CSV Data Cleaning & Python Automation
I developed a reusable Python/Pandas workflow to transform a messy sales CSV into a clean, analysis-ready dataset.
The workflow automatically:
Standardizes inconsistent whitespace in text fields
Validates and standardizes dates
Validates numeric fields such as quantity and unit price
Detects duplicate records
Identifies missing or invalid values
Calculates transaction revenue
Generates an exceptions report
Compares before/after record counts
Produces a reusable Python script and cleaned CSV output
Results
20 records processed with 0 missing values, 0 duplicate records removed, and 0 exceptions identified.
The cleaned dataset contained 8 columns, including a calculated Revenue field. Total revenue was 10,157, with an average transaction revenue of 507.85.
Tools
Python | Pandas | Jupyter Notebook | CSV
Deliverables
Cleaned CSV, exceptions report, reusable Python script, and data-quality summary.
0 likes · 1 view · Sep 14, 2026
CSV Data Cleaning & Python Automation I developed a reusable Python/Pandas workflow to transform a messy sales CSV into a clean, analysis-ready dataset. The ...
