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

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