On Wed, Aug 13, 2014 at 5:15 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> The ever-wonderful pylab mode in matplotlib has a table function for > plotting a table of text in a plot. If I remember correctly, what would > happen is that matplotlib's table() function will simply obliterate the > numpy's table function. This isn't a show-stopper, I just wanted to point > that out. > > Personally, while I wasn't a particular fan of "count_unique" because I > wouldn't necessarially think of it when needing a contingency table, I do > like that it is verb-ish. "table()", in this sense, is not a verb. That > said, I am perfectly fine with it if you are fine with the name collision > in pylab mode. > > Thanks for pointing that out. I only changed it to have something that sounded more table-ish, like the Pandas, R and Matlab functions. I won't update it right now, but if there is interest in putting it into numpy, I'll rename it to avoid the pylab conflict. Anything along the lines of `crosstab`, `xtable`, etc., would be fine with me. Warren > On Wed, Aug 13, 2014 at 4:57 PM, Warren Weckesser < > warren.weckes...@gmail.com> wrote: > >> >> >> >> On Tue, Aug 12, 2014 at 12:51 PM, Eelco Hoogendoorn < >> hoogendoorn.ee...@gmail.com> wrote: >> >>> ah yes, that's also an issue I was trying to deal with. the semantics I >>> prefer in these type of operators, is (as a default), to have every array >>> be treated as a sequence of keys, so if calling unique(arr_2d), youd get >>> unique rows, unless you pass axis=None, in which case the array is >>> flattened. >>> >>> I also agree that the extension you propose here is useful; but ideally, >>> with a little more discussion on these subjects we can converge on an >>> even more comprehensive overhaul >>> >>> >>> On Tue, Aug 12, 2014 at 6:33 PM, Joe Kington <joferking...@gmail.com> >>> wrote: >>> >>>> >>>> >>>> >>>> On Tue, Aug 12, 2014 at 11:17 AM, Eelco Hoogendoorn < >>>> hoogendoorn.ee...@gmail.com> wrote: >>>> >>>>> Thanks. Prompted by that stackoverflow question, and similar problems >>>>> I had to deal with myself, I started working on a much more general >>>>> extension to numpy's functionality in this space. Like you noted, things >>>>> get a little panda-y, but I think there is a lot of panda's functionality >>>>> that could or should be part of the numpy core, a robust set of grouping >>>>> operations in particular. >>>>> >>>>> see pastebin here: >>>>> http://pastebin.com/c5WLWPbp >>>>> >>>> >>>> On a side note, this is related to a pull request of mine from awhile >>>> back: https://github.com/numpy/numpy/pull/3584 >>>> >>>> There was a lot of disagreement on the mailing list about what to call >>>> a "unique slices along a given axis" function, so I wound up closing the >>>> pull request pending more discussion. >>>> >>>> At any rate, I think it's a useful thing to have in "base" numpy. >>>> >>>> _______________________________________________ >>>> NumPy-Discussion mailing list >>>> NumPy-Discussion@scipy.org >>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>>> >>>> >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> Update: I renamed the function to `table` in the pull request: >> https://github.com/numpy/numpy/pull/4958 >> >> >> Warren >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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