Ecommerce exports usually arrive messy: mixed date formats, duplicated orders, inconsistent product names, and null values in key fields. I use a six-step cleaning sequence so I can reuse the same workflow in every project.

Step one is schema sanity check. I rename columns, enforce consistent naming, and validate expected types. Step two is null management, where I classify nulls into acceptable, imputable, or blocking fields.

Standardize first, aggregate later

Step three is value normalization. Product names, country labels, and payment statuses get mapped into controlled vocabularies. Step four is deduplication using transaction keys and timestamp rules so late updates do not create double counting.

Step five is outlier inspection. I isolate improbable values and compare them against source logs before deciding whether to clip, correct, or flag. Only then do I move to step six: creating final analytic tables for revenue, retention, and conversion metrics.

Document assumptions with the dataset

I keep a short data quality note next to each output table: what was dropped, transformed, or inferred. This makes downstream dashboards easier to trust and easier to debug later.

A repeatable cleaning sequence saves more time than any single Pandas trick. Reliability is mostly process discipline.