[SciPy-User] getting started with ndarray

[David Goldsmith]

It's rare that one wants to "manually" create arrays using np.array,
however.  Typically, the arrays used in numerics are "standard" in some way
(e.g., the array corresponding to the n x n identity matrix, obtained from
np.eye(n)) and thus numpy has many, _many_ functions which provide these
various standard arrays for you.  Even in the case of an array created from
data in a file, one doesn't read in the data and then pass it as an argument
to np.array - there's a numpy function which reads the file-stored data
directly into an array for you.

DG

On Wed, May 5, 2010 at 7:24 PM, Keith Goodman <kwgoodman at gmail.com> wrote:

> On Wed, May 5, 2010 at 7:18 PM, Victor Eijkhout
> <eijkhout at tacc.utexas.edu> wrote:
> > I found the "guide to numpy" book, but I can't figure out how to
> > create a multi-dimensional array. Is there a short tutorial? Or can
> > someone give me a short example program with the most relevant features?
>
> Here's one way to create arrays:
>
> One dimensional:
>
> >> import numpy as np
> >> x1 = np.array([1, 2, 3])
> >> x1.ndim
>   1
> >> x1
>   array([1, 2, 3])
>
> Two dimensional:
>
> >> x2 = np.array([[1, 2], [3, 4]])
> >> x2.ndim
>   2
> >> x2
> array([[1, 2],
>       [3, 4]])
>
> Three dimensional:
>
> >> x3 = np.random.rand(2, 3, 4)
> >> x3.ndim
>   3
> >> x3.shape
>   (2, 3, 4)
> >> x3
> array([[[ 0.85887601,  0.2988635 ,  0.93155938,  0.48419988],
>        [ 0.677853  ,  0.67478433,  0.7065251 ,  0.49045808],
>        [ 0.87160361,  0.55503905,  0.36378423,  0.39314846]],
>
>       [[ 0.80761194,  0.54838378,  0.80576339,  0.08248982],
>        [ 0.16729305,  0.16320019,  0.5628961 ,  0.77325458],
>        [ 0.7073337 ,  0.08927084,  0.89050264,  0.54985488]]])
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-- 
Mathematician: noun, someone who disavows certainty when their uncertainty
set is non-empty, even if that set has measure zero.
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