Clean Data
Clean messy data in the active sheet or a specified range.
Preflight: Dependency Check
Before starting, verify required libraries are installed and install any that are missing.
python3 -c "import openpyxl" 2>/dev/null || python3 -m pip install openpyxl
Important: Do not skip this step — the workflow below will fail without these libraries.
Environment
- If running inside Excel (Office Add-in / Office JS): Use Office JS directly. 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 and openpyxl.
Workflow
Step 1: Scope
- If a range is given, such as
A1:F200, use it.
- Otherwise use the full used range of the active sheet.
- Profile each column: detect its dominant type, text vs number vs date, and identify outliers.
Step 2: Detect issues
| Issue |
What to look for |
| Whitespace |
Leading/trailing spaces, double spaces |
| Casing |
Inconsistent casing in categorical columns like usa, USA, Usa |
| Number-as-text |
Numeric values stored as text; stray $, ,, % in number cells |
| Dates |
Mixed formats in the same column like 3/8/26, 2026-03-08, March 8 2026 |
| Duplicates |
Exact-duplicate rows and near-duplicates caused by case or whitespace differences |
| Blanks |
Empty cells in otherwise-populated columns |
| Mixed types |
A column that is mostly numbers but has a few text entries |
| Encoding |
Mojibake, non-printing characters |
| Errors |
#REF!, #N/A, #VALUE!, #DIV/0! |
Step 3: Propose fixes
Show a summary table before changing anything:
| Column |
Issue |
Count |
Proposed Fix |
Step 4: Apply
- Prefer formulas over hardcoded cleaned values. Where the cleaned output can be expressed as a formula, such as
=TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), or =DATEVALUE(D2), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original.
- Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists, such as encoding or mojibake repair.
- For destructive operations like removing duplicates, filling blanks, or overwriting originals, confirm with the user first.
- After each category of fix, whitespace, casing, number conversion, dates, dedup, show a sample of what changed and get confirmation before moving to the next category.
- Report a before/after summary of what changed.
1---2name: clean-data-xls3description: Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", and "this data is messy".4---56# Clean Data78Clean messy data in the active sheet or a specified range.910## Preflight: Dependency Check1112Before starting, verify required libraries are installed and install any that are missing.1314```bash15python3 -c "import openpyxl" 2>/dev/null || python3 -m pip install openpyxl16```1718**Important**: Do not skip this step — the workflow below will fail without these libraries.1920## Environment2122- **If running inside Excel (Office Add-in / Office JS):** Use Office JS directly. Read via `range.values`, write helper-column formulas via `range.formulas = [["=TRIM(A2)"]]`. The in-place vs helper-column decision still applies.23- **If operating on a standalone `.xlsx` file:** Use Python and `openpyxl`.2425## Workflow2627### Step 1: Scope2829- If a range is given, such as `A1:F200`, use it.30- Otherwise use the full used range of the active sheet.31- Profile each column: detect its dominant type, text vs number vs date, and identify outliers.3233### Step 2: Detect issues3435| Issue | What to look for |36|---|---|37| Whitespace | Leading/trailing spaces, double spaces |38| Casing | Inconsistent casing in categorical columns like `usa`, `USA`, `Usa` |39| Number-as-text | Numeric values stored as text; stray `$`, `,`, `%` in number cells |40| Dates | Mixed formats in the same column like `3/8/26`, `2026-03-08`, `March 8 2026` |41| Duplicates | Exact-duplicate rows and near-duplicates caused by case or whitespace differences |42| Blanks | Empty cells in otherwise-populated columns |43| Mixed types | A column that is mostly numbers but has a few text entries |44| Encoding | Mojibake, non-printing characters |45| Errors | `#REF!`, `#N/A`, `#VALUE!`, `#DIV/0!` |4647### Step 3: Propose fixes4849Show a summary table before changing anything:5051| Column | Issue | Count | Proposed Fix |52|---|---|---|---|5354### Step 4: Apply5556- Prefer formulas over hardcoded cleaned values. Where the cleaned output can be expressed as a formula, such as `=TRIM(A2)`, `=VALUE(SUBSTITUTE(B2,"$",""))`, `=UPPER(C2)`, or `=DATEVALUE(D2)`, write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original.57- Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists, such as encoding or mojibake repair.58- For destructive operations like removing duplicates, filling blanks, or overwriting originals, confirm with the user first.59- After each category of fix, whitespace, casing, number conversion, dates, dedup, show a sample of what changed and get confirmation before moving to the next category.60- Report a before/after summary of what changed.
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