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()