DataSchema workflow in Jupyter

After execution of cell

val df = dataFrameOf("name", "age")(
    "Alice", 15,
    "Bob", null,
)

the following actions take place:

Columns in df are analyzed to extract data schema
Empty interface with DataSchema annotation is generated:

@DataSchema
interface DataFrameType

Extension properties for this DataSchema are generated:

val ColumnsContainer<DataFrameType>.age: DataColumn<Int?> @JvmName("DataFrameType_age") get() = this["age"] as DataColumn<Int?>
val DataRow<DataFrameType>.age: Int? @JvmName("DataFrameType_age") get() = this["age"] as Int?
val ColumnsContainer<DataFrameType>.name: DataColumn<String> @JvmName("DataFrameType_name") get() = this["name"] as DataColumn<String>
val DataRow<DataFrameType>.name: String @JvmName("DataFrameType_name") get() = this["name"] as String

Every column produces two extension properties:

Property for ColumnsContainer<DataFrameType> returns column
Property for DataRow<DataFrameType> returns cell value

df variable is typed by schema interface:

val temp = df

val df = temp.cast<DataFrameType>()

To log all these additional code executions, use cell magic

%trackExecution -all

Last modified: 27 September 2024