Dear all I've read on an old status blog (http://morepypy.blogspot.ch/2012/04/numpy-on-pypy-progress-report.html):
> We merged record dtypes support. The only missing dtypes left are > complex (important), datetime (less important) and object (which will > probably never be implemented because it makes very little sense and is a > mess with moving GCs). I use object arrays extensively in CPython, because the numpy API is very convenient and object array operations (such as indexing with an array) can be much faster than equivalent list comprehensions. I'm measured a particular indexing operation to be 10x faster than a list comprehension in CPython. In PyPy, the list comprehension has roughly the same speed as CPython's numpy indexing, so there's nothing to be gained, *except for code portability*. So from a portability point of view, object arrays do make a lot of sense, even if the implementation may internally rely on lists, because PyPy optimizes their speed disadvantage away. Best regards Martin Martin Gfeller ___________________________________________________________________________ Senior Manager Head of Quantax [email protected]<mailto:[email protected]> Xing<https://www.xing.com/net/Quantax>
_______________________________________________ pypy-dev mailing list [email protected] http://mail.python.org/mailman/listinfo/pypy-dev
