Reshaping and Pivoting Data
Reshape data from wide to long format using the stack function:
julia> using DataFrames, CSV
julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../test/data/iris.csv"));
julia> head(iris)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼────────────┼─────────────┼────────────┼───────────────┤
│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ setosa │
│ 2 │ 4.9 │ 3.0 │ 1.4 │ 0.2 │ setosa │
│ 3 │ 4.7 │ 3.2 │ 1.3 │ 0.2 │ setosa │
│ 4 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ setosa │
│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ setosa │
│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ setosa │
julia> tail(iris)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼────────────┼─────────────┼────────────┼───────────────┤
│ 1 │ 6.7 │ 3.3 │ 5.7 │ 2.5 │ virginica │
│ 2 │ 6.7 │ 3.0 │ 5.2 │ 2.3 │ virginica │
│ 3 │ 6.3 │ 2.5 │ 5.0 │ 1.9 │ virginica │
│ 4 │ 6.5 │ 3.0 │ 5.2 │ 2.0 │ virginica │
│ 5 │ 6.2 │ 3.4 │ 5.4 │ 2.3 │ virginica │
│ 6 │ 5.9 │ 3.0 │ 5.1 │ 1.8 │ virginica │
julia> d = stack(iris, 1:4);
julia> head(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼──────────┼───────────────┤
│ 1 │ SepalLength │ 5.1 │ setosa │
│ 2 │ SepalLength │ 4.9 │ setosa │
│ 3 │ SepalLength │ 4.7 │ setosa │
│ 4 │ SepalLength │ 4.6 │ setosa │
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │
julia> tail(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼────────────┼──────────┼───────────────┤
│ 1 │ PetalWidth │ 2.5 │ virginica │
│ 2 │ PetalWidth │ 2.3 │ virginica │
│ 3 │ PetalWidth │ 1.9 │ virginica │
│ 4 │ PetalWidth │ 2.0 │ virginica │
│ 5 │ PetalWidth │ 2.3 │ virginica │
│ 6 │ PetalWidth │ 1.8 │ virginica │The second optional argument to stack indicates the columns to be stacked. These are normally referred to as the measured variables. Column names can also be given:
julia> d = stack(iris, [:SepalLength, :SepalWidth, :PetalLength, :PetalWidth]);
julia> head(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼──────────┼───────────────┤
│ 1 │ SepalLength │ 5.1 │ setosa │
│ 2 │ SepalLength │ 4.9 │ setosa │
│ 3 │ SepalLength │ 4.7 │ setosa │
│ 4 │ SepalLength │ 4.6 │ setosa │
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │
julia> tail(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼────────────┼──────────┼───────────────┤
│ 1 │ PetalWidth │ 2.5 │ virginica │
│ 2 │ PetalWidth │ 2.3 │ virginica │
│ 3 │ PetalWidth │ 1.9 │ virginica │
│ 4 │ PetalWidth │ 2.0 │ virginica │
│ 5 │ PetalWidth │ 2.3 │ virginica │
│ 6 │ PetalWidth │ 1.8 │ virginica │
Note that all columns can be of different types. Type promotion follows the rules of vcat.
The stacked DataFrame that results includes all of the columns not specified to be stacked. These are repeated for each stacked column. These are normally refered to as identifier (id) columns. In addition to the id columns, two additional columns labeled :variable and :values contain the column identifier and the stacked columns.
