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Filter data early

If you know that you are dropping some deleting some rows and columns from your dataset, add these transformation steps early in your recipe. This reduction simplifies working with the content through the application and, at execution, speeds the processing of the remaining valid data. Since you may be executing your job multiple times before it is finalized, it should also speed your development process.

  • To drop delete columns:
    • Select Drop Delete from the column drop-down for individual columns. See Column Menus.
    • Use the Delete Columns transformation to remove multiple discrete columns or ranges of columns.
  • To delete rows: The following example removes all rows that lack a value for the id column:

    D trans
    p03Valuedelete matching rows
    Typestep
    p01NameCondition
    p01ValueIs missing
    p02NameColumn
    p02Valueid
    p03NameAction
    SearchTermFilter rows

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