FillNullInterpolate / Manipulation Layer

Fill missing values using interpolation methods. Similar to pandas' interpolate() or R's approx().

Mathematical concepts: Linear: $y = y_{1} + \frac{( x - x _{1} ) ( y _{2} - y _{1} )}{x _{2} - x _{1}}$ Nearest: $y = y_{i}$ where $i = ar g min_{j} ∣ x - x_{j} ∣$

Common applications:

Time series gap filling
Sensor data completion
Missing measurement estimation
Sequential data reconstruction
Trend continuity preservation
Regular interval restoration
Signal reconstruction
Spatial data completion

Example: Fill gaps in temperature readings while preserving the underlying pattern.

Table

Transforms

[, ...]

Define interpolation parameters for filling missing values. Interpolation preserves data patterns while estimating missing values based on surrounding data points.

SelectFill

column

Column containing null values to interpolate. Typical scenarios:

Regular measurements with gaps
Sequential readings with missing points
Time series with missing intervals
Ordered data with blank spaces

Method

enum

Nearest

Interpolation method for estimating missing values. Choose based on data characteristics and required accuracy.

Nearest ~

Use nearest non-null value. Best for:

Categorical-like numeric data
Step-function patterns
Discrete state changes
Non-continuous trends

Linear ~

Straight line between points. Suitable for:

Continuous measurements
Gradual changes
Regular patterns
Smooth transitions

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.