Stock like 10+ or "in stock" in a CSV: find and fix

Your product CSV has stock cells such as "10+", "5-10", "in stock", "yes" or "var" (Turkish, meaning "there is some"). A stock column is meant to hold a count of sellable items. Depending on the program, a row like this may be rejected, skipped or read as zero, and the stock your shop shows can then differ from what is on the shelf. Which of these happens was not tested here; check your own import guide.

Why it happens. These values are written for people, not for an import. "10+" means at least ten and "5-10" means somewhere from five to ten; neither gives the exact count. "In stock", "yes", "var" and "available" say the item can be sold, but not how many. "Few" and "many" give no count at all. Such values often come from a supplier list, a sheet shared with staff, or text copied from a web page. An import cannot turn them into a count without guessing.

This post covers ranges and availability words only. For placeholders such as N/A, TBD or call us, see "Text in a CSV price or stock column: find and fix". Stock at zero or below has its own post, "Zero or negative CSV price and stock: find and fix". For values such as 2.5 or 0,5, see "Fractional stock quantities in a CSV: find and fix".

Find them. These steps were not tried in a spreadsheet for this post, so try them on a copy first. 1. Make a copy of the file and edit only the copy. 2. Turn on the filter for the stock column and open its list of distinct values. Anything that is not a plain whole number, such as 10+, 5-10, in stock or yes, marks a row to fix. 3. Also search the stock column for "+", "-" and letters, because a long value list is easy to scroll past. A minus sign can also mean negative stock; for that, see "Zero or negative CSV price and stock: find and fix". 4. Look for dates in the stock column. Some spreadsheet programs may turn a range like 5-10 into a date when the file is opened (not verified in every program). If you see a date there, take the original text from the source file or a plain text editor, not from the spreadsheet view. 5. Note each match: SKU, row and old value.

Fix each row. 1. Get the real count from your stock records or a shelf count, and type it as a plain whole number. 2. If a range is all you have and you cannot count before the import, the lower end is the most you can promise: 10 for "10+", 5 for "5-10". The upper end can promise stock you do not have. 3. An availability word is not a count, so look up or count the number in the same way. Some shops have a separate availability or stock status field; if yours does, the word may belong there and the count in the stock column. This depends on the shop and was not verified here, so check its import guide. 4. Do not type 0 as a placeholder for an unknown count; enter 0 only for an item that is truly sold out. If you cannot get a count in time, leave the row out of this upload, if your program allows it, and add it once it is counted. 5. Log each change (row, old value, new value) so every change can be reversed; the post "CSV correction worksheet: log each fix, recheck" explains the method.

In the synthetic example below (made up: not real products, shops or counts), SYN-001 to SYN-004 hold a range or a word, SYN-005 already holds a whole number, and SYN-006 ("few") has no count yet, so it stays out of this upload until it is counted.

Recheck. 1. Filter the stock column again; its value list should now show whole numbers only. 2. Compare the row count with the original, minus any rows you left out on purpose. 3. Save as CSV, then open the saved file in a plain text editor and confirm the stock cells hold numbers, not dates or words.

The free CSV check on heloft.com does not read stock or quantity columns. It reads only SKU, title, price, brand and barcode columns, plus date columns. In a local test with made-up rows, a stock column holding 10+, 5-10, in stock, yes and var got the same answer as a clean file: "No problems found". Headers such as quantity, qty, inventory or Stok were not read either. A clean result therefore says nothing about your stock column; check it by hand as described here. The free check is still useful for the columns it does read, in the first 20 data rows: for example, it reports a price that is not a plain number or a SKU that appears more than once.