[Numpy-discussion] Making cdecimal.Decimal a native numpy type

Sun Jul 22 08:08:35 CDT 2012

```On Sun, Jul 22, 2012 at 8:54 AM, Dr.Leo <fhaxbox66@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
```