[Numpy-discussion] Creating a 1-d structured array
josef.pktd at gmail.com
josef.pktd at gmail.com
Fri May 13 17:33:47 EDT 2011
On Fri, May 13, 2011 at 5:03 PM, Bruce Southey <bsouthey at gmail.com> wrote:
> On 05/13/2011 03:04 PM, josef.pktd at gmail.com wrote:
>> On Fri, May 13, 2011 at 3:11 PM, Derek Homeier
>> <derek at astro.physik.uni-goettingen.de> wrote:
>>> Hi,
>>>
>>> just a comment since I first thought the solution below might not be
>>> what Bruce
>>> was looking for, but having realised it's probably what he's been
>>> asking for...
>>>
>>> On 13 May 2011, at 17:20, josef.pktd at gmail.com wrote:
>>>
>>>> On Fri, May 13, 2011 at 10:58 AM, Bruce Southey<bsouthey at gmail.com>
>>>> wrote:
>>>>> Hi,
>>>>> How do you create a 'single' structured array using np.array()?
>>>>> Basically I am attempting to do something like this that does not
>>>>> work:
>>>>> a=np.array([1,2, 3,4, 5,6], dtype=np.dtype([('foo', int)]))
>>>>>
>>>>> I realize that this is essentially redundant as if A is an 1-d array
>>>>> then a structured array with a named field 'foo' is the same thing
>>>>> - A
>>>>> would be A['foo'], just shorter.
>>> ...
>>>> Using a view works, (and direct assignment of dtype)
>>>>
>>>>>>> a=np.array([1,2, 3,4, 5,6]).view(([('foo', int)]))
>>>>>>> a
>>>> array([(1,), (2,), (3,), (4,), (5,), (6,)],
>>>> dtype=[('foo', '<i4')])
>>>>>>> b = a.copy()
>>>>>>> b
>>>> array([(1,), (2,), (3,), (4,), (5,), (6,)],
>>>> dtype=[('foo', '<i4')])
>>>>
>>>>
>>>>>>> a1 = np.array([1,2, 3,4, 5,6]).astype([('foo', int)])
>>>> Traceback (most recent call last):
>>>> File "<pyshell#36>", line 1, in<module>
>>>> a1 = np.array([1,2, 3,4, 5,6]).astype([('foo', int)])
>>>> TypeError: expected a readable buffer object
>>>>
>>>>>>> a1 = np.array([1,2, 3,4, 5,6])
>>>>>>> a1.dtype
>>>> dtype('int32')
>>>>>>> a1.dtype = np.dtype([('foo', int)])
>>>>>>> a1
>>>> array([(1,), (2,), (3,), (4,), (5,), (6,)],
>>>> dtype=[('foo', '<i4')])
>>> This is a 1-d structured array, yet a is in fact not the same as
>>> a['foo']:
>>>
>>> >>> a['foo']
>>> array([ 1, 2, 3, 4, 5, 6])
>>> >>> a['foo'].shape
>>> (6,)
>>> >>> a.shape
>>> (6,)
>>> >>> a['foo']+np.arange(6)
>>> array([ 1, 3, 5, 7, 9, 11])
>>> >>> a+np.arange(6)
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in<module>
>>> TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and
>>> 'numpy.ndarray'
>> funny things with subclasses, I saw this also in an error message in
>> another package with a subclass of ndarray,
>> should read: 'this ndarray-(sub)class' and 'that ndarray-(sub)class'
>>
>> for some messages it's just getting used to the "coding"
>>
>> "TypeError: expected a readable buffer object" translates into "I
>> cannot interpret the data as the desired array"
>> e.g. list of list or list of tuples
>>
>>
>>> >>> a[0]+1
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in<module>
>>> TypeError: unsupported operand type(s) for +: 'numpy.void' and 'int'
>>>
>>> Thus I am wondering why broadcasting should not be possible in this
>>> case,
>> Even a 1 column table is still a table (or a list of records), and a 1
>> item row is still a row.
>>
>> Josef
>>
>>> and if it really isn't, the first error message certainly is not very
>>> helpful... (maybe
>>> inevitably though, since type() of a structured array is the same as
>>> for a "plain"
>>> array (unlike for a record array).
>>>
>>> Cheers,
>>> Derek
>>>
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> Thanks for the replies, especially the dtype example Josef!
>
> So this should be reported as a bug?
It's by design, a structured array takes a list of tuples and is an
array of records, -- and tuples and records are not lists or arrays
and don't behave that way.
I have no opinion about whether it should be considered a bug, I just
learned to live with it.
Josef
>
> Bruce
>
>
>
>
>
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