Be able to run Python scripts to interrogate SQL queries
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- Dominant language
- Python
- Stars
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Description
Feature request
Is your feature request related to a problem?
The level of depth in SQL developers have varies a lot, even amongst Python experts. Sometimes we don't know how to solve for something as simple as a for loop but in SQL.
Describe the solution you'd like
It would be great to be able to run Python (e.g. notebooks) on the SQL data one pulls. This would let us e.g. build a table in SQL and go deep on the question using Python.
Describe alternatives you've considered
Downloading and running locally or connecting the DB somehow to external notebook.
Additional context
Debug info
- [ ] PostHog Cloud, Debug information: [please copy/paste from https://us.posthog.com/settings/project-details#variables]
- [ ] PostHog Hobby self-hosted with `docker compose`, version/commit: [please provide]
- [ ] PostHog self-hosted with Kubernetes (deprecated, see [`Sunsetting Kubernetes support`](https://posthog.com/blog/sunsetting-helm-support-posthog)), version/commit: [please provide]
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue describes running Python notebooks against SQL query data but names no files, tests, or entry points. Start by mapping the existing SQL query and results workflow and searching for notebook or Python integration points. Done should be defined as a supported way to use query data in Python, with clear scope and acceptance criteria agreed with maintainers.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python, sql
- Domain
- analytics, data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100