A data analyst is examining a dataset containing product quantities and sees some entries labeled 'EMPTY' for the quantity. Numeric calculations fail for those entries. Which approach is best for preserving as much usable data as possible while correcting these values?
Filter out and remove all rows that contain any invalid quantity records
Convert the entire quantity field into a text column to accept all formats
Transfer invalid quantity rows into a new table for further storage
Replacing invalid entries with a suitable computed value preserves data rows and keeps quantity fields numeric. A product quantity of EMPTY most likely means the quantity is zero, therefor transforming this value to a 0 numeric value is a reasonable next step.
Removing all invalid rows leads to excessive data loss. Converting the column to text disregards numeric reporting needs. Moving invalid entries to a separate table does not resolve their invalid state for future calculations. The replacement approach, particularly a historical average or median, preserves analytical integrity.
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