Excel Error Values Explained: #N/A, #DIV/0!, #VALUE! and More

An error in a cell often stops people for longer than the fix takes. The good news is that the error name already says what went wrong. Once you know the seven common ones, most of them take seconds to sort out.

TL;DR: Read the error name. Each one points to a specific cause, and most fixes are one edit.

The seven you will see most

1. #DIV/0! means something was divided by zero. Example: =1200/B2 where B2 is 0. Wrapping it as =IFERROR(1200/B2,0) shows 0 instead of the error.
2. #N/A means a lookup didn't find the value. Example: a name that isn't in the list. Check the spelling, or wrap the lookup: =IFNA(VLOOKUP(...),"Not found").
3. #VALUE! means the formula tried to do math on text. Example: =B4+1 where B4 contains "100 won" as text. Keep only the number in number cells.
4. #NAME? means Excel doesn't recognize a function name, usually a typo. =SUMM(1,2) gives the error; =SUM(1,2) returns 3.
5. #REF! means a cell the formula pointed to has been deleted. =F6*2 becomes =#REF!*2 after column F is removed. Undo the delete or rewrite the reference.
6. ##### is not an error. The column is too narrow to show the value, often a date. Drag the column header border to widen it.
7. #SPILL! means a formula that returns several values has no room to spill into. Clear the cells in the way and the results (1, 2, 3 in our test) fill in.

All seven were produced and fixed on purpose in Excel for the web.

A note on localized Excel

Error names are translated in some language versions. In Korean Excel, #SPILL! appears as #분산!. If you're following an English tutorial on a localized install, or helping someone who is, expect the error text to differ even though the cause is the same.

Be careful with IFERROR

IFERROR is handy for display, but it hides every kind of error, not just the one you expected. A #REF! from a deleted column will quietly turn into 0 and look like real data. Use IFNA when you only mean to catch missing lookups, and fix the cause where you can.

Reading errors from a script

If you read workbooks with openpyxl and load them with data_only=True, you get the values Excel last calculated, and error cells come back as strings such as "#DIV/0!". A quick scan for strings starting with # is an easy way to flag broken cells in a batch of files before anyone opens them.

Drafted with AI assistance. Each error was reproduced and fixed in Excel for the web.