How I Automated 50,000 Messy Excel Rows & 2,000 E-Commerce Product Images in 15 Seconds with Python

Описание и применение

Автоматизация обработки 2 000 изображений и 50 000 строк таблиц за 15 секунд с помощью Python, Pillow и openpyxl.

Задача

Manual resizing of thousands of supplier images to marketplace specs and cleaning 50,000+ rows of messy order spreadsheets were time‑consuming tasks.

Результат

The pipeline reduced image processing from ~14 hours to 14.8 seconds and spreadsheet cleaning from ~6 hours to 8.2 seconds, achieving a 15‑second total runtime for 2,000 images and 50,000 rows.

Рабочий процесс

• 1. Load each supplier image and resize to 1000x1000 using LANCZOS, apply UnsharpMask and contrast enhancement, then save as JPEG with quality 95 and optimization.
• 2. Strip EXIF metadata during the save process.
• 3. Read the order spreadsheet with openpyxl, apply regex to standardize phone numbers, and inject live Excel formulas directly into cells.
• 4. Write the cleaned data back to an ERP‑ready .xlsx workbook.
• 5. Measure and compare execution time against manual benchmarks.

Доказательства

Resizing thousands of supplier images into exact marketplace specs (1000x1000 square cover, 780px detail width) while stripping duplicate EXIF metadata.

Источник

2,000 Product Images (3 sizes + EXIF scrub) ~14 hours Manual Time, 14.8 seconds Python Pipeline Time

Источник

50,000 ERP Order Rows + Summary Formulas ~6 hours Manual Time, 8.2 seconds Python Pipeline Time

Источник