Clean Data

Clean messy data in the active sheet or a specified range.

Environment

• If running inside Excel (Office Add-in / Office JS): Use Office JS directly (Excel.run(async (context) => {...})). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies.
• If operating on a standalone .xlsx file: Use Python/openpyxl.

Workflow

Step 1: Scope

• If a range is given (e.g. A1:F200), use it
• Otherwise use the full used range of the active sheet
• Profile each column: detect its dominant type (text / number / date) and identify outliers

Step 2: Detect issues

Step 3: Propose fixes

Show a summary table before changing anything:

Step 4: Apply

• Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.
• Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
• For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
• After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
• Report a before/after summary of what changed