filter_missing(ds_in: dataset, {
    "param": value,
    ...
}) -> (ds_out: dataset)

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.

filter_missing(ds_in: dataset, {
    "param": value,
    ...
}) -> (ds_out: dataset)
​
columns
array[string]
required

Names of columns used to detect and filter rows containing missing values.

​
exclude
boolean

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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