NorthForgeLabs

Available to hire

Excel & CSV data cleaning, Python automation and data validation for clear, reviewable files. NorthForgeLabs helps turn messy spreadsheets and product lists into structured data that is ready to review and use.

About

Excel & CSV data cleaning, Python automation and data validation for clear, reviewable files.

NorthForgeLabs helps turn messy spreadsheets and product lists into structured data that is ready to review and use.

Services include:

  • Cleaning and standardising text, dates and number formats
  • Combining Excel and CSV files and removing confirmed duplicates
  • Flagging missing values, conflicting IDs and records requiring review
  • Preparing consistent e-commerce product data
  • Automating repeatable cleaning, validation and file-processing steps with Python
  • Converting PDF tables to Excel or CSV after a source-file review

I agree the rules before starting, preserve the original files and make changes traceable. Where appropriate, the handover includes separate Master, Issues and Changes tabs. Ambiguous values are flagged rather than guessed.

My portfolio features an independent Excel-cleaning demonstration using fictional data; it is not paid client work. I also study Media Design / Game Art at MBO Level 4, including Blender and Unreal Engine, and offer 3D/game assets as a secondary specialisation.

Fixed project pricing can be agreed after reviewing the files and requirements. Send a sample, approximate row and file count, desired output format and deadline to discuss the scope.

  • Joined October 2026

Languages

  • DutchFluent
  • EnglishIntermediate

Industry experience

  • Software & Internet

More experts in Leeuwarden, Netherlands

Projects · 2

Traceable Excel Data Cleaning & Validation — Independent Demo

A Python-based workflow combines and validates two Excel files, producing a reviewable workbook with a traceable change log. Independent demonstration using fictional data; no client commissioned or paid for this project. Created by NorthForgeLabs in October 2026. Goal Combine two flat XLSX files into a clean, reviewable workbook while preserving the source files and a traceable record of changes. The source files contain 12 non-empty records across five data columns. Work demonstrated Standardising agreed text, date and number formats Consolidating the two source files Removing one confirmed exact duplicate and retaining 11 records Preserving six issues for review, including conflicting IDs, an invalid date, an ambiguous number and a missing ID Providing separate Master, Issues and Changes tabs Checks and handover The change log records 23 entries, including the separate exclusion of one fully empty physical row. Source references and file hashes support reconciliation. Original files remain unchanged. The workbook was checked with automated reconciliation and cell/type validation, then opened and reviewed in Microsoft Excel. Ambiguous values were flagged rather than guessed. This example does not claim a customer endorsement or commercial result. Discuss a similar project Send a sample file, approximate row/file count, required output format and deadline. Fixed project pricing can be agreed after reviewing the source files and validation rules.

  • Data Analyst
  • Developer
Python CSV Automation for Warehouse Dispatch

Independent demonstration with fictional warehouse exports. Two warehouses export daily shipment CSVs. Before import, an operations team needs to catch duplicate shipments, resolve disagreements between files and keep a record of what was excluded. This Python CLI validates file structure and shipment fields, normalises records, and produces an import CSV, an exception CSV and a JSON reconciliation report. It preserves source files, keeps one copy of identical shipments and sends all conflicting versions of a shipment to review. Verified sample run: 3 exports checked, including 1 rejected legacy layout. Of 13 parsed records, 8 were accepted and 5 were excluded. Accepted shipments total 21 parcels. Automated tests verify reconciliation, rejection of a malformed file and protection against overwriting existing output. For a similar project, share a redacted export, the destination schema and your rules for duplicates and exceptions. I can scope a small pilot around one export format and a reviewable handover.

  • Data Analyst
  • Developer

Work experience

Independent Excel Data Cleaning Project (Demo) · NorthForgeLabs — self-directed projectOct 2026 – Oct 2026 · 1 mo

Self-directed portfolio demonstration using fictional data, completed on 22 October 2026. This entry describes an independent project, not employment or a paid client engagement. Demonstrated a Python-based Excel-cleaning and validation workflow: consolidated two flat XLSX files, standardised agreed text/date/number formats, removed one confirmed exact duplicate, and retained uncertain values for review. Produced Master, Issues and Changes tabs with source references and a change log. Verified example: 12 source records, 11 retained records, six review issues and 23 recorded changes. The local evidence dated 22 October 2026 records automated reconciliation, cell/type validation and a Microsoft Excel review. The published portfolio shows this same demonstration. No client results or commercial outcomes are claimed.

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