On Fri, Aug 29, 2014 at 8:10 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> Consider the following:
>
> a = np.array([(1, 'a'), (2, 'b'), (3, 'c')], dtype=[('foo', 'i'), ('bar',
> 'a1')])
> b = np.append(a, (4, 'd'))
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/home/ben/miniconda/lib/python2.7/site-packages/numpy/lib/function_base.py",
> line 3555, in append
> return concatenate((arr, values), axis=axis)
> TypeError: invalid type promotion
> b = np.insert(a, 4, (4, 'd'))
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/home/ben/miniconda/lib/python2.7/site-packages/numpy/lib/function_base.py",
> line 3464, in insert
> new[slobj] = values
> ValueError: could not convert string to float: d
>
> In my original code snippet I was developing which has a more involved
> dtype, I actually got a different exception:
> b = np.append(a, c)
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/home/ben/miniconda/lib/python2.7/site-packages/numpy/lib/function_base.py",
> line 3553, in append
> values = ravel(values)
> File
> "/home/ben/miniconda/lib/python2.7/site-packages/numpy/core/fromnumeric.py",
> line 1367, in ravel
> return asarray(a).ravel(order)
> File
> "/home/ben/miniconda/lib/python2.7/site-packages/numpy/core/numeric.py",
> line 460, in asarray
> return array(a, dtype, copy=False, order=order)
> ValueError: setting an array element with a sequence.
>
> Luckily, this works as a work-around:
> >>> b = np.append(a, np.array([(4, 'd')], dtype=a.dtype))
> >>> b
> array([(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd')],
> dtype=[('foo', 'i'), ('bar', 'S1')])
>
> The same happens whether I enclose the value with square bracket or not. I
> suspect that this array type just wasn't considered when its checking logic
> was developed. This is with 1.8.2 from miniconda. Should we consider this a
> bug or are structured arrays just not expected to be modified like this?
>
>
Could be one of many bug reports related to assignment to structured types.
Can you try using `x`?
In [25]: x = array([(4, 'd')], dt)[0]
In [26]: type(x)
Out[26]: numpy.void
In [27]: x
Out[27]: (4, 'd')
Chuck
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