You will get a cleaned, deduplicated Excel/CSV file + a data-cleaning validation report
Project details
Messy CSV or Excel-style data can look usable while still hiding quiet problems: duplicate rows, conflicting duplicate values, missing required fields, broken email values, inconsistent column names, unsafe formula-like cells, or rows that disappear during cleanup.
I clean small business datasets into a reviewable handoff: a clean CSV, an exceptions file, and a short quality report that shows what changed and what still needs review before import.
The main difference is that rows are not silently dropped. Every source row is accounted for either in the clean output through source row IDs or in the exceptions file with an issue and action.
This is for bounded CSV/Excel-style cleanup, deduplication, validation, and handoff work. It is not lead scraping, CRM automation, compliance review, or a production data platform.
I clean small business datasets into a reviewable handoff: a clean CSV, an exceptions file, and a short quality report that shows what changed and what still needs review before import.
The main difference is that rows are not silently dropped. Every source row is accounted for either in the clean output through source row IDs or in the exceptions file with an issue and action.
This is for bounded CSV/Excel-style cleanup, deduplication, validation, and handoff work. It is not lead scraping, CRM automation, compliance review, or a production data platform.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$30
|
Standard
$175
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Frequently asked questions
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JM
Jim M.
Jun 7, 2026
Technical Scoping for Wagyu Sales Tracking
Michael delivered the agreed scoping assessment on time and was responsive throughout the engagement. The report identified several important technical considerations and helped focus the next stage of investigation. Professional communication and a straightforward process.
About Michael
CSV/Excel Cleanup & Validation | Python, Exceptions & PDF Handoffs
Hamburg, Germany - 2:56 pm local time
Two main strands:
- Messy CSV and Excel files become clean, deduplicated files for review or import, with mismatch flags, missing-value notes, an exceptions file and a short quality summary.
- PDF and parser output becomes reviewable JSON, CSV, Markdown and chunk records with source context before downstream review, import, automation or retrieval preparation.
For CSV and Excel work, the goal is not just a cleaner file. The goal is a handoff where you can see what changed, which rows were merged or flagged and which records still need review before import into a shop, CRM, spreadsheet or internal workflow.
For PDF and parser work, current public examples start from Docling JSON. The same review pattern can be scoped for Docling, PyMuPDF or similar parser output when sample output is available. Where the parser output provides it, I preserve source context such as source file, page number, section heading, bounding box, provenance, block IDs and stable chunk IDs.
Typical deliverables:
- Clean, deduplicated CSV or Excel files with exceptions and quality notes
- Mismatch, missing-value and duplicate-review flags
- Normalized JSON or CSV outputs from PDF or parser output
- Clean Markdown export with source-reference comments
- JSONL chunk records with section and page context
- Handoff bundles and validation flags for human review before downstream use
Typical first step:
Send a small sample: one messy CSV or Excel file, 3 to 5 representative PDF pages or an existing parser-output export. I return a small reviewable sample so you can inspect the output shape before committing to a larger scope.
Recent client feedback:
Reliable and professional delivery; on-time scoping work that identified important technical considerations and helped focus the next investigation stage.
Not a good fit:
- Full SaaS or platform builds
- Production OCR accuracy guarantees
- Web scraping or lead-generation services
- Accounting, tax, legal, medical or compliance ownership
- Full RAG chatbot or answer-quality ownership
- Cloud, infrastructure or deployment ownership
Steps for completing your project
After purchasing the project, send requirements so Michael can start the project.
Delivery time starts when Michael receives requirements from you.
Michael works on your project following the steps below.
Revisions may occur after the delivery date.
Review input and output rules
I check the file structure, required columns, dedupe key and any rows that need special handling before cleaning.
Clean and validate the data
I map columns, clean values, merge duplicates, flag conflicts and create the clean CSV plus exceptions file.