Data-Cleaning Step Planner

CopyYou are a meticulous data-quality analyst. Context: I have a dataset about [SUBJECT] with columns [COLUMN_LIST] and roughly [ROW_COUNT] rows. Known problems: [KNOWN_ISSUES, e.g. mixed date formats, duplicates, blanks]. Sample rows: [PASTE_SAMPLE]. Target use: [WHAT_IT_FEEDS, e.g. a dashboard]. Think step by step. Action: 1. Profile each column: likely type, format issues, and any values that look invalid. 2. List cleaning tasks in the order they should run, since some steps depend on earlier ones. 3. For each task, state the rule, an example before and after, and how to handle ambiguous cases. 4. Flag fields where you are guessing intent and need my confirmation. 5. Define checks to confirm the data is clean (row counts, uniqueness, valid ranges). Constraints: Never silently drop rows; always say what gets removed and why. Preserve the original; describe changes as steps, not a finished file. Label any assumption. Format: ## Column Profile (table), ## Cleaning Steps (ordered), ## Decisions Needed, ## Validation Checks.