If you have ever worked with e-commerce sellers or operations teams, you know the two biggest time sinks:

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

Cleaning 50,000+ rows of messy order spreadsheets (mixed phone formats, bracketed option strings, duplicate order IDs) and injecting live =SUM() formulas into an ERP-ready .xlsx workbook.

Here is the exact minimal Python pipeline using Pillow and openpyxl that turns a 3-day manual job into a 15-second script.

1. High-Speed LANCZOS Image Normalizer + EXIF Scrubber

from PIL import Image , ImageFilter , ImageEnhance import io , hashlib

def normalize_product_image ( raw_img : Image . Image , index : int ) -> bytes : # Resize with LANCZOS and apply subtle sharpening for mobile clarity out = raw_img . resize (( 1000 , 1000 ), Image . Resampling . LANCZOS ) out = out . filter ( ImageFilter .…