Hi everyone,
I am an engineering student building a Python script to help automate data cleaning for Shopify imports (fixing column names, formatting, etc.).
I need to test my code against real-world “messy” data structures to see if it breaks.
Does anyone have a problematic CSV structure or a file that failed to import recently? I would love to see the column headers or a small sample to improve my script. I can try running it on your file to see if it fixes the errors.
Please ensure no sensitive data is included. Thanks for the help!
Hey @Diegojs!
The best way to check your tool by uploading the messy CSV file. I mean delete the column or add the non acceptable CSV file and then check whether your tool able to detect that specific issue or not.
Thanks @ecom-experts!
That is a great idea. I’ve actually been doing exactly that using the standard apparel.csv sample from Shopify’s docs—intentionally deleting headers and mixing up data types to stress-test the script. It works well for those syntax errors.
The reason I’m asking for real-world examples is to catch the “weird” edge cases that are hard to simulate artificially (like mixed text encodings, specific non-standard delimiters used by some suppliers, or inconsistent attribute mapping).
If you recall any specific file structure that was a nightmare for you in the past, I’d love to know about it!
Sorry @Diegojs! Currently I don’t have any where I face issue.
I will touch with you and share once I face the issue. Anyway, can you share the link of the tool. This will help us to all community members.
Thanks for the interest [email removed]ecom-experts!
Right now, it’s running as a local Python script (backend only), so there isn’t a public web interface or URL yet.
I am planning to wrap it into a simple web app for the community soon. I will definitely come back to this thread and share the link once the beta is live!
In the meantime, I’m manually processing files for anyone who needs help to refine the logic.