The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
CSV cleanup & reconciliation — a self-initiated Rivetfern demonstration using synthetic data.
Python validates and normalizes invoice/payment exports, quarantines conflicting records, preserves an audit trail and reconciles totals separately by currency. The reproducible fixture has 90 invoice rows and 92 payment rows; outputs retain 80 valid invoices and 84 valid payments, with exceptions kept visible.
Handover: clean CSVs, reconciliation report, rejected-row reasons, source hashes, runnable code and tests. This is a portfolio demo, not paid client work or a claim of client results.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
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.
🚨 CODES, SHADERS & SPOOKY UI: WHY BUILDERS CAN’T MATCH HAND-CODED PRECISION
Most creators blend static assets in Figma or Canva. As a Full-Stack Engineer, I treat code itself as an art form.
For the Rive Halloween Challenge, I brought together raw code, terminal-driven workflows, and GPU shaders into one ultimate dev collage:
• Rive CLI & RML: Writing responsive interactive scenes directly inside VS Code—zero bloat.
• GPU Canvas (WGSL Shaders): Custom fog, flickering lights, and real-time spooky effects powered directly by the GPU.
• Cybersecurity & Clean Architecture: High-performance, pixel-perfect web apps built to convert in 0.05 seconds.
Forget laggy plugins and heavy template builders. True digital experiences are engineered from the ground up.
💬 Quick Question for Creatives & Devs:
Are you team Drag-and-Drop Builders or team 100% Hand-Coded Architecture?
👇 Drop your thoughts below, let's connect and push the boundaries of modern web tech!
The part where you drive fog and flickering lights with WGSL shaders on the GPU feels like the kind of low level control that really cuts latency.
