Forum Discussion
Python Module Not Found Error When Importing Pandas: 'no module named 'fsspec_wrapper.trident.core'
- 1 year ago
This issue seems to stem from a recent change in how Fabric handles internal dependencies—particularly around the fsspec_wrapper.trident.core module, which is now required when importing pandas in custom environments.
🔍 Root Cause
The error:
ModuleNotFoundError: No module named 'fsspec_wrapper.trident.core'
typically occurs when using custom environments in Fabric notebooks. It appears that the pandas import is now indirectly dependent on internal Fabric components that are not bundled in custom environments by default.
âś… Workarounds That Helped Others
Switch to the Workspace Default Environment
This resolves the issue for many users, but it’s not always viable if your custom environment includes packages not available in the default one.Force Re-Publish the Custom Environment
As suggested by Microsoft staff, make any change to your custom environment (e.g., add a dummy package like pytest from PyPI), save, and redeploy. This forces a refresh of the environment and often resolves the issue [1].Manually Add fsspec from Public Libraries
Some users had success by adding the latest version of fsspec (e.g., from February 2025) via the public library tab. However, this alone may not always fix the issue if the internal wrapper is still missing.Upgrade the Runtime Version
Upgrading your custom environment’s runtime (e.g., from 1.2 to 1.3) may help, as it updates the Spark version and potentially resolves dependency mismatches.
⚠️ Long-Term Consideration
This seems to be a platform-level dependency issue, and while workarounds exist, it may reoccur with future updates. If you're running production workloads, consider:
- Monitoring Fabric release notes for changes to internal libraries.
- Creating a fallback environment strategy.
- Submitting feedback to Microsoft to improve dependency isolation in custom environments.
The workaround is to force the environment to republish. This will fix the problem until the next time internal core libraries in Fabric are changing. In order to publish _any_ change must be made to the environment. It does not matter what you're doing. I tested by adding the public "pytest" package from PyPi, saved, and deployed the environment and the problem was gone.
We've already implemented the workaround, but it remains a workaround. My concern is if this kind scenario is likely to be a recurring thing, and if there's anything we could/should be doing to mitigate against it in a production environment? At least one person above has indicated the issue has re-occurred for them, and required fixing with a workaround for a second time this week.