On Mar 21, 2012, at 11:48 PM, Yan Tang wrote:
> Hi,
>
> I am really confused on the np array or record array, and cannot understand
> how it works.
>
> What I want to do is that I have a normal python two dimensional array/list:
>
> a = [['2000-01-01', 2],['2000-01-02', 3]]
>
> I want to convert it to a recarray with this dtype [('date', 'object'),
> ('count', 'int')]. I tried multiple ways and none of them works. And some
> of the tests show pretty odd behavior.
>
> This is good, and it is almost what i want:
>
> >>> import numpy as np
> >>> a = [('2000-01-01', 2), ('2000-01-02', 3)]
> >>> np.array(a, dtype=[('date', 'object'), ('count', 'int')])
> array([('2000-01-01', 2), ('2000-01-02', 3)],
> dtype=[('date', '|O8'), ('count', '<i8')])
This is the correct way to initiate the record array, or structured array, from
a Python object.
>
> Why this doesn't work?!
>
> >>> a = [['2000-01-01', 2],['2000-01-02', 3]]
> >>> np.array(a, dtype=[('date', 'object'), ('count', 'int')])
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> ValueError: tried to set void-array with object members using buffer.
>
The error here could be more instructive, but the problems is that to simplify
the np.array factory function (which is already somewhat complex) it was
decided to force records to be input as "tuples" and not as lists. You *must*
use tuples to specify records for a structured array.
> Why can this cause segmentation fault?!
>
> >>> a = [['2000-01-01', 2],['2000-01-02', 3]]
> >>> np.ndarray((len(a),), buffer=np.array(a), dtype=[('date', 'object'),
> >>> ('count', 'int')])
> Segmentation fault (And python quit!)
The np.ndarray constructor should not be used directly unless you know what you
are doing.
The np.array factory function is the standard way to create arrays. The
problem here is that you are explicitly asking NumPy to point to a particular
region of memory to use as it's data-buffer. This memory is the data buffer
of an array of "strings". The np.array factory function will try and
auto-detect the data-type of the array if you do not specify it --- which in
this case results in an array of strings. Then, with the dtype specification
you are asking it to interpret a portion of that array of strings as a pointer
to a Python object. This will cause a segmentation fault when the printing
code tries to dereference a pointer which is actually 4 characters of a string.
This should probably be checked for in the ndarray constructor. I don't think
it ever really makes sense to use an "object" dtype when you also supply the
buffer unless that buffer actually held Python object pointers in the first
place. Even in this case you could do what you wanted without calling the
constructor. So, likely a check should be made so that you can't have an
object array and also supply a buffer.
>
> Python version 2.6.5
>
> On this reference page,
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html
>
> >>> x = np.array([(1,2),(3,4)])
> >>> x
> array([[1, 2],
> [3, 4]])
> >>> np.array([[1, 2], [3, 4]])
> array([[1, 2],
> [3, 4]])
>
> Can anyone help me about this?
I'm not sure what you are asking for here? Yes, for arrays with
non-structured dtypes, numpy will treat tuples as lists.
Best regards,
-Travis
>
> Thanks.
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