While reviewing an inventory dataset, a data analyst notices that warehouse clerks type the literal string "EMPTY" in the Quantity field whenever an item is out of stock. Because the column is stored as text, any numeric aggregations fail. Which remediation preserves all rows and restores the column to a usable numeric format?
Move rows with "EMPTY" quantities to a separate holding table
Replace each "EMPTY" entry with the numeric value 0
Change the entire Quantity column to a text data type
Delete every row that contains an "EMPTY" quantity
Replacing the word "EMPTY" with the numeric value 0 converts the column to a consistent numeric type and keeps every record available for analysis. In this context, "EMPTY" truly indicates an out-of-stock item, so 0 is the correct business value. Deleting the rows would discard other useful attributes, converting the column to text would still block numeric summaries, and moving the rows elsewhere only postpones the problem.
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Why is replacing 'EMPTY' with 0 the best solution?
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What problems arise when changing the Quantity column to text?
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What is the disadvantage of moving 'EMPTY' rows to another table?