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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.
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A sample Python project showing my data cleaning workflow — removing duplicates, handling missing values, standardizing text fields, and summarizing sales by city using Pandas. This reflects the kind of cleanup and analysis I do for client datasets.
BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
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