On 12 Dec 2014 23:22, "Valentin Haenel" wrote:
>
> Dear Nathaniel,
>
> thanks very much for your response.
>
> * Nathaniel Smith [2014-12-11]:
> > On Wed, Dec 10, 2014 at 8:26 PM, Valentin Haenel
wrote:
> > > I am using numpy version 1.9.0 and Python 2.7.9 and have a question
> > > about the dty
Dear Nathaniel,
thanks very much for your response.
* Nathaniel Smith [2014-12-11]:
> On Wed, Dec 10, 2014 at 8:26 PM, Valentin Haenel wrote:
> > I am using numpy version 1.9.0 and Python 2.7.9 and have a question
> > about the dtype:
> >
> > In [14]: np.dtype(" > Out[14]: dtype('float64')
> >
On 12 Dec 2014 19:29, "Stephan Hoyer" wrote:
>
> def common_shape(*args):
Nitpick: let's call this broadcast_shape, not common_shape; it's as-or-more
clear and clearly groups the related functions together.
-n
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On Fri, Dec 12, 2014 at 11:28 AM, Stephan Hoyer wrote:
>
> On Fri, Dec 12, 2014 at 5:48 AM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>> np.broadcast is the Python object of the old iterator. It may be a better
>> idea to write all of these functions using the new one, np.nditer:
On Fri, Dec 12, 2014 at 6:25 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> it seems that all the functionality that has been discussed are one-liners
> using nditer: do we need new functions, or better documentation?
>
I think there is utility to adding a new function or two (my in
On Fri, Dec 12, 2014 at 5:48 AM, Jaime Fernández del Río <
jaime.f...@gmail.com> wrote:
> np.broadcast is the Python object of the old iterator. It may be a better
> idea to write all of these functions using the new one, np.nditer:
>
> def common_shape(*args):
> return np.nditer(args).shape[:
On Fr, 2014-12-12 at 06:25 -0800, Jaime Fernández del Río wrote:
> On Fri, Dec 12, 2014 at 5:57 AM, Sebastian Berg
> wrote:
> On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río
> wrote:
> > On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer
>
> > wrote:
On Fri, Dec 12, 2014 at 5:57 AM, Sebastian Berg
wrote:
>
> On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río wrote:
> > On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer
> > wrote:
> > On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg
> > wrote:
> > One option
On Fr, 2014-12-12 at 05:48 -0800, Jaime Fernández del Río wrote:
> On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer
> wrote:
> On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg
> wrote:
> One option
> would also be to have something like:
>
On Thu, Dec 11, 2014 at 10:53 AM, Stephan Hoyer wrote:
>
> On Thu, Dec 11, 2014 at 8:17 AM, Sebastian Berg <
> sebast...@sipsolutions.net> wrote:
>
>> One option
>> would also be to have something like:
>>
>> np.common_shape(*arrays)
>> np.broadcast_to(array, shape)
>> # (though I would like many
Hello,
We are proud to announce v0.15.2 of pandas, a minor release from 0.15.1.
This release includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug
fixes.
This was a short release of 4 weeks with 137 commits by 49
On Do, 2014-12-11 at 16:20 +, Robert Kern wrote:
> On Thu, Dec 11, 2014 at 4:17 PM, Sebastian Berg
> wrote:
> >
> > On Do, 2014-12-11 at 16:56 +0100, Pierre Haessig wrote:
> > > Le 11/12/2014 16:52, Robert Kern a écrit :
> > > >
> > > > And we already have a numpy.broadcast() function.
> > > >
Chris Barker wrote:
> Anyway, the point is that if we wanted this to be a
> used-more-than-very-rarely in only very special cases feature de-allocating
> an array's data buffer), then ndarray would need to grow a check for an
> invalid buffer on access.
One would probably need something like Jav
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