Initializer

init(namedColumns:)

Creates a data table from a dictionary of column names and untyped columns.

Declaration

init(namedColumns: [String : MLUntypedColumn]) throws

Parameters

namedColumns

The dictionary of each column name and its associated untyped column data.

Discussion

Use this initializer to create a data table from untyped columns.

A table of information about a book. Columns named "Title", "Author", "Pages", and "Genre". The first row is "Alice in Wonderland", "Lewis Carroll", "124", and "Fantasy".

For example, to create a data table as shown above, first create your untyped columns.

let pages = MLUntypedColumn([124, 98, 280, 94])
let genre = MLUntypedColumn(["Fantasy", "Drama", "Adventure", "Fantasy"])
let title = MLUntypedColumn(["Alice in Wonderland", "Hamlet", "Treasure Island", "Peter Pan"])
let author = MLUntypedColumn(["Lewis Carroll", "William Shakespeare", "Robert L. Stevenson", "J. M. Barrie"])

Then, use init(namedColumns:) to create a data table from the columns paired with their names.

let bookTable = try MLDataTable(namedColumns: ["Title": title,
                                               "Author": author,
                                               "Pages": pages,
                                               "Genre": genre])

See Also

Creating a Data Table

Creating a Model from Tabular Data

Train a machine learning model by using Core ML to import and manage tabular data.

init(contentsOf: URL, options: MLDataTable.ParsingOptions)

Creates a data table from an imported JSON or CSV file.

init(dictionary: [String : MLDataValueConvertible])

Creates a data table from a dictionary of column names and data values.

init()

Creates an empty table containing no rows or columns.