If you use Python's own arrays and generator expressions instead of list
comprehension (by just dropping the `[ ]`s),
you will get each number converted to the target type in memory as soon as
it is calculated. (It will be a full
Python float/int instance during the calculation itself, though).
This won't work with the usual numpy array constructors as those need the
array size beforehand - but if you know
the size beforehand, there is probably a numpy constructor with no need to
go through Python arrays first (but I don't know one by heart)
```
import numpy as np
import array
data = np.array(array.array("b", (int(127 * cos(i/100)) for i in
range(628))), dtype="int8", copy=False)
```
On Fri, Jun 23, 2023 at 10:53 AM Neal Becker <ndbeck...@gmail.com> wrote:
> One item I admire from Julia and miss in python/numpy,
>
> I often use the power of python list comprehension to process data. This
> data often needs to be converted to numpy for other operations, for
> example
> fancy indexing. The fact that operations using comprehensions (which
> produce lists) and operations on numpy arrays use different incompatible
> data structures requires conversions between lists and numpy arrays.
> Comprehensions in Julia produce arrays directly (I believe), removing the
> need for conversions.
>
> I don't see any easy way to improve this. Any ideas?
>
> Thanks,
> Neal
>
> --
> *Those who don't understand recursion are doomed to repeat it*
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