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User stories - tracking issue #42
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User Story No. 1 |
User Story No. 2 |
User Story No. 3 |
User Story No. 4 Let me try one:) Maria is a lecture in the new Data Science department. She needs to teach an introductory class about NumPy and numerical computing to students that know basic Python and math. She is looking for material that can be used for in-person teaching in class, rather than tutorials aimed at self-learning. |
User Story No. 5 John is a data scientist, consuming libraries that use python like TensorFlow. He is running into a problem in his code that emits some strange error, and the traceback shows an exception from NumPy. What should his next step be? When should he open an issue on the NumPy issue tracker? |
User Story No. 6 I am a NumPy and SciPy developer, and although I have been using NumPy for many years, I still have occasion to check the online reference documentation for a function in the latest released version. I would like to be able to get to the API documentation with just one click from the main page (https://numpy.org/). For a nice example of what I would find useful, take a look at the pandas web page. The links to the reference documentation are right there in a box on the right side. With just one click I can get to the reference docs of the latest released version (or the development version, or older versions, too). |
User Story No. 7 I would like to contribute to NumPy more actively. Since NumPy is such a huge beast, wrapping my head around the functionalities of NumPy is really challenging. I think having a high-level overview / descriptions of NumPy and each of the module would be very helpful. Also, it would be great if C API documentation could have more structure, maybe something to python API. I found it hard to browse through the C APIs. Adding to @WarrenWeckesser comment, I also like the way scikit-learn webpage separate out the API documentation from tutorial and have links from API to tutorials and examples. Maybe it's just because I'm more familiar with their docs. |
User Story No. 8 Kartoffel Schmidt is a data scientist/researcher who's writing code for NumPy, and he knows that at some point, he may have to translate his code to use distributed computing, GPU computing or sparse computing. What guidelines should he follow to make sure that his code will be portable across Dask, CuPy or PyData/Sparse in the future? |
User Story No. 9 |
User Story No. 10 |
User Story No. 11 |
User Story No. 12 |
User Story No. 13 |
Packagers definitely need their own story, thanks.
Everyone should delegate to the package manager provided BLAS. Only NumPy itself should break that rule, because of PyPI/pip limitations. |
Based on #406, placing a link to https://numpy.org/citing-numpy/ in the footer. |
User stories must have acceptance criteria and a definition of "done."
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