On Tue, Aug 12, 2014 at 12:17 PM, 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
On Sun, Jan 25, 2015 at 1:48 PM, Warren Weckesser <
warren.weckes...@gmail.com> wrote:
>
>
> On Wed, Aug 13, 2014 at 6:17 PM, Eelco Hoogendoorn <
> hoogendoorn.ee...@gmail.com> wrote:
>
>> Its pretty easy to implement this table functionality and more on top of
>> the code I linked above. I still
On Wed, Aug 13, 2014 at 6:17 PM, Eelco Hoogendoorn <
hoogendoorn.ee...@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
>
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
On Wed, Aug 13, 2014 at 5:15 PM, Benjamin Root 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
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.
P
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(a
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
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
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
On Tue, Aug 12, 2014 at 11:35 AM, Warren Weckesser <
warren.weckes...@gmail.com> wrote:
> I created a pull request (https://github.com/numpy/numpy/pull/4958) that
> defines the function `count_unique`. `count_unique` generates a
> contingency table from a collection of sequences. For example,
>
I created a pull request (https://github.com/numpy/numpy/pull/4958) that
defines the function `count_unique`. `count_unique` generates a
contingency table from a collection of sequences. For example,
In [7]: x = [1, 1, 1, 1, 2, 2, 2, 2, 2]
In [8]: y = [3, 4, 3, 3, 3, 4, 5, 5, 5]
In [9]: (xvals
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