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CSV Cleanup with Unresolved Records — Fictional Example
A self-created six-row CSV demonstration of agreed cleanup rules, original record preservation, and an explicit QA status. The article includes before-and-after data and a Python example: all six rows remain, while ambiguous duplicate emails and missing values are left for review. This is fictional data, not a client delivery or a customer success story. It does not certify CRM compatibility or guarantee import success. The image shows the published technical article.
English portfolio text prepared with AI assistance.
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Exact duplicates and conflicting IDs need different treatment.
CSV Exact Audit is now available: a local Python toolkit for analysts and freelancers who need a repeatable check on CSV exports.
The synthetic demo has 12 input records: 5 in cleaned.csv, 5 flagged for review, and 2 exact duplicates logged separately. Leading zeros are preserved as cell strings. Conflicting or blank IDs are flagged for review; values are not guessed or silently merged.
Included: reusable Python source, run instructions, tests, synthetic examples, three result CSVs, a JSON audit, and an original-byte backup. Processing stays on your machine; no API key or extra packages are needed.
$19 one-time. Requires Python 3.10+ and a terminal. Verified on macOS; Windows and Linux are not natively tested for this release. Scope is one UTF-8 CSV at a time, exact duplicates and optional ID review.
AI assisted code development and testing. The example is synthetic, not customer data.
See the scope and download details:
https://contra.com/products/hXKUWINO-csv-exact-audit-local-duplicate-and-id-review-toolkit
I’m available for a small remote CSV cleanup task.
This original Python demo turns 12 synthetic records into 7 clean rows, 2 exact duplicates and 3 records for review. The source file stays unchanged, and every change is logged.
For a first task: one CSV, up to 1,000 rows, 20 columns and three agreed cleanup rules. Deliverables include cleaned data, separate review and duplicate files, a reusable script and a short run guide.
AI-assisted work. We agree scope, price, deadline and one in-scope revision before funded work starts. Message me with an anonymized sample and the output you need.
Messy CSV exports should not slow down analysis or system imports.I built the CSV Cleanup & Validation Toolkit to standardize headers, trim inconsistent spacing, remove exact duplicates, check required fields, flag duplicate keys, and generate a clear issue report. It runs with Python 3.9+ and requires no third-party packages.Explore the toolkit: https://contra.com/products/hD2ajF0B-csv-cleanup-and-validation-toolkit#Python #DataCleaning #Automation #CSV
