On Sun, Jan 25, 2015 at 1:48 PM, Warren Weckesser < warren.weckesser@gmail.com> wrote:
On Wed, Aug 13, 2014 at 6:17 PM, Eelco Hoogendoorn < hoogendoorn.eelco@gmail.com> wrote:
Its pretty easy to implement this table functionality and more on top of the code I linked above. I still think such a comprehensive overhaul of arraysetops is worth discussing.
import numpy as np import grouping x = [1, 1, 1, 1, 2, 2, 2, 2, 2] y = [3, 4, 3, 3, 3, 4, 5, 5, 5] z = np.random.randint(0,2,(9,2)) def table(*keys): """ desired table implementation, building on the index object cleaner, and more functionality performance should be the same """ indices = [grouping.as_index(k, axis=0) for k in keys] uniques = [i.unique for i in indices] inverses = [i.inverse for i in indices] shape = [i.groups for i in indices] t = np.zeros(shape, np.int) np.add.at(t, inverses, 1) return tuple(uniques), t #here is how to use print table(x,y) #but we can use fancy keys as well; here a composite key and a row-key print table((x,y), z) #this effectively creates a sparse matrix equivalent of your desired table print grouping.count((x,y))
On Wed, Aug 13, 2014 at 11:25 PM, Warren Weckesser < warren.weckesser@gmail.com> wrote:
On Wed, Aug 13, 2014 at 5:15 PM, Benjamin Root <ben.root@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.weckesser@gmail.com> wrote:
On Tue, Aug 12, 2014 at 12:51 PM, Eelco Hoogendoorn < hoogendoorn.eelco@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 <joferkington@gmail.com> wrote:
> > > > On Tue, Aug 12, 2014 at 11:17 AM, Eelco Hoogendoorn < > hoogendoorn.eelco@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 > >
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Update: I renamed the function to `table` in the pull request: https://github.com/numpy/numpy/pull/4958
Warren
Hey all,
I'm reviving this thread about the proposed `table` enhancement in https://github.com/numpy/numpy/pull/4958, because Chuck has poked me (via the pull request ) about it, so I'm poking the mailing list. Ignoring the issue of the name for the moment, is there any opposition to adding the proposed `table` function to numpy? I don't think it would preclude adding more powerful tools later, but that's not something I have time to work on at the moment.
If the only issue is the name, I'm open to any suggestions. I started with `count_unique`, and changed it to `table`, but Benjamin pointed out the potential conflict of `table` with a matplotlib function.
Warren
Looks like the original email in the thread is not part of the quoted (and somewhat disordered) emails. Here's my original email from last August: http://mail.scipy.org/pipermail/numpy-discussion/2014-August/070941.html Warren
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