Open avriiil opened 1 year ago
For quick comparison, here's a screenshot of Discourse categories:
And highest-traffic Github issue labels:
Does anyone here have experience using the Github API to fetch and run analyses on repo data?
Thanks for doing this @rrpelgrim . I'd like to also suggest that you read through a bunch of the top issues (maybe 100 or so?) to get a feel for them as well. I think that getting dirty with the actual questions is important to understand things. I welcome the quantitative analysis that you've done here. I'd like to also encourage you to get in and get qualitative as well. I suspect that it gives a different kind of knowledge.
I'm starting to dig more deeply into Dask user experience. I'd like to experiment with a more data-driven approach based on the various places where we interact with Dask users, specifically StackOverflow, Discourse and Github.
As a first step in that direction I've been playing around with scraping all StackOverflow questions and running some basic analyses on that data. Pretty rudimentary still atm but thought I'd share here for visibility and feedback.
Some Interesting Findings
Interesting to see a mix of pandas-related questions (expected) as well as Parquet and Spark-related questions. A list like this could be used to inform Dask messaging and content creation on the blog or social channels. These are clearly the topics folks are interested in most.
These are the top 10 most popular "sub-tags". Assuming that all "pandas"-tagged questions are DataFrame questions, that would mean that almost half of the questions (48.7%) are related to Dask DataFrame.
Possible Next Steps
It would be interesting to:
I'm going to keep chugging ahead here but would welcome any feedback and further discussion. If anyone's interested in helping me build out analyses for Discourse and Github, I'd be very happy to join forces.
NOTEBOOK: https://github.com/rrpelgrim/dask-stackoverflow/blob/main/scrape-stackoverflow.ipynb