Access by the interpreter prevents array resizing.
Yes, one can use the function, in place of the method but this appears
to require copying the whole array.
If one sets b= a, then that reference can be deleted with del b.
Is there any similar technique for the interpreter?
Colin W.
Python
Christopher Barker wrote:
Colin J. Williams wrote:
Access by the interpreter prevents array resizing.
yup -- resize is really fragile for that reason. It really should be
used quite sparingly.
Personally, I think it should probably only be used when wrapped with
a higher level layer.
Colin J. Williams wrote:
Thanks for this. My aim is to extract a row of data from a line in a
file and append it to an array. The number of columns is fixed but, at
the start, the number of rows is unknown.
That is exactly the kind of use-case I had in mind.
In fact, you can use it now if
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of fixed dimension.
Therefore, is not clear to me why in numpy the size of an
Alice Invernizzi wrote:
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of fixed dimension.
Therefore, is not clear to
V. Armando Solé wrote:
Sorry, there was a bug in the sent code. It should be:
import numpy
a=numpy.arange(100.)
a.shape = 10, 10
b = a * 1 # just to get a copy
b.shape = 5, 2, 5, 2
b = (b.sum(axis=3)).sum(axis=1)
In that way, on b I have a binned image of a.
Alice Invernizzi wrote:
Therefore, is not clear to me why in numpy the size of an array can be
changed (either with the 'returning-value' resize() function either with
the 'in-place' array method resize()).
Would you please be so kind to give some explanation for the existence
of resize
On Thu, Sep 24, 2009 at 09:58, Alice Invernizzi inverni...@cilea.it wrote:
Dear all,
I have an Hamletic doubt concerning the numpy array data type.
A general learned rule concerning the array usage in other high-level
programming languages is that array data-type are homogeneous datasets
of
Robert Kern skrev:
While this description is basically true of numpy arrays, I would
caution you that every language has a different lexicon, and the same
word can mean very different things in each. For example, Python lists
are *not* linked lists; they are like C++'s std::vectors with a
On Thu, Sep 24, 2009 at 17:32, Sturla Molden stu...@molden.no wrote:
Robert Kern skrev:
While this description is basically true of numpy arrays, I would
caution you that every language has a different lexicon, and the same
word can mean very different things in each. For example, Python lists
Robert Kern skrev:
collections.deque() is a linked list of 64-item chunks.
Thanks for that useful information. :-) But it would not help much for a
binary tree...
Since we are on the NumPy list... One could image making linked lists
using NumPy arrays with dtype=object. They are storage
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