A third optional argument to stack represents the id columns that are repeated. This makes it easier to specify which variables you want included in the long format:
julia> d = stack(iris, [:SepalLength, :SepalWidth], :Species);
julia> head(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼──────────┼───────────────┤
│ 1 │ SepalLength │ 5.1 │ setosa │
│ 2 │ SepalLength │ 4.9 │ setosa │
│ 3 │ SepalLength │ 4.7 │ setosa │
│ 4 │ SepalLength │ 4.6 │ setosa │
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │
julia> tail(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼────────────┼──────────┼───────────────┤
│ 1 │ SepalWidth │ 3.3 │ virginica │
│ 2 │ SepalWidth │ 3.0 │ virginica │
│ 3 │ SepalWidth │ 2.5 │ virginica │
│ 4 │ SepalWidth │ 3.0 │ virginica │
│ 5 │ SepalWidth │ 3.4 │ virginica │
│ 6 │ SepalWidth │ 3.0 │ virginica │melt is an alternative function to reshape from wide to long format. It is based on stack, but it prefers specification of the id columns as:
julia> d = melt(iris, :Species);
julia> head(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼──────────┼───────────────┤
│ 1 │ SepalLength │ 5.1 │ setosa │
│ 2 │ SepalLength │ 4.9 │ setosa │
│ 3 │ SepalLength │ 4.7 │ setosa │
│ 4 │ SepalLength │ 4.6 │ setosa │
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │
julia> tail(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼────────────┼──────────┼───────────────┤
│ 1 │ PetalWidth │ 2.5 │ virginica │
│ 2 │ PetalWidth │ 2.3 │ virginica │
│ 3 │ PetalWidth │ 1.9 │ virginica │
│ 4 │ PetalWidth │ 2.0 │ virginica │
│ 5 │ PetalWidth │ 2.3 │ virginica │
│ 6 │ PetalWidth │ 1.8 │ virginica │unstack converts from a long format to a wide format. The default is requires specifying which columns are an id variable, column variable names, and column values:
julia> iris[:id] = 1:size(iris, 1)
1:150
julia> longdf = melt(iris, [:Species, :id]);
julia> head(longdf)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
├─────┼─────────────┼──────────┼───────────────┼───────┤
│ 1 │ SepalLength │ 5.1 │ setosa │ 1 │
│ 2 │ SepalLength │ 4.9 │ setosa │ 2 │
│ 3 │ SepalLength │ 4.7 │ setosa │ 3 │
│ 4 │ SepalLength │ 4.6 │ setosa │ 4 │
│ 5 │ SepalLength │ 5.0 │ setosa │ 5 │
│ 6 │ SepalLength │ 5.4 │ setosa │ 6 │
julia> tail(longdf)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
├─────┼────────────┼──────────┼───────────────┼───────┤
│ 1 │ PetalWidth │ 2.5 │ virginica │ 145 │
│ 2 │ PetalWidth │ 2.3 │ virginica │ 146 │
│ 3 │ PetalWidth │ 1.9 │ virginica │ 147 │
│ 4 │ PetalWidth │ 2.0 │ virginica │ 148 │
│ 5 │ PetalWidth │ 2.3 │ virginica │ 149 │
│ 6 │ PetalWidth │ 1.8 │ virginica │ 150 │
julia> widedf = unstack(longdf, :id, :variable, :value);
julia> head(widedf)
6×5 DataFrame
│ Row │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
├─────┼───────┼─────────────┼────────────┼─────────────┼────────────┤
│ 1 │ 1 │ 1.4 │ 0.2 │ 5.1 │ 3.5 │
│ 2 │ 2 │ 1.4 │ 0.2 │ 4.9 │ 3.0 │
│ 3 │ 3 │ 1.3 │ 0.2 │ 4.7 │ 3.2 │
│ 4 │ 4 │ 1.5 │ 0.2 │ 4.6 │ 3.1 │
│ 5 │ 5 │ 1.4 │ 0.2 │ 5.0 │ 3.6 │
│ 6 │ 6 │ 1.7 │ 0.4 │ 5.4 │ 3.9 │
julia> tail(widedf)
6×5 DataFrame
│ Row │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
├─────┼───────┼─────────────┼────────────┼─────────────┼────────────┤
│ 1 │ 145 │ 5.7 │ 2.5 │ 6.7 │ 3.3 │
│ 2 │ 146 │ 5.2 │ 2.3 │ 6.7 │ 3.0 │
│ 3 │ 147 │ 5.0 │ 1.9 │ 6.3 │ 2.5 │
│ 4 │ 148 │ 5.2 │ 2.0 │ 6.5 │ 3.0 │
│ 5 │ 149 │ 5.4 │ 2.3 │ 6.2 │ 3.4 │
│ 6 │ 150 │ 5.1 │ 1.8 │ 5.9 │ 3.0 │If the remaining columns are unique, you can skip the id variable and use:
julia> longdf = melt(iris, [:Species, :id]);
julia> head(longdf)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
