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I will automate repetitive CSV and json file processing with python
Alexis H
About this gig
Turn a repeated file task into a reusable local Python script with clear outputs and exception reports.
One input format and schema: UTF-8 comma-separated CSV with headers, or a flat JSON array of objects with text values. Up to 4 fields, 10,000 rows and 5 MiB per run. One identifier plus up to 3 transformations: DD/MM/YYYY dates to ISO, trimming label spaces, and integer quantities (1-1,000,000). Field names and selected types come from your mandatory requirements.
All duplicate-ID rows and invalid values go to exceptions; no silent deletion. Structural errors stop processing. The original is preserved.
You receive Python source, local instructions, synthetic acceptance tests, clean CSV, exceptions JSON, change log and summary. You run your real data locally on Windows 11 with Python 3.12; no installation or batch-processing service is included.
$180, 7 calendar days from submission of requirements, 1 in-scope revision and correction of my own defects. Ask before buying if unsure; consultation is optional. My gallery uses my own synthetic demonstration, not client data.
Get to know Alexis H
Alexis H
Python automation for CSV and JSON files
- FromSpain
- Member sinceAug 2023
Languages
English, Spanish, Catalan
I build reusable local Python scripts for repetitive CSV and flat JSON tasks, with clear rules, validated outputs and exception reports. Deliverables include source code, instructions and tests. Since 2020, I have created tools and scripts for personal use and to improve my work and colleagues' workflows. My personal file normalization and validation projects have been active since 2024. My public demonstration uses synthetic data. Communication in Spanish and written English.
FAQ
What schema is supported?
One text ID field, plus optional date, label and quantity fields (maximum four). Each role appears once. Your requirements specify exact headers/keys and selected roles. ID: 1-40 ASCII letters, digits or underscore; leading zeros preserved.
Which transformations are included?
Up to three: DD/MM/YYYY dates to YYYY-MM-DD; trim outer label spaces (nonempty); integer quantities 1-1,000,000, with no decimals. No alternate dates, merges, calculations, semantic decisions or additional rule types.
What happens to duplicates and invalid data?
ALL rows with a repeated ID go to exceptions; no keep-first/last or silent deletion. Invalid values and possible formulas are reported conservatively. Malformed structure or exceeded limits stops processing without publishing a partial result.
Which files and environment are compatible?
UTF-8 comma CSV with unique headers (BOM supported), or flat JSON array with unique keys and string values. Up to 10,000 rows and 5,242,880 bytes per run. Windows 11 / Python 3.12 / command line. No nested JSON, native Excel, scraping, APIs or remote installation.
What deliverables and acceptance are included?
Python source, instructions, synthetic tests and results; clean CSV, exceptions JSON with original row/reason, change log and summary with counts/hashes. Your sanitized example and exact expected rows define acceptance. Original bytes stay unchanged; existing output folders are never overwritten.
When does the delivery period start?
Fiverr starts the seven-calendar-day period when you submit requirements, without waiting for my manual acceptance. Missing or incompatible answers do not automatically pause it. Ask before buying if unsure; consultation is optional.
What does one revision cover?
One revision within the selected schema and listed rule types, plus correction of my own defects. New schemas, formats or rule types are outside this package. The package supplies a reusable script; processing your real batch for you is excluded.
What data should I send?
Only small synthetic or sanitized examples and expected results, without personal data, contacts, credentials or private records. You run real data locally yourself. CSV bytes preserve text IDs, but spreadsheet applications may reinterpret them when opening the file.
