On Sun, Jul 22, 2012 at 8:54 AM, Dr.Leo <fhaxbo...@googlemail.com> wrote: > Hi, > > I am a seasoned numpy/pandas user mainly interested in financial > applications. These and other applications would greatly benefit from a > decimal data type with flexible rounding rules, precision etc. > > Yes, there is cdecimal, the traditional decimal module from the Python > stdlib rewritten in C, > > - http://www.bytereef.org/mpdecimal/index.html - > > which has become part of the stdlib from Python 3.3. > > However, it appears that cdecimal cannot be meaningfully used with numpy > (see the benchmark below). Squaring an n=10000 ndarray is 1500 times > faster with float64 than with a dtype=object ndarray based on > cdecimal.Decimal, and even simple operations fail in the first place. > > I am not deeply enough into ufuncs etc. to judge if some of these > problems can be avoided with a few lines of Python code. However, my > impression is that ultimately we would all benefit from cdecimal.Decimal > becoming a native numpy type. Put bluntly, cdecimal is a great tool. But > it is not yet where we most need it. > > The author of cdecimal, Stefan Krah, would probably have a great deal of > the skillset needed to successfully take such a project forward. He > happens to have also written the new memoryview implementation of Python > 3.3. And from recent correspondence I understand he might be willing to > get involved in an effort to marry numpy and cdecimal. > > The main question is if such project would fit into what core developers > see as the future of numpy. > > Regards > > Leo > > And here is the benchmark: > > In [1]: from numpy import * > > In [2]: from cdecimal import Decimal > > In [3]: r=random.rand(10000) > > In [4]: d=ndarray(10000, dtype=Decimal) > > In [5]: d.dtype > Out[5]: dtype('object') > > In [6]: r.dtype > Out[6]: dtype('float64') > > In [7]: for i in range(10000): d[i] = Decimal(r[i]) > > In [8]: %timeit r**2 > 100000 loops, best of 3: 14.7 us per loop > > In [9]: %timeit d**2 > 10 loops, best of 3: 21.2 ms per loop > > In [10]: r.var() > Out[10]: 0.082478142261349557 > > In [11]: d.var() > --------------------------------------------------------------------------- > TypeError Traceback (most recent call last) > C:\<ipython-input > -11-bf09d28e33ab> in <module>() > ----> 1 d.var() > > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
The numpy-dtypes repository (https://github.com/numpy/numpy-dtypes) has been created recently as a repository for extension dtypes for numpy. This would be the natural place for a decimal dtype. Currently there is a rational and quaternion type, and documentation on how to implement a new dtype. This project is at an early stage and moving somewhat slowly, so contributions and input would be quite welcome. - Tom _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
