Function `map/map!`

Introduction

There are multiple ways to call a function on individual values (observations) of a column. In this section we discuss two of them: map! and map. These two functions are high-performance and customised version of map! and map in Julia Base.

map! and map call functions on the actual values, i.e. if the column is formatted the functions are called on the values which may be different from what you see when show a data set.

`map!`

The InMemoryDatasets' map! function modifies a data set in-place by calling a function on individual values of a column. The syntax for the function is

map!(ds, fun, cols; threads = true)

where ds is a data set, and cols can be a single column or multiple columns selector. When fun is a single function, map! call that function on all values of cols. Since map! is modifying the data set ds in-place, it will skip columns in cols where fun changes the type of columns, and print a warning on the screen.

The threads = true keyword argument causes map! to exploit all cores available to Julia for doing the calculations. This is particularly useful when the fun function is expensive to calculate.

When fun is a vector of functions and cols refers to multiple columns, map! calls each element of fun to the values of the corresponding column in cols. this means that it is possible to call different functions on different columns of a data set, however, the length of fun and the number of selected columns must be the same.

Examples

In the following data set we like to replace every missing in :x2 and :x3 with value 0. Thus, we use map! and a suitable function to do this:

julia> ds = Dataset(x1 = 1:5, x2 = [-2, -1, missing, 1, 2],
                    x3 = [0.0, 0.1, 0.2, missing, 0.4])
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼──────────────────────────────
   1 │        1        -2       0.0
   2 │        2        -1       0.1
   3 │        3   missing       0.2
   4 │        4         1   missing
   5 │        5         2       0.4

julia> map!(ds, x -> ismissing(x) ? 0 : x, 2:3)
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼──────────────────────────────
   1 │        1        -2       0.0
   2 │        2        -1       0.1
   3 │        3         0       0.2
   4 │        4         1       0.0
   5 │        5         2       0.4

Now let's call sqrt on all values. Note that sqrt of an integer is a float and sqrt of negative integer is not valid in Julia. So map! only applies the provided function on the last column and skips the first two columns.

julia> map!(ds, sqrt, :)
┌ Warning: cannot map `f` on ds[!, :x1] in-place, the selected column is Union{Missing, Int64} and the result of calculation is Union{Missing, Float64}
└ @ InMemoryDatasets ...
┌ Warning: cannot map `f` on ds[!, :x2] in-place, the selected column is Union{Missing, Int64} and the result of calculation is Union{Missing, Float64}
└ @ InMemoryDatasets ...
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼──────────────────────────────
   1 │        1        -2  0.0
   2 │        2        -1  0.316228
   3 │        3         0  0.447214
   4 │        4         1  0.0
   5 │        5         2  0.632456

As another example let's look at a data set where a column already has a format.

julia> ds = Dataset(x1 = 1:5, x2 = [1,2,1,2,1])
julia> setformat!(ds, 1=>isodd)
5×2 Dataset
 Row │ x1      x2
     │ isodd   identity
     │ Int64?  Int64?
─────┼──────────────────
   1 │   true         1
   2 │  false         2
   3 │   true         1
   4 │  false         2
   5 │   true         1

julia> map!(ds, x->div(x,2), 1:2)
5×2 Dataset
 Row │ x1      x2
     │ isodd   identity
     │ Int64?  Int64?
─────┼──────────────────
   1 │  false         0
   2 │   true         1
   3 │   true         0
   4 │  false         1
   5 │  false         0

Note that the format of :x1 is preserved and the function call changed the actual values. Thus, Datasets applies the format of :x1 to new values.

The following example shows how different functions can be used for different columns.

julia> ds = Dataset(x1 = 1:5, x2 = [-2, -1, missing, 1, 2],
                    x3 = [0.0, 0.1, 0.2, missing, 0.4])
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼──────────────────────────────
   1 │        1        -2       0.0
   2 │        2        -1       0.1
   3 │        3   missing       0.2
   4 │        4         1   missing
   5 │        5         2       0.4

julia> fun_vec = [x -> div(x, 2),
                  x -> ismissing(x) ? 0 : x,
                  sqrt]
3-element Vector{Function}:
 #5 (generic function with 1 method)
 #6 (generic function with 1 method)
 sqrt (generic function with 19 methods)

julia> map!(ds, fun_vec, 1:3)
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼────────────────────────────────────
   1 │        0        -2        0.0
   2 │        1        -1        0.316228
   3 │        1         0        0.447214
   4 │        2         1  missing
   5 │        2         2        0.632456

`map`

The map function also calls a function (or a vector of functions) on single or muliple columns of a data set. However, it differs from map! that we discussed earlier in three main areas,

it doesn't do in-place operation and returns a copy of the data set,
unlike map!, it can change the type of columns, and
it doesn't preserve the formats of the columns that are going to be modified.

Examples

In the following example we call x -> x/2 on all columns. However, note that map automatically changes the data type of the first two columns, and more importantly it doesn't modify the original data set:

julia> ds = Dataset(x1 = 1:5, x2 = [-2, -1, missing, 1, 2],
                    x3 = [0.0, 0.1, 0.2, missing, 0.4])
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼──────────────────────────────
   1 │        1        -2       0.0
   2 │        2        -1       0.1
   3 │        3   missing       0.2
   4 │        4         1   missing
   5 │        5         2       0.4
julia> map(ds, x -> x/2, r"x")
5×3 Dataset
 Row │ x1        x2         x3
     │ identity  identity   identity
     │ Float64?  Float64?   Float64?
─────┼─────────────────────────────────
   1 │      0.5       -1.0        0.0
   2 │      1.0       -0.5        0.05
   3 │      1.5  missing          0.1
   4 │      2.0        0.5  missing
   5 │      2.5        1.0        0.2

julia> ds # map doesn't modify the original data set
5×3 Dataset
 Row │ x1        x2        x3
     │ identity  identity  identity
     │ Int64?    Int64?    Float64?
─────┼───────────────────────────────
   1 │        1        -2        0.0
   2 │        2        -1        0.1
   3 │        3   missing        0.2
   4 │        4         1  missing
   5 │        5         2        0.4

In the following example, we map some functions on columns of a data set which one of the columns has a format.

julia> ds = Dataset(x1 = 1:5, x2 = [1,2,1,2,1])
julia> setformat!(ds, 1=>isodd)
5×2 Dataset
 Row │ x1      x2
     │ isodd   identity
     │ Int64?  Int64?
─────┼──────────────────
   1 │   true         1
   2 │  false         2
   3 │   true         1
   4 │  false         2
   5 │   true         1

julia> map(ds, [x -> div(x,2), x -> x/2], :)
5×2 Dataset
 Row │ x1        x2
     │ identity  identity
     │ Int64?    Float64?
─────┼────────────────────
   1 │        0       0.5
   2 │        1       1.0
   3 │        1       0.5
   4 │        2       1.0
   5 │        2       0.5

Function map/map!

Introduction

map!

Examples

map

Examples

Function `map/map!`

`map!`

`map`