├─────┼─────────────┼──────────┼───────────────┼───────┤
│ 1 │ SepalLength │ 5.1 │ setosa │ 1 │
│ 2 │ SepalLength │ 4.9 │ setosa │ 2 │
│ 3 │ SepalLength │ 4.7 │ setosa │ 3 │
│ 4 │ SepalLength │ 4.6 │ setosa │ 4 │
│ 5 │ SepalLength │ 5.0 │ setosa │ 5 │
│ 6 │ SepalLength │ 5.4 │ setosa │ 6 │
julia> widedf = unstack(longdf, :variable, :value);
julia> head(widedf)
6×6 DataFrame
│ Row │ Species │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Categorical…⍰ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
├─────┼───────────────┼───────┼─────────────┼────────────┼─────────────┼────────────┤
│ 1 │ setosa │ 1 │ 1.4 │ 0.2 │ 5.1 │ 3.5 │
│ 2 │ setosa │ 2 │ 1.4 │ 0.2 │ 4.9 │ 3.0 │
│ 3 │ setosa │ 3 │ 1.3 │ 0.2 │ 4.7 │ 3.2 │
│ 4 │ setosa │ 4 │ 1.5 │ 0.2 │ 4.6 │ 3.1 │
│ 5 │ setosa │ 5 │ 1.4 │ 0.2 │ 5.0 │ 3.6 │
│ 6 │ setosa │ 6 │ 1.7 │ 0.4 │ 5.4 │ 3.9 │stackdf and meltdf are two additional functions that work like stack and melt, but they provide a view into the original wide DataFrame. Here is an example:
julia> d = stackdf(iris);
julia> head(d)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
├─────┼─────────────┼──────────┼───────────────┼───────┤
│ 1 │ SepalLength │ 5.1 │ setosa │ 1 │
│ 2 │ SepalLength │ 4.9 │ setosa │ 2 │
│ 3 │ SepalLength │ 4.7 │ setosa │ 3 │
│ 4 │ SepalLength │ 4.6 │ setosa │ 4 │
│ 5 │ SepalLength │ 5.0 │ setosa │ 5 │
│ 6 │ SepalLength │ 5.4 │ setosa │ 6 │
julia> tail(d)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
├─────┼────────────┼──────────┼───────────────┼───────┤
│ 1 │ PetalWidth │ 2.5 │ virginica │ 145 │
│ 2 │ PetalWidth │ 2.3 │ virginica │ 146 │
│ 3 │ PetalWidth │ 1.9 │ virginica │ 147 │
│ 4 │ PetalWidth │ 2.0 │ virginica │ 148 │
│ 5 │ PetalWidth │ 2.3 │ virginica │ 149 │
│ 6 │ PetalWidth │ 1.8 │ virginica │ 150 │This saves memory. To create the view, several AbstractVectors are defined:
:variable column – EachRepeatedVector This repeats the variables N times where N is the number of rows of the original AbstractDataFrame.
:value column – StackedVector This is provides a view of the original columns stacked together.
Id columns – RepeatedVector This repeats the original columns N times where N is the number of columns stacked.
None of these reshaping functions perform any aggregation. To do aggregation, use the split-apply-combine functions in combination with reshaping. Here is an example:
julia> d = melt(iris, :Species);
julia> head(d)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
├─────┼─────────────┼──────────┼───────────────┤
│ 1 │ SepalLength │ 5.1 │ setosa │
│ 2 │ SepalLength │ 4.9 │ setosa │
│ 3 │ SepalLength │ 4.7 │ setosa │
│ 4 │ SepalLength │ 4.6 │ setosa │
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │
julia> x = by(d, [:variable, :Species], df -> DataFrame(vsum = mean(df[:value])));
julia> head(x)
6×3 DataFrame
│ Row │ variable │ Species │ vsum │
│ │ Symbol │ Categorical…⍰ │ Float64 │
├─────┼─────────────┼───────────────┼─────────┤
│ 1 │ SepalLength │ setosa │ 5.006 │
│ 2 │ SepalLength │ versicolor │ 5.936 │
│ 3 │ SepalLength │ virginica │ 6.588 │
│ 4 │ SepalWidth │ setosa │ 3.428 │
│ 5 │ SepalWidth │ versicolor │ 2.77 │
│ 6 │ SepalWidth │ virginica │ 2.974 │
julia> head(unstack(x, :Species, :vsum))
5×4 DataFrame
│ Row │ variable │ setosa │ versicolor │ virginica │
│ │ Symbol │ Float64⍰ │ Float64⍰ │ Float64⍰ │
├─────┼─────────────┼──────────┼────────────┼───────────┤
│ 1 │ PetalLength │ 1.462 │ 4.26 │ 5.552 │
│ 2 │ PetalWidth │ 0.246 │ 1.326 │ 2.026 │
│ 3 │ SepalLength │ 5.006 │ 5.936 │ 6.588 │
│ 4 │ SepalWidth │ 3.428 │ 2.77 │ 2.974 │
│ 5 │ id │ 25.5 │ 75.5 │ 125.5 │