filter_missing

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.

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Usage

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

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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").

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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).