On Mon, May 14, 2012 at 9:47 PM, Ralf Gommers
wrote:
>
>
> On Sun, May 13, 2012 at 1:14 PM, Paul Anton Letnes
> wrote:
>>
>> On Sat, May 12, 2012 at 9:50 PM, Ralf Gommers
>> wrote:
>> >
>> >
>> > On Sun, May 6, 2012 at 12:12 AM, Charles R Harris
>> > wrote:
>> >>
>> >>
>> >>
>> >> On Sat, May 5
On May 14, 2012, at 7:07 PM, Stéfan van der Walt wrote:
> Hi Zach
>
> On Mon, May 14, 2012 at 4:33 PM, Zachary Pincus
> wrote:
>> The below seems to be a bug, but perhaps it's unavoidably part of the
>> indexing mechanism?
>>
>> It's easiest to show via example... note that using "[0,1]" to
> On Mon, May 14, 2012 at 4:33 PM, Zachary Pincus
> wrote:
>> The below seems to be a bug, but perhaps it's unavoidably part of the
>> indexing mechanism?
>>
>> It's easiest to show via example... note that using "[0,1]" to pull two
>> columns out of the array gives the same shape as using ":2
Hi Zach
On Mon, May 14, 2012 at 4:33 PM, Zachary Pincus wrote:
> The below seems to be a bug, but perhaps it's unavoidably part of the
> indexing mechanism?
>
> It's easiest to show via example... note that using "[0,1]" to pull two
> columns out of the array gives the same shape as using ":2"
Hello all,
The below seems to be a bug, but perhaps it's unavoidably part of the indexing
mechanism?
It's easiest to show via example... note that using "[0,1]" to pull two columns
out of the array gives the same shape as using ":2" in the simple case, but
when there's additional slicing happe
On Mon, May 14, 2012 at 5:31 PM, mark florisson
wrote:
> On 12 May 2012 22:55, Dag Sverre Seljebotn
> wrote:
> > On 05/11/2012 03:37 PM, mark florisson wrote:
> >>
> >> On 11 May 2012 12:13, Dag Sverre Seljebotn
> >> wrote:
> >>>
> >>> (NumPy devs: I know, I get too many ideas. But this time I *
On Sun, May 13, 2012 at 9:48 AM, Dinesh Prasad wrote:
> Hello. I am new to the list thanks for accepting my question.
>
> I am trying to run the attached code, directly from the book in the title.
>
> It simply calculates correlation of returns of the stock listed in the
> spreadsheets. could it b
On 05/14/2012 10:36 PM, Dag Sverre Seljebotn wrote:
> On 05/14/2012 06:31 PM, mark florisson wrote:
>> On 12 May 2012 22:55, Dag Sverre Seljebotn
>> wrote:
>>> On 05/11/2012 03:37 PM, mark florisson wrote:
On 11 May 2012 12:13, Dag Sverre Seljebotn
wrote:
>
> (NumPy dev
On 05/14/2012 06:31 PM, mark florisson wrote:
> On 12 May 2012 22:55, Dag Sverre Seljebotn wrote:
>> On 05/11/2012 03:37 PM, mark florisson wrote:
>>>
>>> On 11 May 2012 12:13, Dag Sverre Seljebotn
>>> wrote:
(NumPy devs: I know, I get too many ideas. But this time I *really*
beli
On Sun, May 13, 2012 at 1:14 PM, Paul Anton Letnes <
paul.anton.let...@gmail.com> wrote:
> On Sat, May 12, 2012 at 9:50 PM, Ralf Gommers
> wrote:
> >
> >
> > On Sun, May 6, 2012 at 12:12 AM, Charles R Harris
> > wrote:
> >>
> >>
> >>
> >> On Sat, May 5, 2012 at 2:56 PM, Paul Anton Letnes
> >> w
For what it's worth, I'd prefer ndmasked.
As has been mentioned elsewhere, some algorithms can't really cope with
missing data. I'd very much rather they fail than silently give incorrect
results. Working in the climate prediction business (as with many other
domains I'm sure), even the *potential
On 12 May 2012 22:55, Dag Sverre Seljebotn wrote:
> On 05/11/2012 03:37 PM, mark florisson wrote:
>>
>> On 11 May 2012 12:13, Dag Sverre Seljebotn
>> wrote:
>>>
>>> (NumPy devs: I know, I get too many ideas. But this time I *really*
>>> believe
>>> in it, I think this is going to be *huge*. And i
> is this intended?
>
> np.histogramdd([[1,2],[3,4]],bins=2)
>
> (array([[ 1., 0.],
>[ 0., 1.]]),
> [array([ 1. , 1.5, 2. ]), array([ 3. , 3.5, 4. ])])
>
> np.histogram2d([1,2],[3,4],bins=2)
>
> (array([[ 1., 0.],
>[ 0., 1.]]),
> array([ 1. , 1.5, 2. ]),
> array([ 3
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