Abid Ali Awan Shows How to Clean 10-Row CSVs With Python and Pandas
Updated
Updated · KDnuggets · Jul 8
Abid Ali Awan Shows How to Clean 10-Row CSVs With Python and Pandas
1 articles · Updated · KDnuggets · Jul 8
Summary
Awan walks readers through cleaning a messy 10-row, 8-column customer CSV in pandas, from initial inspection to exporting a validated clean_customers.csv file.
The guide tackles common data-quality problems step by step: messy column names, blank placeholders, 1 duplicate row, inconsistent text, mixed date formats, invalid emails, and currency values stored as text.
Pandas operations convert age and spending into numeric fields, standardize membership values to bronze, silver, gold or unassigned, and parse join_date with mixed-format datetime handling.
Missing-data rules then drop rows without customer_id, fill age with the median 31, set missing spend to 0.0, and leave unresolved email and date gaps as nulls when forced values would be unreliable.
Final assertions check unique customer IDs, ages between 0 and 120, nonnegative spending, and approved membership categories, framing a reusable workflow of inspect, clean, validate, and save.