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I will do data cleaning deduplication and normalization with python
Full Stack Developer and Automation Specialist with 88 Five Star Reviews
Python developer specializing in web scraping, automation, and API integration. 83 five-star reviews, all from real Python projects.
I build scrapers that extract product data, prices, contacts, and ...
Level 2
Has met high performance criteria and has a proven track record for meeting client expectations.
About this Gig
Messy dataset slowing you down? I do professional data cleaning on CSV, Excel, JSON, and SQL dumps. Duplicates removed, fields normalized, dates parsed, missing values handled.
What I clean:
+ CSV, Excel, JSON, SQL dumps, TSV, Parquet
+ Scraped product catalogs and price lists
+ Customer and mailing lists
+ Survey exports and form submissions
+ Messy transaction or inventory files
Operations I perform:
+ Deduplication with custom key rules
+ String normalization (case, whitespace, encoding)
+ Date and phone and address parsing
+ Missing value handling (impute, drop, flag)
+ Outlier detection and field mapping
Tools I use:
+ pandas, numpy, SQL, regex, Python
What you receive:
+ Clean structured dataset in your chosen format
+ Python cleaning script you can re-run
+ Documentation of every rule applied
89 five-star reviews across my profile and 30+ scraping projects that produced messy data I then cleaned end to end. I know exactly how broken real files get.
Important: Message me BEFORE ordering with a sample file and your cleaning rules. I will confirm scope and timeline.
My Portfolio
FAQ
What file formats do you support?
CSV, Excel (xlsx and xls), JSON, TSV, SQL dumps, Parquet, and plain text. If your format is unusual, send a sample and I will confirm before you order.
How many rows can you clean?
Basic covers up to 5,000 rows, Standard up to 50,000, Premium is unlimited. For very large datasets I build a pipeline and process in chunks.
How do you decide what counts as a duplicate?
You tell me the key fields. If you are unsure I propose a ruleset based on the data and you approve before I run it.
How do you handle missing values?
Three options: impute from other rows or external sources, drop the row, or flag for review. I confirm per column before running.
Do I get the Python script or just the clean data?
Both from Standard onward. The script is reusable on future files and comes with a documented rule list.
Is my data confidential?
Yes. I work on a private workspace, delete your files after delivery on request, and sign an NDA if needed.
Can you handle dates and addresses in mixed formats?
Yes. Dates get parsed into ISO 8601, phones into E.164, addresses standardized. Edge cases flagged for your review.
