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 datatypes. The Engine currently support 5 datatypes. They are as follows:

Datatype Description Corresponding Type Examples
Numeric Integers and continuous real numbers
  • 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
Categorical Discrete categories
  • Julia: CategoricalVector
  • sunny, cloudy, windy
  • spam, not spam
Text Free form text
  • Julia: String
  • Python: str, object (pandas)
  • R: character
  • John Doe
  • New York
DateTime Date time
  • Julia: Date, DateTime
  • Python: str, object (pandas)
  • R: Date, POSIXt
  • 2015-04-03 12:34:56
  • 2015-04-03
JsonObject JSON strings of attribute-value pairs
  • String (in all languages)
  • {"name": "...", "height": 183, "weight": 80}
  • {"shape": "circle", "color": "blue", "size": 5}
JSONArray JSON string representation of arrays/lists
  • String (in all languages)
  • [1, 2, "a", true, null]
  • [{"height": 183, "weight": 80}, {"height": 159, "weight": 60}]