# FILL_NA
URL: https://docs.valsight.ai/fill-na/
Description: FILL_NA fills missing values (N/A) that result from pivoting a dimension into columns, replacing gaps with a specified value (default 0) on existing level combinations.
**Category:** [Filtering & data shaping](/filtering-data-shaping/)

## Overview

The **FILL\_NA** function **fills missing values (N/A) on existing level combinations** after the specified dimension is pivoted into columns.

Use this function when reshaping or pivoting creates missing cells and you want to explicitly fill those gaps with a specific value.

## Syntax

`FILL_NA('Node' [, "Dimension" [, Value]])`

## Parameters

| Parameter     | Description                                                             | Type           | Required | Default                             |
| ------------- | ----------------------------------------------------------------------- | -------------- | -------- | ----------------------------------- |
| **Node**      | Node that may contain missing values (N/A).                             | Node reference | Yes      | --                                  |
| **Dimension** | Name of the dimension to pivot into columns before filling.             | Dimension name | No       | `"Time"` at the node's finest level |
| **Value**     | Value written into former missing cells on existing level combinations. | Number         | No       | `0`                                 |

## Watch out

* The node must already contain levels of the **specified dimension**.
* The function fills only where the dimension is **pivoted into columns**.
* It does **not** fill the complete time horizon and does not create missing level values that are not present in the input node (e.g., absent products). To fill missing values, use [FILLMISSING](/fillmissing/) or [FILLMISSING\_LAST](/fillmissing-last/).
* Linked levels of the chosen dimension also appear as columns in the pivoted result.

## Examples

### Filling missing time values with 0 (default)

This example fills missing values after pivoting the time dimension into columns. The default fill value is 0.

The time horizon of the project is 2025-2028.

The dimension `"ProductDimension"` consists of the level `"Product"` and the level values `"Car"`, `"Van"`, and `"Truck"`.

The input node features the level values `"2026"`, `"2028"` and `"Car"`, `"Van"`.

**Input node:** `'Input node'`

| Year | Product | Value |
| ---- | ------- | ----- |
| 2026 | Car     | 100   |
| 2028 | Van     | 90    |

Pivoted `Input node` by dimension `"Time"`

| Product | 2026 | 2028 |
| ------- | ---- | ---- |
| Car     | 100  | N/A  |
| Van     | N/A  | 90   |

**Formula:** `FILL_NA('Input node')`

**Equivalent to:** `FILL_NA('Input node', "Time", 0)`

| Product | 2026 | 2028 |
| ------- | ---- | ---- |
| Car     | 100  | 0    |
| Van     | 0    | 90   |

This function fills the missing level values of the finest time dimension with 0. Notice that the formula does not fill the complete time horizon (e.g. expands the years `"2025"` and `"2027"`).

### Filling along a custom dimension with a custom value

This example fills missing values along the ProductDimension using 16 as the fill value instead of the default 0.

**Formula:** `FILL_NA('Input node', "ProductDimension", 16)`

| Year | Car | Van |
| ---- | --- | --- |
| 2026 | 100 | 16  |
| 2028 | 16  | 90  |

This function fills the missing level values of the dimension `"ProductDimension"` with `16`. Notice that the formula does not add any values available in the dimension that are not present in the input (e.g. `"Truck"`).

## Related

| Function                                | When to use instead                                                                               |
| --------------------------------------- | ------------------------------------------------------------------------------------------------- |
| [FILLMISSING](/fillmissing/)            | When you want to add missing level values for a dimension, not just fill N/A cells from pivoting. |
| [FILLMISSING\_LAST](/fillmissing-last/) | When you want to fill missing values using the last available value instead of a constant.        |