[Numpy-discussion] Problem with concatenate and object arrays

Charles R Harris charlesr.harris at gmail.com
Thu Sep 7 14:22:01 CDT 2006


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,)

Chuck
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