So what's happening here?

[Gary Herron]

On 06/05/2015 06:39 AM, Todd wrote:
> On Fri, Jun 5, 2015 at 3:23 PM, Gary Herron 
> <gary.herron at islandtraining.com 
> <mailto:gary.herron at islandtraining.com>> wrote:
>
>     On 06/05/2015 06:11 AM, Paul Appleby wrote:
>
>         On Fri, 05 Jun 2015 14:55:11 +0200, Todd wrote:
>
>             Numpy arrays are not lists, they are numpy arrays. They
>             are two
>             different data types with different behaviors.  In lists,
>             slicing is a
>             copy.  In numpy arrays, it is a view (a data structure
>             representing some
>             part of another data structure).  You need to explicitly
>             copy the numpy
>             array using the "copy" method to get a copy rather than a
>             view:
>
>         OK, thanks.  I see.
>
>         (I'd have thought that id(a[1]) and id(b[1]) would be the same
>         if they
>         were the same element via different "views", but the id's seem
>         to change
>         according to rules that I can't fathom.)
>
>     Nope.  It's odder than that.  a[1] is still a view into the
>     inderlying numpy array, and your id is the id of that view. Each
>     such index produces a new such view object. Check this out:
>
>     >>> import numpy
>     >>> a = numpy.array([1,2,3])
>     >>> id(a[1])
>     28392768
>     >>> id(a[1])
>     28409872
>
>     This produces two different view of the same underlying object.
>
>
> a[1] and b[1] are not views:
>
> >>> a[1].flags['OWNDATA']
> True
> >>> b[1].flags['OWNDATA']
> True
> >>> a[1:2].flags['OWNDATA']
> False

Right.  My bad.  Each execution of a[1] creates a new numpy.int64 object 
with the value from the array.

 >>> type(a[1])
<class 'numpy.int64'>

Each execution of id(a[1]) creates an int64 object which is immediately 
used and then deleted.  Two successive executions of id(a[1]) may or may 
not reuse the same piece of memory, depending on what else is going on 
in memory.  Indeed when I produced the above example with id(a[1]), a 
third and fourth runs of id(a[1]) did indeed repeat 28409872, but they 
are all new creations of an int64 object which happen to use the same 
recently freed bit of memory.

Gary Herron







>
>

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