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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.

Examples

  • Example 1
  • Signature
To keep only those rows where neither “address” nor “name” is missing
filter_missing(ds, {"columns": ["address", "name"]} -> (ds_filtered)

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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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ds_in
dataset
required
An input dataset to filter.
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ds_out
dataset
required
A dataset containing the same columns as the input dataset but including or excluding the matched rows.

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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).

Parameters

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columns
array[string]
required
Names of columns used to detect and filter rows containing missing values.
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Item
string (ds_in.column)
Each item in array.
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exclude
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.
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