Case Study
CSV Cleaner Toolkit
A lightweight toolkit for cleaning, validating, and standardising CSV files — designed for analytics pipelines, reporting workflows, and automation systems where clean input data is critical.
Project type
Utility toolkit
Input
Raw CSV files
Output
Clean, validated CSVs
Use cases
Analytics • Reporting • Imports
The problem
CSV files are everywhere — but rarely clean. Inconsistent headers, mixed date formats, empty rows, duplicated records, and broken encodings create friction for analytics and automation workflows. Manual fixes don’t scale.
The solution
The CSV Cleaner Toolkit provides a predictable cleaning pipeline: load → validate → standardise → export. It ensures that every file entering a system follows clear rules before being used downstream.
Key features
- Automatic header normalisation
- Date, number, and currency standardisation
- Duplicate detection and removal
- Empty row and invalid value filtering
- Schema validation with clear error output
- Batch processing support
Architecture
The toolkit is designed as composable steps — each transformation is explicit, testable, and reusable across projects.
Pipeline steps
- Loader
- Validator
- Cleaner
- Standardiser
- Exporter
Typical rules
- Required columns
- Type enforcement
- Allowed value ranges
- Encoding checks
Outcome
A reliable data-cleaning layer that removes guesswork. Downstream systems receive consistent, validated files, reducing bugs, reporting errors, and manual intervention.
Video
Toolkit demo (optional)
Optional walkthrough: raw CSV → cleaned output.
Gallery
Screens & outputs
Input files, validation output, and cleaned exports.
Tired of fixing CSVs manually?
If messy imports are slowing your analytics or automation, I can build a cleaning pipeline tailored to your data rules.