FillNanWithValue / Manipulation Layer
Replace NaN (Not a Number) values in floating-point columns with a specified constant value. Similar to pandas' fillna(value) or R's replace(x, is.nan(x), value).
This operation helps handle floating-point special values that occur from:
- Failed computations (0/0)
- Invalid mathematical operations (log(-1))
- Missing sensor readings
- Undefined results
Common applications:
- Signal processing (replacing artifacts)
- Financial data cleaning (replacing invalid prices)
- Scientific computing (handling computation errors)
- Sensor data preprocessing (fixing invalid readings)
- Machine learning dataset preparation
Note: Only affects floating-point columns; other data types remain unchanged as they cannot contain NaN values.
Transforms
[, ...]Specifies a single column transformation replacing NaN values with a constant. This provides a way to substitute invalid or undefined floating-point values with a known, meaningful value appropriate for subsequent analysis.
SelectNan
columnThe floating-point column containing NaN values to replace. Common sources:
- Mathematical operations (division by zero)
- Statistical computations (undefined moments)
- Data import errors
- Sensor malfunctions Non-floating-point columns will remain unchanged.
Value
f64The constant value to replace NaN with. Common choices:
- 0.0 for additive operations
- 1.0 for multiplicative operations
- Domain-specific defaults (e.g., -999.9 for weather data)
- Neutral values for specific calculations
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.