Python Data Cleaning & Quality Report

Role: Data Analyst / Python Analyst
Dataset: Synthetic / anonymized demo data created for portfolio use
Stack: Python, pandas, matplotlib, CSV/Excel processing

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Business problem

Raw customer order data contained duplicate rows, inconsistent date formats, text-based numeric values, mixed city names, missing emails, and inconsistent order statuses.

The goal of this project was to build a reproducible data-cleaning pipeline that converts messy operational data into a clean dataset, a quality summary report, and a simple revenue visualization.

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What was built

Built a Python/pandas cleaning pipeline that:

• standardizes emails, phone numbers, city names, dates, order statuses and revenue fields;
• removes duplicate rows;
• validates and normalizes core fields;
• exports a clean customer-order dataset;
• generates a reusable data-quality summary;
• creates a revenue visualization after cleaning.

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Key results

Metric	Result
Raw rows	945
Clean rows	900
Duplicates removed	45
Missing emails after cleaning	28
Paid orders	395
Total paid revenue	$46,912.19
Clean export generated	Yes
Quality summary generated	Yes
Revenue visualization generated	Yes

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Key outputs

• results/clean_customer_orders.csv — cleaned dataset
• results/cleaning_summary.csv — data quality metrics
• results/revenue_after_cleaning.png — revenue visualization after cleaning
• results/run_log.txt — pipeline execution summary

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Revenue after cleaning

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Freelance use cases

I can adapt this project for:

• cleaning client CSV/Excel files;
• building automated data-quality reports;
• creating Google Sheets / Excel dashboards;
• building trading journal dashboards;
• analyzing PnL, win rate, drawdown and strategy performance.

Project structure

python-data-cleaning-report/
├── README.md
├── requirements.txt
├── data/
│   └── raw_customer_orders.csv
├── src/
│   └── main.py
└── results/
    ├── clean_customer_orders.csv
    ├── cleaning_summary.csv
    ├── revenue_after_cleaning.png
    └── run_log.txt