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.Examples
Examples
- Example 1
- Signature
To keep only those rows where neither “address” nor “name” is missing
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").
Inputs
Inputs
dataset
required
An input dataset to filter.
Outputs
Outputs
dataset
required
A dataset containing the same columns as the input dataset but including or excluding the matched rows.
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).
Parameters
Parameters
array[string]
required
Names of columns used to detect and filter rows containing missing values.
Array items
Array items
string (ds_in.column)
Each item in array.
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.