> For the complete documentation index, see [llms.txt](https://doc.verteego.com/verteego-doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.verteego.com/verteego-doc/pipelines/forecasting-pipelines/calculators/transformation/fillna.md).

# fillna

Fills the NaN values of a column.

## Usage

{% hint style="info" %}
Fill the NaN values of a column
{% endhint %}

This calculator can be used with the following method:

<mark style="color:red;">**`fillna`**</mark>

Examples:

* Fill the quantities of my target variable that are not provided with 0
* Fill the NA values in the `product category` column with `other` value.

***

## Main Parameters

{% hint style="success" %}
**The bold options** represent the default values when the parameters are optional.
{% endhint %}

* *<mark style="color:blue;">input\_columns</mark>* \
  list of columns used as input of the calculators
* *<mark style="color:blue;">output\_columns</mark>* \
  list of columns added by the calculators
* *<mark style="color:blue;">global</mark>* *(true, **false)*** \
  Should this calculator be performed before data splitting during training for cross-validation
* *<mark style="color:blue;">steps</mark>* \[optionnal] *(**training, prediction**, postprocessing*)\
  List of steps in a pipeline where columns from this calculator are added to the data. Note that when the training option is listed, the calculator is actually added during preprocessing.
* *<mark style="color:blue;">store\_in\_model</mark>* \[optionnal] *(true, **false)*** \
  Please indicate whether the "calculated" columns by the calculator should be stored in the model or not to avoid recalculating them during prediction. This is only relevant if the calculated columns are added to both training and prediction. Without this parameter, the values will not be stored in the model. The following parameters only make sense if this parameter is set to *true*.
* *<mark style="color:blue;">stored\_columns</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> is true*] \
  List indicating the columns to be stored among the *<mark style="color:blue;">output\_columns</mark>*.
* *<mark style="color:blue;">stored\_keys</mark>* \[required if *<mark style="color:blue;">store\_in\_model</mark> is true*] \
  List indicating the columns to use for identifying the correct values to join on the data for prediction among the stored values (logically, they are to be chosen from the *<mark style="color:blue;">input\_columns</mark>*).

***

## Specific Parameters

* *<mark style="color:blue;">fill\_value</mark>*\
  Value to use for filling
* *<mark style="color:blue;">limit</mark>*\
  Max number of consecutive values to fill

***

## Examples

1. Let's imagine that our quantity column has NaN values on Saturdays and Sundays, and we want to have 0s for these days. Therefore, we can use the calculator to replace the NaNs with 0s for these two days while limiting the number of consecutive values to be filled to two

   ```yaml
   calculated_cols:
     calc_duration:
       method: fillna
       input_columns:
       - qty
       output_columns:
       - filled_col
       params:
         fill_value: 0
         limit: 2
   ```