Differences Between Arrays and Matrices in Numpy
robert.kern at gmail.com
Wed Jul 29 19:42:05 EDT 2009
On 2009-07-29 18:27, Colin J. Williams wrote:
> Robert Kern wrote:
>> On 2009-07-29 15:23, Nanime Puloski wrote:
>>> What are some differences between arrays and matrices using the Numpy
>>> library? When would I want to use arrays instead of matrices and vice
>> You will want to ask numpy questions on the numpy mailing list:
>> An overview of how the matrix subclass differs from ndarray, see the
>> Basically, I suggest that you just use regular arrays always. There is
>> a syntactical convenience to matrix objects, but it does cause
>> incompatibilities with the majority of code that is written for
>> regular arrays. The convenience is usually not worth the cost.
> Numpy's arrays can have any dimensionality, whereas
> typically have two. A single column can represent a vector or a single
> row can represent a transposed vector.
> Does the additional cost arise because the commonly used procedures are
> accessed through numpy's array?
Most functions are written to expect that its inputs behave like ndarrays; e.g.
a*b is elementwise multiplication rather than matrix multiplication. When you
use the matrix subclass, you are basically confining yourself to a smallish
ghetto of functions that knows how to deal with matrix semantics. That's a huge
cost compared to the relatively small syntactic cost of having to write dot(a,b)
instead of (a*b).
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
More information about the Python-list