.425596	0.718592	0.668344	0.862516	0.231098	0.209897	0.436451	0.656335	0.556578	0.804893	0.258121	0.28222	0.636424	0.652443	0.222565	0.479368	0.426149	0.175931	0.405326	0.00835287	0.565661	0.432638	0.0971571	0.433251	0.999749	0.506784	0.722461	0.605742	0.0421695	0.809622	0.594125	0.262733	0.935439	0.96583	0.763589	0.0738046	0.554941	0.938291	0.878693	0.873989	0.658703	0.263339	0.277613	0.387373	0.239012	0.140129	0.0874112	0.0975285	0.577632	0.406463	0.126622	⋯

@time select(large_df, AsTable(:) => ByRow(sum) => :sum)

  0.040210 seconds (26.27 k allocations: 1.419 MiB, 80.16% compilation time)

1000×1 DataFrame

975 rows omitted

Row	sum
	Float64
1	4987.2
2	5016.2
3	4978.86
4	5018.0
5	5011.07
6	4991.09
7	4952.1
8	5006.35
9	4997.14
10	4994.29
11	5020.93
12	4917.99
13	5023.11
⋮	⋮
989	4997.01
990	5003.09
991	4938.96
992	4974.83
993	4952.49
994	5015.75
995	5013.19
996	4976.72
997	5015.42
998	5054.93
999	5033.81
1000	5013.15

@time select(large_df, AsTable(:) => ByRow(sum) => :sum)

  0.008024 seconds (19.63 k allocations: 1.069 MiB)

1000×1 DataFrame

975 rows omitted

Row	sum
	Float64
1	4987.2
2	5016.2
3	4978.86
4	5018.0
5	5011.07
6	4991.09
7	4952.1
8	5006.35
9	4997.14
10	4994.29
11	5020.93
12	4917.99
13	5023.11
⋮	⋮
989	4997.01
990	5003.09
991	4938.96
992	4974.83
993	4952.49
994	5015.75
995	5013.19
996	4976.72
997	5015.42
998	5054.93
999	5033.81
1000	5013.15

---

This notebook was generated using Literate.jl.