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