Nuvida
All selected workReal data workflow

Nuvida / Digital Products Studio / Founder-led independent studio

Transforming 2,016 product rows into a Shopify-ready import system.

A repeatable data workflow that replaces fragile manual catalog preparation with validated outputs.

Capability
Data & Automation
Delivery
Data mapping, Validation, Automation
Stack
Python, pandas, openpyxl, Shopify

2,016

source product rows

15 → 79

source to target columns

CSV + XLSX

review-ready outputs

Context

What the project needed to make clear.

A real product catalog had to be converted from a compact supplier export into Shopify's much wider import structure without turning the work into a fragile manual spreadsheet exercise.

Challenge

The problem behind the visible interface.

The source and target files used different schemas, encodings and value formats. Product handles needed to remain unique, price strings needed extraction and required Shopify defaults had to be applied consistently across 2,016 rows.

Response

A focused system, not a pile of features.

A repeatable Python transformation reads source and template files with encoding fallbacks, validates required columns, maps product fields, creates Shopify-safe unique handles and extracts normalized prices.

Verified evidence

The work, shown without inflated claims.

Source export
  • name
  • brand
  • image src
  • vintage
  • sale-price
15 source columns
Shopify import
  • Handle
  • Title
  • Vendor
  • Variant Image
  • Variant Price
79 target columns
Required columns validated
Unique handles generated
CSV and formatted XLSX written

Result

A clearer, repeatable outcome.

The workflow produces both an import-ready CSV and a reviewer-friendly XLSX with frozen headers, filters, deliberate column widths and formatted price values.

Delivery highlights

  • Required source and target columns are validated before transformation.
  • Duplicate-safe handles and price extraction replace manual cleanup steps.
  • Locked output files fall back to timestamped copies instead of losing work.

Evidence note

Real data workflow. Customer and product values remain private; only verified structure and process metrics are shown.

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