I will clean validate and process CSV data with python
Python Automation, API and Excel Developer
About this Gig
I clean, validate, deduplicate, format, and process CSV or spreadsheet data so it is ready for reporting, upload, analysis, or business use.
This gig is for messy files with duplicate rows, inconsistent columns, missing values, bad formatting, incorrect dates, broken names, invalid emails, repeated records, or data that needs to be cleaned before it can be trusted.
I can help with:
CSV data cleaning
Excel data cleaning
Duplicate removal
Data correction
Date, number, name, and column formatting
Missing value checks
Validation and error logs
Clean output files
Python-based repeatable processing
Preparing data for reports, dashboards, imports, or analysis
The goal is simple: turn messy raw data into a clean, structured, usable file.
Every delivery is built for clarity. You receive the cleaned file, error notes where needed, and a simple explanation of what was corrected.
Choose the package that matches your file size, place the order, upload your data, and I will clean and prepare it from there.
My Portfolio
FAQ
What file types can you clean?
I can clean CSV and Excel files. If your data comes from another source, export it as CSV or Excel before uploading it with the order.
What kind of data issues can you fix?
I can help with duplicate rows, inconsistent formatting, missing values, incorrect dates, broken names, invalid emails, repeated records, messy columns, and data that needs to be standardized for reporting or upload.
Will you change the meaning of my data?
No. I clean, validate, format, and structure the data. I do not make unsupported assumptions. If a value cannot be corrected safely, I will flag it instead of guessing.
Can you handle large files?
Yes. Choose the package that matches your file size, or add extra items if your dataset is larger than the package limit.
What will I receive after the order?
You will receive the cleaned data file, error notes where needed, and a simple explanation of what was cleaned, corrected, removed, or standardized.

