I think we can do better if we use fromiter and supply the size as count:
np.fromiter ((u**2 for u in range(10)), dtype=float, count=10)
On Fri, Jun 23, 2023 at 10:39 AM Joao S. O. Bueno
wrote:
> 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 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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