[Numpy-discussion] size of arrays

Todd toddrjen at gmail.com
Sat Mar 13 18:27:23 EST 2021

```No, because the array of 100 elements will only have the overhead once,
while the 100 arrays will each have the overhead repeated.

space, but provides storage for the book, information to help you find it,
etc. Each book only needs one cover. So a single 100 page book only needs
one cover, while a hundred 1 page books needs 100 covers. Also, as the book
gets more pages the cover takes a smaller portion of the total size of the
book.

On Sat, Mar 13, 2021, 16:17 <klark--kent at yandex.ru> wrote:

> So is it right that 100 arrays of one element is smaller than one array
> with size of 100 elements?
>
> 14.03.2021, 00:06, "Todd" <toddrjen at gmail.com>:
>
> Ideally float64 uses 64 bits for each number while float16 uses 16 bits.
> up a large portion of small arrays, but becomes negligible as the array
> gets bigger.
>
> On Sat, Mar 13, 2021, 16:01 <klark--kent at yandex.ru> wrote:
>
> Dear colleagues!
>
> Size of np.float16(1) is 26
> Size of np.float64(1) is 32
> 32 / 26 = 1.23
>
> Since memory is limited I have a question after this code:
>
>    import numpy as np
>    import sys
>
>    a1 = np.ones(1, dtype='float16')
>    b1 = np.ones(1, dtype='float64')
>    div_1 = sys.getsizeof(b1) / sys.getsizeof(a1)
>    # div_1 = 1.06
>
>    a2 = np.ones(10, dtype='float16')
>    b2 = np.ones(10, dtype='float64')
>    div_2 = sys.getsizeof(b2) / sys.getsizeof(a2)
>    # div_2 = 1.51
>
>    a3 = np.ones(100, dtype='float16')
>    b3 = np.ones(100, dtype='float64')
>    div_3 = sys.getsizeof(b3) / sys.getsizeof(a3)
>    # div_3 = 3.0
> Size of np.float64 numpy arrays is four times more than for np.float16.
> Is it possible to minimize the difference close to 1.23?
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