In [1]: import numpy

In [2]: foo = numpy.array([-.1, 0, .1])

In [3]: numpy.sign(foo)
Out[3]: array([-1., 0., 1.])

In [4]: numpy.sign(foo.astype(numpy.object))
Out[4]: array([1, -1, -1], dtype=object)

The underlying data is still just a float:
In [5]: type(foo.astype(numpy.object)[0])
Out[5]: float

This result when using an array of numpy.object does not seem desirable. Either it should operate correctly on the underlying numbers or if called with invalid data (strings, etc) it should throw an exception. This way I could populate an array with heterogeneous data, say a mix of integers and floats, and still get a valid result.

I tested this on:
Mac OS 10.6.8
Python 3.4.2
numpy 1.9.2