What are column datatypes in the Engine?

This article summarizes the different column datatypes in the AI & Analytics Engine.

In a dataset, values in the same column must be of the same datatype. The Engine currently supports the following datatypes:

Datatype	Description	Corresponding Type	Examples
Numeric	Whole numbers (Integers) and fractional numbers (real)	Julia: Float32, Float64, Int, Int32, Int64, BigInt Python: double, int R: numeric, double, int, bigint	-8, -5, -3, 0, 1, 3, 5, 8 -8.53, -0.001, 1.358
Boolean	True or False	Julia: Bool Python: bool, numpy.bool_ R: logical	True, False
Text	Free-form text Category labels JSON strings of attribute-value pairs JSON string representation of arrays/lists	Julia: String, CategoricalVector Python: str, object (pandas) R: character	John Doe New York sunny, cloudy, windy {"shape": "circle", "color": "blue", "size": 5} [1, 2, "a", true, null] [{"height": 183, "weight": 80}, {"height": 159, "weight": 60}]
DateTime	Timestamp indicating date and time	Julia: Date, DateTime Python: str, object (pandas) R: Date, POSIXt	2015-04-03 12:34:56 2015-04-03