# 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 --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.acho.io/acho-studio/data-prep-projects/applying-actions/data-cleaning/cleanse.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.