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| using DataFrames | |
| showln(x) = (show(x); println()) | |
| # TODO: needs more links to docs. | |
| # A DataFrame is an in-memory database | |
| df = DataFrame(A = [1, 2], B = [e, pi], C = ["xx", "xy"]) | |
| showln(df) | |
| #> 2x3 DataFrame | |
| #> |-------|---|---------|------| | |
| #> | Row # | A | B | C | | |
| #> | 1 | 1 | 2.71828 | "xx" | | |
| #> | 2 | 2 | 3.14159 | "xy" | | |
| # The columns of a DataFrame can be indexed using numbers or names | |
| showln(df[1]) | |
| #> [1,2] | |
| showln(df[:A]) | |
| #> [1,2] | |
| showln(df[2]) | |
| #> [2.718281828459045,3.141592653589793] | |
| showln(df[:B]) | |
| #> [2.718281828459045,3.141592653589793] | |
| showln(df[3]) | |
| #> String["xx","xy"] | |
| showln(df[:C]) | |
| #> String["xx","xy"] | |
| # The rows of a DataFrame can be indexed only by using numbers | |
| showln(df[1, :]) | |
| #> 1x3 DataFrame | |
| #> |-------|---|---------|------| | |
| #> | Row # | A | B | C | | |
| #> | 1 | 1 | 2.71828 | "xx" | | |
| showln(df[1:2, :]) | |
| #> 2x3 DataFrame | |
| #> |-------|---|---------|------| | |
| #> | Row # | A | B | C | | |
| #> | 1 | 1 | 2.71828 | "xx" | | |
| #> | 2 | 2 | 3.14159 | "xy" | | |
| # importing data into DataFrames | |
| # ------------------------------ | |
| # DataFrames can be loaded from CSV files using readtable() | |
| iris = readtable("iris.csv") | |
| # the iris dataset (and plenty of others) is also available from | |
| using RData, RDatasets | |
| iris = dataset("datasets","iris") | |
| # you can directly import your own R .rda dataframe with | |
| # mydf = DataFrame(read_rda("path/to/your/df.rda")["name_of_df"]), e.g. | |
| diamonds = DataFrame(load(joinpath(Pkg.dir("RDatasets"),"data","ggplot2","diamonds.rda"))["diamonds"]) | |
| # showing DataFrames | |
| # ------------------ | |
| # Check the names and element types of the columns of our new DataFrame | |
| showln(names(iris)) | |
| #> [:SepalLength,:SepalWidth,:PetalLength,:PetalWidth,:Species] | |
| showln(eltypes(iris)) | |
| #> Type[Float64,Float64,Float64,Float64,UTF8String] | |
| # Subset the DataFrame to only include rows for one species | |
| showln(iris[iris[:Species] .== "setosa", :]) | |
| #> 50x5 DataFrame | |
| #> |-------|-------------|------------|-------------|------------|----------| | |
| #> | Row # | SepalLength | SepalWidth | PetalLength | PetalWidth | Species | | |
| #> | 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" | | |
| #> | 7 | 4.6 | 3.4 | 1.4 | 0.3 | "setosa" | | |
| #> | 8 | 5.0 | 3.4 | 1.5 | 0.2 | "setosa" | | |
| #> | 9 | 4.4 | 2.9 | 1.4 | 0.2 | "setosa" | | |
| #> ⋮ | |
| #> | 41 | 5.0 | 3.5 | 1.3 | 0.3 | "setosa" | | |
| #> | 42 | 4.5 | 2.3 | 1.3 | 0.3 | "setosa" | | |
| #> | 43 | 4.4 | 3.2 | 1.3 | 0.2 | "setosa" | | |
| #> | 44 | 5.0 | 3.5 | 1.6 | 0.6 | "setosa" | | |
| #> | 45 | 5.1 | 3.8 | 1.9 | 0.4 | "setosa" | | |
| #> | 46 | 4.8 | 3.0 | 1.4 | 0.3 | "setosa" | | |
| #> | 47 | 5.1 | 3.8 | 1.6 | 0.2 | "setosa" | | |
| #> | 48 | 4.6 | 3.2 | 1.4 | 0.2 | "setosa" | | |
| #> | 49 | 5.3 | 3.7 | 1.5 | 0.2 | "setosa" | | |
| #> | 50 | 5.0 | 3.3 | 1.4 | 0.2 | "setosa" | | |
| # Count the number of rows for each species | |
| showln(by(iris, :Species, df -> size(df, 1))) | |
| #> 3x2 DataFrame | |
| #> |-------|--------------|----| | |
| #> | Row # | Species | x1 | | |
| #> | 1 | "setosa" | 50 | | |
| #> | 2 | "versicolor" | 50 | | |
| #> | 3 | "virginica" | 50 | | |
| # Discretize entire columns at a time | |
| iris[:SepalLength] = round.(Integer, iris[:SepalLength]) | |
| iris[:SepalWidth] = round.(Integer, iris[:SepalWidth]) | |
| # Tabulate data according to discretized columns to see "clusters" | |
| tabulated = by( | |
| iris, | |
| [:Species, :SepalLength, :SepalWidth], | |
| df -> size(df, 1) | |
| ) | |
| showln(tabulated) | |
| #> 17x4 DataFrame | |
| #> |-------|--------------|-------------|------------|----| | |
| #> | Row # | Species | SepalLength | SepalWidth | x1 | | |
| #> | 1 | "setosa" | 4 | 3 | 4 | | |
| #> | 2 | "setosa" | 5 | 2 | 1 | | |
| #> | 3 | "setosa" | 5 | 3 | 23 | | |
| #> | 4 | "setosa" | 5 | 4 | 17 | | |
| #> | 5 | "setosa" | 6 | 4 | 5 | | |
| #> | 6 | "versicolor" | 5 | 2 | 3 | | |
| #> | 7 | "versicolor" | 5 | 3 | 3 | | |
| #> | 8 | "versicolor" | 6 | 2 | 6 | | |
| #> | 9 | "versicolor" | 6 | 3 | 29 | | |
| #> | 10 | "versicolor" | 7 | 3 | 9 | | |
| #> | 11 | "virginica" | 5 | 3 | 1 | | |
| #> | 12 | "virginica" | 6 | 2 | 1 | | |
| #> | 13 | "virginica" | 6 | 3 | 22 | | |
| #> | 14 | "virginica" | 7 | 3 | 19 | | |
| #> | 15 | "virginica" | 7 | 4 | 1 | | |
| #> | 16 | "virginica" | 8 | 3 | 4 | | |
| #> | 17 | "virginica" | 8 | 4 | 2 | | |
| # you can setup a grouped dataframe like this | |
| gdf = groupby(iris,[:Species, :SepalLength, :SepalWidth]) | |
| # and then iterate over it | |
| for idf in gdf | |
| println(size(idf,1)) | |
| end | |
| # Adding/Removing columns | |
| # ----------------------- | |
| # insert!(df::DataFrame,index::Int64,item::AbstractArray{T,1},name::Symbol) | |
| # insert random numbers at col 5: | |
| insert!(iris, 5, rand(nrow(iris)), :randCol) | |
| # remove it | |
| delete!(iris, :randCol) |