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

说明与用途

使用Python、Pillow和openpyxl将2,000张产品图片和50,000行表格数据自动化处理，仅耗时15秒。

问题

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

结果

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.

工作流

• 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.

证据

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

来源

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

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

来源