ColumnArithmetic

Converter

DashAI.back.converters.simple_converters.ColumnArithmetic

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Combine the selected columns into a new numeric column.

Applies addition, subtraction, multiplication, or division element-wise to the columns selected in scope. Select two columns to operate between them, or one column to operate between it and a fixed constant (e.g. column * 2). The operands are taken from the selection: with two columns the operation is <first column> <op> <second column> in dataset order, which can be reversed with swap_operands (relevant for subtract and divide). Division by zero yields NaN instead of raising an error.

The original columns are left untouched; the result is appended as a new column named output_column_name, or, if not provided, <column_a>_<operation>_<column_b> or <column_a>_<operation>_<constant>.

The output column is Integer when both operands are Integer (a whole-number constant counts as Integer), the operation is add, subtract, or multiply (all of which stay exact on integers), and neither operand column has missing values (since a missing value has no exact integer representation). divide always produces a Float column, since integer division is not exact in general, and any operation involving a Float operand, or an operand column with missing values, also produces a Float column.

Parameters

operation : string, default=add

Arithmetic operation to apply between the selected columns (or between the single selected column and 'constant').

constant, default=None

Fixed number used as the second operand. Only used (and required) when a single column is selected.

swap_operands : boolean, default=False

When two columns are selected, swap the operand order. By default the operation is 'first column' OP 'second column' (in dataset order); enable this to compute 'second' OP 'first' instead. Only affects subtract and divide.

output_column_name, default=None

Name of the resulting column. If null, a name is generated from the operands and the operation.

Methods

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'ColumnArithmetic'

Defined on ColumnArithmetic

Derive the operands from scope and validate them.

Parameters

x : DashAIDataset

The scoped dataset, expected to contain one or two columns.

y : DashAIDataset, optional

Ignored. Defaults to None.

Returns

ColumnArithmetic

The fitted converter instance (self).

get_output_type(self, column_name: str = None) -> DashAI.back.types.dashai_data_type.DashAIDataType

Defined on ColumnArithmetic

Return the output type for the arithmetic result.

Parameters

column_name : str, optional

Not used; the result column always has the same type. Defaults to None.

Returns

DashAIDataType

An Integer type backed by pyarrow.int64(), or a Float type backed by pyarrow.float64().

transform(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on ColumnArithmetic

Compute the arithmetic result and append it as a new column.

Parameters

x : DashAIDataset

The dataset containing the operand column(s) derived during fit.

y : DashAIDataset, optional

Ignored. Defaults to None.

Returns

DashAIDataset

The original dataset with the arithmetic result appended as a new column, typed Integer or Float depending on the operands and the operation (see class docstring).

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type

The converter class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]

Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict

Dictionary representing the Json Schema of the component.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.