> For the complete documentation index, see [llms.txt](https://docs.acho.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.acho.io/acho-studio/data-prep-projects/applying-actions/data-cleaning/cleanse.md).

# Cleanse

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