Generic Initializer

init(column:)

Creates a new column of integers from a given column whose elements can be converted to integers.

Declaration

init<T>(column: MLDataColumn<T>) where T : MLDataValueConvertible
Available when Element is Int.

Parameters

column

An MLDataColumn of elements convertible to Int.

Discussion

Use this initializer to create a column of integers from another column. Start by creating a column that is convertible to a column of integers.

let stringsColumn = MLDataColumn(["1", "2", "3", "4", "5"])
print(stringsColumn)
// Prints ["1", "2", "3", "4", "5"]

Then use init(column:) to convert the column to a column of integers.

let intsColumn = MLDataColumn<Int>(column: stringsColumn)
print(intsColumn)
// Prints [1, 2, 3, 4, 5]

See Also

Creating a Data Column by Converting Another Column

func map<T>(to: T.Type) -> MLDataColumn<T>

Creates a new column by converting this column to the given type.

init<T>(column: MLDataColumn<T>)

Creates a new column of arrays of integers from a given column whose elements can be converted to an array of integers.

init<T>(column: MLDataColumn<T>)

Creates a new column of doubles from a given column whose elements can be converted to doubles.

init<T>(column: MLDataColumn<T>)

Creates a new column of arrays of doubles from a given column whose elements can be converted to an array of doubles.

init<T>(column: MLDataColumn<T>)

Creates a new column of strings from a given column whose elements can be converted to strings.

init<T>(column: MLDataColumn<T>)

Creates a new column of arrays of strings from a given column whose elements can be converted to an array of strings.

init<T>(column: MLDataColumn<T>)

Creates a new column of machine learning sequences from a given column whose elements can be converted to sequences.

init<T>(column: MLDataColumn<T>)

Creates a new column of machine learning dictionaries from a given column whose elements can be converted to dictionaries.