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USD
I will clean and organize your messy CSV or excel data in python
Python developer for scraping, APIs, data cleaning and automation
Python developer, 11+ years on production systems: backend architecture, data pipelines, API integrations, automation. I also build LLM assistants (LangChain / LangGraph) and n8n automation flows.
Wh...
About this Gig
Send me the messy export and I send back one tidy table plus the summary you actually need.
The kind of mess I fix: mixed date formats, "$1,299.00" vs "1.299,00", stray spaces, ACME CORP vs Acme Corp, blank rows, duplicate rows, negative or zero quantities, numbers stored as text.
What you get: the cleaned dataset (CSV, Excel or Google Sheet), the summary tables you pivot on (by month, by product, top customers) or a short report, and the Python script so you can re-run it next month on a new file. Everything ships with tests, and dropped rows are reported with the reason, never silently.
How it works: send a sample (even 50 rows, columns anonymised is fine) and I tell you exactly what I would normalise and what the output looks like before you order. Turn a file you dread into one you can pivot.
FAQ
What file formats do you accept, and what do you deliver?
Send CSV, Excel (.xlsx/.xls) or a Google Sheet link. I deliver the cleaned file in the format you choose, the summary tables, and the Python script that produced them. Tell me if the raw file has multiple sheets or a header that isn't on row 1.
How do you handle rows you can't clean?
They're dropped and counted, with the reason (blank key field, unparseable date, quantity <= 0, exact duplicate). You get a short note of how many rows went in, how many came out, and why - nothing disappears silently.
