[Numpy-discussion] Fwd: Named dtype array: Difference between a['name'] and a['name']?
diehose at freenet.de
Thu May 24 17:07:50 EDT 2012
thanks a lot. I updated the question on stackoverflow and opened a ticket
Am Montag, 21. Mai 2012, 15:37:36 schrieb Travis Oliphant:
This is the right place to ask, it's just that it can take time to get an
answer because people who might know the answer may not have the time to
The short answer is that this is not really a "normal" bug, but it could be
considered a "design" bug (although the issues may not be straightforward to
resolve). What that means is that it may not be changed in the short term
--- and you should just use the first spelling.
Structured arrays can be a confusing area of NumPy for several of reasons.
You've constructed an example that touches on several of them. You have a
data-type that is a "structure" array with one member ("tuple"). That member
contains a 2-vector of integers.
First of all, it is important to remember that with Python, doing a['tuple']
 = (1,2) is equivalent to b = a['tuple']; b = (1,2). In like manner,
a['tuple'] = (1,2) is equivalent to b = a; b['tuple'] = (1,2).
To understand the behavior, we need to dissect both code paths and what
happens. You built a (3,) array of those elements in 'a'. When you write b
= a['tuple'] you should probably be getting a (3,) array of (2,)-integers, but
as there is currently no formal dtype support for (n,)-integers as a general
dtype in NumPy, you get back a (3,2) array of integers which is the closest
thing that NumPy can give you. Setting the  row of this object via
a['tuple'] = (1,2)
works just fine and does what you would expect.
On the other hand, when you type:
b = a
you are getting back an array-scalar which is a particularly interesting kind
of array scalar that can hold records. This new object is formally of type
numpy.void and it holds a "scalar representation" of anything that fits under
the "VOID" basic dtype.
For some reason:
b['tuple'] = [1,2]
is not working. On my system I'm getting a different error: TypeError: object
of type 'int' has no len()
I think this should be filed as a bug on the issue tracker which is for the
time being here: http://projects.scipy.org/numpy
The problem is ultimately the void->copyswap function being called in
voidtype_setfields if someone wants to investigate. I think this behavior
On May 21, 2012, at 1:50 PM, bmu wrote:
can anybody tell me, why nobody is answering this question? is this the wrong
place to ask? or does nobody know an answer?
From: bmu <diehose at freenet.de>
Subject: Named dtype array: Difference between a['name'] and a['name']?
Date: May 20, 2012 6:45:03 AM CDT
To: numpy-discussion at scipy.org
I came acroos a question on stackoverflow (http://stackoverflow.com/q/9470604)
and I am wondering if this is a bug
import numpy as np
dt = np.dtype([('tuple', (int, 2))])
a = np.zeros(3, dt)
type(a['tuple']) # ndarray
type(a['tuple']) # ndarray
a['tuple'] = (1,2) # ok
a['tuple'] = (1,2) # ValueError: shape-mismatch on array construction
Could somebody explain this behaviour (either in this mailing list or on
NumPy-Discussion mailing list
NumPy-Discussion at scipy.org
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