> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# filter_missing

> Filter rows based on missing values in one or more columns. 

By default keeps only those rows where values in selected columns are not missing (non-NaNs). Using the `exclude`
parameter, the row selection can be inverted, such that only rows with missing values in selected rows
will be returned.

## Usage

The following example shows how the step can be used in a recipe.

<Accordion title="Examples" icon="code" defaultOpen="true">
  <Tabs>
    <Tab title="Example 1">
      To keep only those rows where neither "address" nor "name" is missing

      ```stan theme={null}
      filter_missing(ds, {"columns": ["address", "name"]}) -> (ds_filtered)
      ```
    </Tab>

    <Tab title="Signature">
      General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.

      ```stan theme={null}
      filter_missing(ds_in: dataset, {
          "param": value,
          ...
      }) -> (ds_out: dataset)
      ```
    </Tab>
  </Tabs>
</Accordion>

## Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally
columns (`ds.first_name`), datasets (`ds` or `ds[["first_name", "last_name"]]`) or models (referenced
by name e.g. `"churn-clf"`).

<Accordion title="Inputs" icon="right-to-bracket">
  <ParamField path="ds_in" type="dataset" required>
    An input dataset to filter.
  </ParamField>
</Accordion>

<Accordion title="Outputs" icon="right-from-bracket">
  <ParamField path="ds_out" type="dataset" required>
    A dataset containing the same columns as the input dataset but including or excluding the matched rows.
  </ParamField>
</Accordion>

## Configuration

The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last "input" to the step, i.e. `step(..., {"param": "value", ...}) -> (output)`.

<Accordion title="Parameters" defaultOpen="true" icon="sliders">
  <ParamField path="columns" type="array[string]" required>
    Names of columns used to detect and filter rows containing missing values.

    <Accordion title="Array items">
      <ParamField path="Item" type="string (ds_in.column)">
        Each item in array.
      </ParamField>
    </Accordion>
  </ParamField>

  <ParamField path="exclude" type="boolean" default="false">
    if `true`, rows with *non*-missing values will be excluded.
    I.e., only rows containing missing values in the selected columns will be included in the resulting dataset.
  </ParamField>
</Accordion>