Ah, got it. Thanks, Chris!
I thought recarray can be only one-dimensional (like tables with named columns).

Maybe it's better to ask directly what I was looking for: 
something that works like a table with named columns (but no labelling for rows), and keeps data (of different dtypes) in a column-by-column way (and this is numpy, not pandas). 

Is there such a magic thing?

Alex.


22 февр. 2017 г., в 2:10, Chris Barker <chris.barker@noaa.gov> написал(а):



On Tue, Feb 21, 2017 at 3:05 PM, Alex Rogozhnikov <alex.rogozhnikov@yandex.ru> wrote:
a question about numpy.recarray:
There is a parameter order in constructor https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.recarray.html, but it seems to have no effect:
x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[1000], order='C')

you are creating a 1D array here -- there is no difference between Fortran and C order for a 1D array. For 2D:

In [2]: x = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10], order='C')


In [3]: x.strides
Out[3]: (160, 16)


In [4]: y = numpy.recarray(dtype=[('a', int), ('b', float)], shape=[10,10], order='F')


In [5]: y.strides
Out[5]: (16, 160)

note the easier way to get the strides, too :-)

-CHB



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