Data model (data)ΒΆ

Orange stores data in classes. The most commonly used storage is, which stores all data in two-dimensional numpy arrays. Each row of the data represents a data instance.

Individual data instances are represented as instances of Different storage classes may derive subclasses of Instance to represent the retrieved rows in the data more efficiently and to allow modifying the data through modifying data instance. For example, if table is, table[0] returns the row as

Every storage class and data instance has an associated domain description domain (an instance of that stores descriptions of data columns. Every column is described by an instance of a class derived from The subclasses correspond to continuous variables (, discrete variables ( and string variables ( These descriptors contain the variable’s name, symbolic values, number of decimals in printouts and similar.

The data is divided into attributes (features, independent variables), class variables (classes, targets, outcomes, dependent variables) and meta attributes. This division applies to domain descriptions, data storages that contain separate arrays for each of the three parts of the data and data instances.

Attributes and classes are represented with numeric values and are used in modelling. Meta attributes contain additional data which may be of any type. (Currently, only string values are supported in addition to continuous and numeric.)

In indexing, columns can be referred to by their names, descriptors or an integer index. For example, if inst is a data instance and var is a descriptor of type Continuous, referring to the first column in the data, which is also names “petal length”, then inst[var], inst[0] and inst[“petal length”] refer to the first value of the instance. Negative indices are used for meta attributes, starting with -1.

Continuous and discrete values can be represented by any numerical type; by default, Orange uses double precision (64-bit) floats. Discrete values are represented by whole numbers.