Why this chapter matters
Aggregation and modeling are only as good as input quality, so cleaning rules must be explicit and auditable.
What you will learn
- Profile datasets with shape, dtypes, and missing-value checks.
- Convert dates and numeric fields to correct types.
- Apply duplicate and null-handling rules based on business meaning.
Lessons in this chapter
- Inspect before editingMeasure quality issues before transforming data.
- Type conversionStandardize date and numeric columns for correct operations.
- Nulls and duplicatesResolve missing and repeated records with explicit policy.
- Guide: pandas data cleaningApply a repeatable cleanup sequence to real tables. Read the full guide →
Study task
Clean an orders DataFrame by normalizing channel labels, converting amount and date fields, and deduplicating on order_id.
Chapter checkpoint
Why should numeric columns be converted before aggregation?
If numeric fields stay as strings, sums and averages can be wrong or fail unexpectedly.