IsNotNanMask / Boolean Layer

Create boolean mask columns identifying non-NaN (valid number) values in numeric columns. Similar to numpy ~isnan() or R's !is.nan(). Identifies all valid numeric values, including infinities.

Common applications:

Valid numerical data filtering
Mathematical operation preprocessing
Statistical analysis preparation
Scientific data validation
Numerical quality assurance

Example:

Index	Float Column	Not NaN Mask
0	1.5	true
1	NaN	false
2	-3.2	true
3	NaN	false
4	Inf	true

Table

Mask

[, ...]

List of non-NaN checking operations to perform. Each mask creates a new boolean column. Common scenarios:

Preparing data for numerical analysis
Filtering valid floating-point values
Identifying usable numerical records
Quality control in scientific computing

At least one mask must be specified.

Select

column

The column to check for non-NaN values. Applicable to floating-point columns. The mask will be a boolean column where:

True indicates any valid number (including infinities)
False indicates NaN value

Useful for filtering valid numerical data for further processing or analysis.

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.