[Numpy-discussion] Re: Suggestion to show the shape in repr for summarized arrays

[Evgeni Burovski via NumPy-Discussion]

>
> Given that we've already chosen to use the fake `shape=...` keyword when
> there is a 0-length axis, what do you think we should do, consistency-wise?
>
> >>> np.empty([10, 0])
> array([], shape=(10, 0), dtype=float64)
>
> 1. Follow the precedent and use the fake `shape=...` keyword in the
> summarized-array case.
> 2. Ignore the precedent and use a following `# shape=...` comment
> afterwards in the summarized-array case and leave the 0-length-axis case
> alone.
> 3. Fix the 0-length-axis case to use the following `# shape=...` comment
> too.
>
>
Consistency-wise, I guess option 3 (fix 0-length-axis case) is the best
one; whether it's worth code churn in NumPy... this can go either way, so
option 2 (ignore the precedent and keep 0-length-axis arrays alone) is
fine, too, IMO.

Evgeni
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[Numpy-discussion] Re: Suggestion to show the shape in repr for summarized arrays

[Evgeni Burovski via NumPy-Discussion]

I like this, too.

And I think the trailing comment, # shape=... is much better, as it gets
the best of both worlds: user get the info, and tools which do
eval(repr(..)) only need to learn to ignore the comment. For one, numpy's
own doctests will keep working with no churn since scipy_doctest handles
this already.

Evgeni

вт, 1 окт. 2024 г., 01:07 Chris Barker via NumPy-Discussion <
numpy-discussion@python.org>:

> I like this.
>
> while ideally, eval(repr(an_object)) == object, in practice this is
> already violated fro large arays -- so other than doctests, this shouldn't
> cause too many headaches.
>
> -CHB
>
>
> On Mon, Sep 30, 2024 at 10:13 AM Marten van Kerkwijk <
> m...@astro.utoronto.ca> wrote:
>
>> Hi All,
>>
>> When the repr of an array is shown, currently the dtype and shape are
>> explicitly listed if these cannot be directly inferred from the list
>> that is shown, i.e., if the dtype is not float64 or int64, and if the
>> size of the array is zero, but the shape not the simple (0,).
>>
>> For instance,
>>
>> ```
>> np.empty((10,2,0), dtype="i2")
>> array([], shape=(10, 2, 0), dtype=int16)
>> ```
>>
>> I propose to also show the shape for the (rare) case that an array is
>> summarized, i.e., when it has more than the default threshold of 1000
>> elements, and elements are replaced by `...`.  The logic is that also in
>> that case it is no longer clear what the shape actually is, which is
>> useful information (e.g., if working in a notebook -- which is the
>> original use case at https://github.com/numpy/numpy/issues/27461).
>>
>> I have a PR for that at https://github.com/numpy/numpy/pull/27482
>> which would lead to the following:
>>
>> ```
>> np.arange(1001)
>> array([   0,1,2, ...,  998,  999, 1000], shape=(1001,))
>> ```
>>
>> Just to be sure: this PR causes *no* change for any arrays with sizes
>> less than a 1000, so I do not believe this change will lead to a lot of
>> unnecessary churn for down-stream packages.  Indeed, between numpy and
>> astropy (which has lots of doctests), the only changes to (doc)tests
>> that were needed are the very few for arrays where the "threshold" is
>> explicitly exceeded.
>>
>> One irritant is that the shape is not an argument that can be passed in
>> to an `np.array` call.  While this is just as much the case for
>> zero-sized arrays, perhaps a better solution would be to move the shape
>> information out of the parentheses, e.g., using ``...)  # shape=(...)``.
>> I can change the PR to do that if that's the consensus.
>>
>> All the best,
>>
>> Marten
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>>
>
>
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>
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[Numpy-discussion] I am getting an error in anaconda jupyter notebook, I am unable to solve. please help me.

[Usha Gayatri via NumPy-Discussion]

This is the error i am getting when trying to execute import numpy as np
File E:\anaconda3\Lib\site-packages\pandas\_libs\interval.pyx:1, in init 
pandas._libs.interval()

ValueError: numpy.dtype size changed, may indicate binary incompatibility. 
Expected 96 from C header, got 88 from PyObject
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[Numpy-discussion] Re: Suggestion to show the shape in repr for summarized arrays

[Robert Kern via NumPy-Discussion]

On Tue, Oct 1, 2024 at 9:57 AM Evgeni Burovski via NumPy-Discussion <
numpy-discussion@python.org> wrote:

> I like this, too.
>
> And I think the trailing comment, # shape=... is much better, as it gets
> the best of both worlds: user get the info, and tools which do
> eval(repr(..)) only need to learn to ignore the comment. For one, numpy's
> own doctests will keep working with no churn since scipy_doctest handles
> this already.
>

Given that we've already chosen to use the fake `shape=...` keyword when
there is a 0-length axis, what do you think we should do, consistency-wise?

>>> np.empty([10, 0])
array([], shape=(10, 0), dtype=float64)

1. Follow the precedent and use the fake `shape=...` keyword in the
summarized-array case.
2. Ignore the precedent and use a following `# shape=...` comment
afterwards in the summarized-array case and leave the 0-length-axis case
alone.
3. Fix the 0-length-axis case to use the following `# shape=...` comment
too.

-- 
Robert Kern
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[Numpy-discussion] Re: I am getting an error in anaconda jupyter notebook, I am unable to solve. please help me.

[matti picus via NumPy-Discussion]

Your version of pandas is not compatible with your version of numpy.
Try reinstalling both of them.
Matti

On Tue, Oct 1, 2024 at 4:27 PM Usha Gayatri via NumPy-Discussion
 wrote:
>
> This is the error i am getting when trying to execute import numpy as np
> File E:\anaconda3\Lib\site-packages\pandas\_libs\interval.pyx:1, in init 
> pandas._libs.interval()
>
> ValueError: numpy.dtype size changed, may indicate binary incompatibility. 
> Expected 96 from C header, got 88 from PyObject
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> Member address: matti.pi...@gmail.com
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