[Numpy-discussion] Fwd: Named dtype array: Difference between a[0]['name'] and a['name'][0]?

diehose 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 
respond immediately.  

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']
[0] = (1,2) is equivalent to b = a['tuple']; b[0] = (1,2).   In like manner, 
a[0]['tuple'] = (1,2) is equivalent to b = a[0]; 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 [0] row of this object via 

a['tuple'][0] = (1,2)

works just fine and does what you would expect. 

On the other hand, when you type: 

b = a[0]

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 
should work. 


On May 21, 2012, at 1:50 PM, bmu wrote:

dear all,

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[0]['name'] and a['name'][0]?

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'][0])  # ndarray
type(a[0]['tuple'])  # ndarray

a['tuple'][0] = (1,2)  # ok
a[0]['tuple'] = (1,2)  # ValueError: shape-mismatch on array construction

Could somebody explain this behaviour (either in this mailing list or on 


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