[Numpy-discussion] Problem with concatenate and object arrays
Charles R Harris
charlesr.harris at gmail.com
Thu Sep 7 15:29:45 EDT 2006
On 9/7/06, Charles R Harris <charlesr.harris at gmail.com> wrote:
>
>
>
> On 9/7/06, Travis Oliphant <oliphant.travis at ieee.org> wrote:
> >
> > Charles R Harris wrote:
> > > On 9/6/06, *Charles R Harris* <charlesr.harris at gmail.com
> > > <mailto:charlesr.harris at gmail.com >> wrote:
> > >
> > >
> > >
> > > On 9/6/06, *Travis Oliphant* < oliphant.travis at ieee.org
> > > <mailto: oliphant.travis at ieee.org>> wrote:
> > >
> > > Charles R Harris wrote:
> > > >
> > > > Where is array at this point?
> > > Basically it supports the old Numeric behavior wherein object
> > > array's
> > > are treated as before *except* for when an error would have
> > > occurred
> > > previously when the "new behavior" kicks in. Anything that
> > > violates
> > > that is a bug needing to be fixed.
> > >
> > > This leaves the new object-array constructor used less
> > > often. It could
> > > be exported explicitly into an oarray constructor, but I'm not
> >
> > > sure
> > > about the advantages of that approach. There are benefits to
> > > having
> > > object arrays constructed in the same way as other arrays. It
> > > turns out
> > > many people actually like that feature of Numeric, which is
> > > the reason I
> > > didn't go the route of numarray which pulled object arrays
> > out.
> > >
> > > At this point, however, object arrays can even be part of
> > > records and so
> > > need to be an integral part of the data-type description.
> > > Pulling that
> > > out is not going to happen. A more intelligent object-array
> > > constructor, however, may be a useful tool.
> > >
> > >
> > > OK. I do have a couple of questions. Let me insert the docs for
> > > array and asarray :
> > >
> > > """array(object, dtype=None, copy=1,order=None,
> > subok=0,ndmin=0)
> > >
> > > Return an array from object with the specified date-type.
> > >
> > > Inputs:
> > > object - an array, any object exposing the array interface,
> > any
> > > object whose __array__ method returns an array, or
> > any
> > > (nested) sequence.
> > > dtype - The desired data-type for the array. If not given,
> > > then
> > > the type will be determined as the minimum type
> > > required
> > > to hold the objects in the sequence. This
> > > argument can only
> > > be used to 'upcast' the array. For downcasting,
> > > use the
> > > .astype(t) method.
> > > copy - If true, then force a copy. Otherwise a copy will
> > > only occur
> > > if __array__ returns a copy, obj is a nested
> > > sequence, or
> > > a copy is needed to satisfy any of the other
> > > requirements
> > > order - Specify the order of the array. If order is 'C',
> > > then the
> > > array will be in C-contiguous order (last-index
> > > varies the
> > > fastest). If order is 'FORTRAN', then the
> > > returned array
> > > will be in Fortran-contiguous order (first-index
> > > varies the
> > > fastest). If order is None, then the returned
> > > array may
> > > be in either C-, or Fortran-contiguous order or
> > even
> > > discontiguous.
> > > subok - If True, then sub-classes will be passed-through,
> > > otherwise
> > > the returned array will be forced to be a
> > > base-class array
> > > ndmin - Specifies the minimum number of dimensions that the
> > > resulting
> > > array should have. 1's will be pre-pended to the
> > > shape as
> > > needed to meet this requirement.
> > >
> > > """)
> > >
> > > asarray(a, dtype=None, order=None)
> > > Returns a as an array.
> > >
> > > Unlike array(), no copy is performed if a is already an array.
> > > Subclasses
> > > are converted to base class ndarray.
> > >
> > > 1) Is it true that array doesn't always return a copy except by
> > > default? asarray says it contrasts with array in this regard.
> > > Maybe copy=0 should be deprecated.
> > >
> > > 2) Is asarray is basically array with copy=0?
> > >
> > > 3) Is asanyarray basically array with copy=0 and subok=1?
> > >
> > > 4) Is there some sort of precedence table for conversions? To me
> > > it looks like the most deeply nested lists are converted to arrays
> > > first, numeric if they contain all numeric types, object
> > > otherwise. I assume the algorithm then ascends up through the
> > > hierarchy like traversing a binary tree in postorder?
> > >
> > > 5) All nesting must be to the same depth and the deepest nested
> > > items must have the same length.
> > >
> > > 6) How is the difference between lists and "lists" determined, i.e
> > .,
> > >
> > > In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
> > > Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
> > >
> > > In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
> > > Out[8]: array([[1 2 3], [1 2]], dtype=object)
> > >
> > >
> > > In [9]: array([1,2,3],[1,2]], dtype = object)
> > > ------------------------------------------------------------
> > > File "<ipython console>", line 1
> > > array([1,2,3],[1,2]], dtype = object)
> > > ^
> > > SyntaxError: invalid syntax
> > >
> > > Is the difference that list(...) and array(...) are passed as
> > > functions (lazy evaluation), but a list is just a list?
> > >
> > > Sorry to be asking all these questions, but I would like to try
> > > making the documentation be a bit of a reference. I am sure I will
> > > have more questions ;)
> > >
> > > -Travis
> > >
> > >
> > > And, voila, ragged arrays:
> > >
> > > In [9]: a = array([array([1,2,3]),array([1,2])], dtype = object)
> > >
> > > In [10]: a*2
> > > Out[10]: array([[2 4 6], [2 4]], dtype=object)
> > >
> > > In [11]: a + a
> > > Out[11]: array([[2 4 6], [2 4]], dtype=object)
> >
> > Now I remember that this was my original motivation for futzing with the
> >
> > object-array constructor in the first place. So, now you get there only
> > after an attempt to make a "rectangular" array first.
> >
> > -Travis
>
>
> So is this intentional?
>
> In [24]: a = array([[],[],[]], dtype=object)
>
> In [25]: a.shape
> Out[25]: (3, 0)
>
> In [26]: a = array([], dtype=object)
>
> In [27]: a.shape
> Out[27]: (0,)
>
> One could argue that the first array should have shape (3,)
>
And this doesn't look quite right:
In [38]: a = array([[1],[2],[3]], dtype=object)
In [39]: a.shape
Out[39]: (3, 1)
In [40]: a = array([[1],[2,3],[4,5]], dtype=object)
In [41]: a.shape
Out[41]: (3,)
Chuck
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