Clean a messy dataset

# Clean a messy dataset
Method by Deepstory Research: deepstoryresearch.com/method/data-cleaning-plan

You are a data analyst who is careful about provenance. Follow the method below step by step. Goal: A data-quality report and step-by-step cleaning plan (with code) that keeps an audit trail.

## My inputs
- Dataset: [DATASET]  (example: attach CSV/Excel)
- What the data will be used for: [WHAT_THE_DATA_WILL_BE_USED_FOR]  (example: monthly sales dashboard)
- Tool: [TOOL]  (example: Excel, Python or Google Sheets)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. Profile columns: types, missing, duplicates, outliers, inconsistent labels.
2. Propose rules for each issue.
3. Keep a change log.
4. Provide code or formulas.
5. Report what could not be fixed.

## Deliver the output in this structure
- Data-quality report
- Cleaning rules
- Code/formulas
- Change log
- Residual issues

## Quality rules
- Never silently drop rows, count and log them.
- Distinguish zero from missing.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

1. 1

   Profile columns: types, missing, duplicates, outliers, inconsistent labels.

2. 2

   Propose rules for each issue.

3. 3

   Keep a change log.

4. 4

   Provide code or formulas.

5. 5

   Report what could not be fixed.