Cleaning should preserve raw inputs and document each transformation. Missing values, duplicate records and invalid categories need separate policies rather than one blanket replacement.
Before you begin
Know Python functions and basic collections. Work from a copy of a small fictional dataset and record the column definitions.
Work through the idea
Keep the raw input unchanged and record the transformation steps. Inspect row counts, types and a few known records before and after the operation. Decide what missing or invalid values mean for this task rather than applying one universal cleaning rule. Save a short explanation with the resulting table.
- Profile missing and duplicate values
- Validate data types
- Record counts before and after cleaning
Practice task
Clean a fictional order table containing a repeated ID, invalid date and missing amount.
Check your result
Produce a quality report showing what was corrected, excluded and left unresolved.
A mistake to avoid
Do not fill every missing numeric value with zero without considering its meaning.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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