If I have two recarrays with the same len and column headers, the __eq__ method returns the rich comparison, which is great.  E.g.

In [20]: x = np.rec.fromrecords([(1,2,'dd',.3),(33,2,'y',2.2),(2,3,'a',21.4),(3,4,'b',33.2)],names=['A','B','C','D'])

In [21]: y = np.rec.fromrecords([(1,2,'dd',.3),(33,2,'y',2.2),(2,3,'a',21.4),(3,4,'b',33.2)],names=['A','B','C','D'])

In [22]: x == y
Out[22]: rec.array([ True,  True,  True,  True], dtype=bool)


But notice that the returned object is a recarray, not an array of bools.   Why is this, and what is the purpose of having it this way?

Similarly, if I subclass recarray, and say, in my subclass attach some attributes to x, then these attributes are attached to the result of the rich comparison.  E.g. suppose I have a subclass of recarray called "NewRecarray" to which I attach an attribute .info.  Then

x = NewRecarray(data)
y = NewRecarray(data)
z = x == y

Then z is a NewRecarray object and z.info = x.info.

Is this the expected / proper behavior?  Is there something wrong with the way I've subclassed recarray?


Thanks,
Dan

Hi there --

Is there a fast way to make a numpy ndarray from column data? 

For example, suppose I want to make an ndarray with 2 rows and 3 columns of different data types based on the following column data:

C0 = [1,2]
C1 = ['a','b']
C2 = [3.3,4.4]

I could create an empty ndarray and fill the columns one by one:

X = numpy.core.ndarray((2,), dtype='<i4,|S1,<f8')
X['f0'] = C0
X['f1'] = C1
X['f2'] = C2

The result is the same as:

X = numpy.array([(1,'a',3.3), (2,'b',4.4)], dtype='<i4,|S1,<f8')

but I would like to make X directly from the column data.

[  I know that numpy.core.records.fromarrays will produce a numpy recarray from column data, but this of course is a recarray and not a ndarray! For ex:

X = numpy.numpy.core.records.fromarrays([C0,C1,C2])  ]

Thanks for any help,

Elaine