IsNanMask / Boolean Layer
Create boolean mask columns identifying NaN (Not a Number) values in numeric columns. Similar to numpy isnan() or R's is.nan(). Specifically targets floating-point NaN values, distinct from null/None values.
Common applications:
- Numerical computation validation
- Scientific data cleaning
- Error detection in calculations
- Float arithmetic validation
- Infinite value handling
Example:
| Index | Float Column | NaN Mask |
|---|---|---|
| 0 | 1.5 | false |
| 1 | NaN | true |
| 2 | -3.2 | false |
| 3 | NaN | true |
| 4 | Inf | false |
Mask
[, ...]List of NaN checking operations to perform. Each mask creates a new boolean column. Common scenarios:
- Checking results of mathematical operations
- Validating scientific calculations
- Identifying undefined floating-point results
- Quality control in numerical analysis
At least one mask must be specified.
Select
columnThe column to check for NaN values. Applicable to floating-point columns. The mask will be a boolean column where:
- True indicates NaN value
- False indicates any other value (including infinities)
Particularly useful for detecting undefined mathematical results or floating-point errors.
AsColumn
nameName 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.