krangl in 10 Minutes

Welcome to krangl. Relational data and how to handle it properly is a huge topic, but the core concepts are relatively simple. So let's get started!

Columns and Rows

DataFrames are just tables with type constraints within each column. To glance into them horizontally and vertically we can do

irisData.print(maxRows=10)
irisData.schema()

irisData is bundled with krangl, and gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Columns and Rows can be accessed using krangl with

val col = irisData["Species"]
val cell = irisData["Species"][1]

Get your data into krangl

To save a data frame simply use

irisData.writeCSV(File("my_iris.txt"))

To load a data-frame simply {done}

irisData.writeCSV(File("my_iris.txt"))

It allows to Read from tsv, csv, json, jdbc, e.g.

val tornados = DataFrame.readCSV(pathAsStringFileOrUrl)
tornados.writeCSV(File("tornados.txt.gz"))

krangl will guess column types unless the user provides a column type model.

You can also simply define new data-frames in place

val users : DataFrame = dataFrameOf(
"firstName", "lastName", "age", "hasSudo")(
"max", "smith" , 53, false,
"eva", "miller", 23, true,
null , "meyer" , 23, null
)

krangl also allows to convert any iterable into a data-frame via reflection. See the section about Nested Data for details.

Other input formats

krangl also allows to read in json array data. For a complete overview see JsonIO‚Äč

val df = fromJson("my.json")
val df2 = fromJson("http://foo.bar/my.json")