On Mon, Sep 7, 2009 at 6:36 PM, Skipper Seabold<jsseab...@gmail.com> wrote:
> Hello all,
>
> I ran into a problem with some of my older code (since figured out the
> user error). However, in trying to give a simple example that
> replicates the problem I was having, I ran into this.
>
> In [19]: a = np.array((1.))
>
> In [20]: a
> Out[20]: array(1.0)
>
> # the dtype is 'float64'
>
> In [21]: a.dtype='<i8'
The way I understand it is:
Here you are telling numpy to interpret the existing memory/data in a
different way, which might make sense or not depending on the types,
e.g. I also used this to switch between structured arrays and regular
arrays with compatible memory. However it does not convert the data.
If you want to convert the data to a different type, numpy needs to
create a new array, e.g. with astype
>>> a = np.array((1.))
>>> b = a.astype('<i8')
>>> b
array(1L, dtype=int64)
Josef
>
> In [22]: a
> Out[22]: array(4607182418800017408)
>
> I've seen some recent threads about handling changes in types, but I
> didn't follow closely, so forgive me if I'm missing something that is
> known. In general, is it just a bad idea to touch the dtype like
> this?
>
> Best,
> Skipper
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