You will get clean, accurate Excel or CSV data with a clear QA summary


Project details
I will turn messy Excel or CSV data into a clean, consistent, client-ready dataset. Your file can be checked for duplicate rows, extra spaces, inconsistent capitalization, mixed date formats, inconsistent phone or country formats, misplaced columns, and missing values. I preserve the original meaning of your data and never invent missing information. You will receive a cleaned Excel or CSV file, consistent columns and formatting, duplicate detection based on agreed rules, and a concise QA summary describing what changed and what still needs review. For larger or recurring datasets, the Advanced package can include a reusable Python cleaning script with simple instructions.
Data Entry Type
Copy Paste, Data Cleansing, Error DetectionData Entry Tool
Google Sheets, Microsoft ExcelWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$75
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 2 |
Number of Hours of Work | 2 | 4 | 8 |
Formatting & Clean Up | - | - | - |
Graph & Table Creation | - | - | - |
Frequently asked questions
About Debra
Python Data Cleaning & ETL Specialist | Excel, CSV, Web Scraping
Kansas City, United States - 4:51 pm local time
My background includes building research data pipelines for social media collection, entity extraction, geocoding, quality checks, and versioned dataset generation. I can also automate Linux and Bash workflows and analyze results.
You will receive clear deliverables, documented steps, and responsive communication. Send me a sample file and the desired output format, and I will suggest a practical approach.
Steps for completing your project
After purchasing the project, send requirements so Debra can start the project.
Delivery time starts when Debra receives requirements from you.
Debra works on your project following the steps below.
Revisions may occur after the delivery date.
Review the file and confirm rules
I review the file structure, data types, duplicate logic, missing values, and expected output format.
Clean and standardize the data
I clean the data according to the agreed rules and run duplicate, blank-field, and format checks.
