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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