matloff / matloff/R-vs.-Python-for-Data-Science
Available libraries a tie?
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Description
Thank you very much for your overview!
However, you call the race about "available libraries" a tie even though you mention that quite basic statistical procedures are not available (or hard to find) in Python:
> The following searches in PyPI turned up nothing: log-linear model; Poisson regression; instrumental variables; spatial data; familywise error rate; etc.
I would say that this is a huge minus for Python?! In my own experience, the package availability for time series modeling is even worse.
In general, a search for statistics at PyPI (https://pypi.org/search/?q=statistics) returns only 2,541 packages (what do I care about packages like (https://pypi.org/project/plone.event/) 😄).
(Sidenote: I am a fan of the tidyverse and would say that it is also beneficial for professional users but I recognize that this is open for debate)
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Research direction
Review the overview discussed in the issue and the cited PyPI search results. Compare the claim that library availability is a tie with the listed gaps in statistical procedures and time-series modeling. Done means the overview addresses this feedback and its conclusion is supported by the available evidence.
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Assessment
- Tech stack
- python
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100