The Cleanse action can help eliminate some of the impurities within the dataset to allow better post-processing. For instance, due to human error, there are leading spaces in some cells within a column, "Female" vs. " Female", causing the data to arbitrarily inflate in unique categories. Or in another example, some values are "nulls" within a numeric field, forcing the field to become a string field and lose all the numeric features.

Acho currently supports cleaning action at the column level, and the available cleaning actions are:

  • cut string

    • from left

    • from right

  • remove a specific character

  • padding with a rule

    • from left

    • from right

  • uppercase/lowercase standardization

    • capitalize all characters

    • capitalize the first character

    • decapitalize all character

  • trim leading zeros

  • trim spaces

    • from both left and right

    • from left

    • from right

  • replacing string nulls with blanks

  • replacing numeric nulls with zero

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