A product CSV can look valid in one Shopify store and still fail in another store with a missing-column error. That does not always mean the product data is broken. Often the source export and the destination store template are not aligned.
Before copying hundreds of rows by hand, I like to separate the template problem from the product-data problem.
- Export a fresh product CSV from the destination store, even if it only contains a test product.
- Compare the header row against the source CSV before changing any product values.
- Keep destination-only columns in place instead of deleting columns that look blank.
- Map values only when the Shopify header name is the same or clearly equivalent.
- Import one or two products first and confirm the intended fields changed.
- Save the mapped file separately so the original export remains untouched.
The easy mistake is treating every CSV as a generic spreadsheet. Shopify product CSV files are closer to templates: the header row tells Shopify what each value means, and small differences in names, spacing, or expected columns can make an otherwise reasonable file fail.
For a first-pass check, you can use the Shopify product CSV template checker here: check the product CSV template. It is most useful before a cross-store import, because it helps you review headers and structure before you spend time moving row data.
This still does not replace a real destination-store test import. The safest workflow is: template check, small mapped sample, import test, then the full product set.
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