On Sat, Feb 14, 2015 at 12:36 PM, <[email protected]> wrote: > On Sat, Feb 14, 2015 at 12:05 PM, cjw <[email protected]> wrote: > > > > On 14-Feb-15 11:35 AM, [email protected] wrote: > >> > >> On Wed, Feb 11, 2015 at 4:18 PM, Ryan Nelson <[email protected]> > >> wrote: > >>> > >>> Colin, > >>> > >>> I currently use Py3.4 and Numpy 1.9.1. However, I built a quick test > >>> conda > >>> environment with Python2.7 and Numpy 1.7.0, and I get the same: > >>> > >>> ############ > >>> Python 2.7.9 |Continuum Analytics, Inc.| (default, Dec 18 2014, > 16:57:52) > >>> [MSC v > >>> .1500 64 bit (AMD64)] > >>> Type "copyright", "credits" or "license" for more information. > >>> > >>> IPython 2.3.1 -- An enhanced Interactive Python. > >>> Anaconda is brought to you by Continuum Analytics. > >>> Please check out: http://continuum.io/thanks and https://binstar.org > >>> ? -> Introduction and overview of IPython's features. > >>> %quickref -> Quick reference. > >>> help -> Python's own help system. > >>> object? -> Details about 'object', use 'object??' for extra details. > >>> > >>> In [1]: import numpy as np > >>> > >>> In [2]: np.__version__ > >>> Out[2]: '1.7.0' > >>> > >>> In [3]: np.mat([4,'5',6]) > >>> Out[3]: > >>> matrix([['4', '5', '6']], > >>> dtype='|S1') > >>> > >>> In [4]: np.mat([4,'5',6], dtype=int) > >>> Out[4]: matrix([[4, 5, 6]]) > >>> ############### > >>> > >>> As to your comment about coordinating with Statsmodels, you should see > >>> the > >>> links in the thread that Alan posted: > >>> http://permalink.gmane.org/gmane.comp.python.numeric.general/56516 > >>> http://permalink.gmane.org/gmane.comp.python.numeric.general/56517 > >>> Josef's comments at the time seem to echo the issues the devs (and > >>> others) > >>> have with the matrix class. Maybe things have changed with Statsmodels. > >> > >> Not changed, we have a strict policy against using np.matrix. > >> > >> generic efficient versions for linear operators, kronecker or sparse > >> block matrix styly operations would be useful, but I would use array > >> semantics, similar to using dot or linalg functions on ndarrays. > >> > >> Josef > >> (long reply canceled because I'm writing too much that might only be > >> of tangential interest or has been in some of the matrix discussion > >> before.) > > > > Josef, > > > > Many thanks. I have gained the impression that there is some antipathy > to > > np.matrix, perhaps this is because, as others have suggested, the array > > doesn't provide an appropriate framework. > > It's not directly antipathy, it's cost-benefit analysis. > > np.matrix has few advantages, but makes reading and maintaining code > much more difficult. > Having to watch out for multiplication `*` is a lot of extra work. > > Checking shapes and fixing bugs with unexpected dtypes is also a lot > of work, but we have large benefits. > For a long time the policy in statsmodels was to keep pandas out of > the core of functions (i.e. out of the actual calculations) and > restrict it to inputs and returns. However, pandas is becoming more > popular and can do some things much better than plain numpy, so it is > slowly moving inside some of our core calculations. > It's still an easy source of bugs, but we do gain something. >
Any bits of Pandas that might be good for numpy/scipy to steal? <snip> Chuck
_______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
