> On 5/28/2011 3:40 PM, Robert wrote:
>> (myarray in mylist) turns into mylist.__contains__(myarray).
>> Only the list object is ever checked for this method. There is no
>> paired method myarray.__rcontains__(mylist) so there is nothing that
>> numpy can override to make this operation do anything different from
>> what lists normally do,
however, numpy arrays should be able to override "in" be defining their
own.__contains__ method, so you could do:
something in an_array
and get a useful, vectorized result.
So I thought I'd see what currently happens when I try that:
In [24]: a
Out[24]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
In [25]: 3 in a
Out[25]: True
So the simple case works just like a list. But what If I want what the
OP wants:
In [26]: b
Out[26]: array([3, 6, 4])
In [27]: b in a
Out[27]: False
OK, so the full b array is not in a, and it doesn't "vectorize" it,
either. But:
In [29]: a
Out[29]:
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
In [30]: b in a
Out[30]: True
HUH?
I'm not sure by what definition we would say that b is contained in a.
but maybe..
In [41]: b
Out[41]: array([ 4, 2, 345])
In [42]: b in a
Out[42]: False
so it's "are all of the elements in b in a somewhere?" but only for 2-d
arrays?
So what does it mean?
The docstring is not helpful:
In [58]: np.ndarray.__contains__?
Type: wrapper_descriptor
Base Class: <type 'wrapper_descriptor'>
String Form: <slot wrapper '__contains__' of 'numpy.ndarray' objects>
Namespace: Interactive
Docstring:
x.__contains__(y) <==> y in x
If nothing useful, maybe it could provide a vectorized version of "in"
for this sort of use case.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
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chris.bar...@noaa.gov
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