No, because the array of 100 elements will only have the overhead once,
while the 100 arrays will each have the overhead repeated.
Think about the overhead like a book cover on a book. It takes additional
space, but provides storage for the book, information to help you find it,
etc. Each book on
On Sat, Mar 13, 2021 at 4:18 PM wrote:
> So is it right that 100 arrays of one element is smaller than one array
> with size of 100 elements?
>
No, typically the opposite is true.
--
Robert Kern
___
NumPy-Discussion mailing list
NumPy-Discussion@pyth
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" :Ideally float64 uses 64 bits for each number while float16 uses 16 bits. 64/16=4. However, there is some additional overhead. This overhead makes up a large portion of sma
On Sat, Mar 13, 2021 at 4:02 PM wrote:
> Dear colleagues!
>
> Size of np.float16(1) is 26
> Size of np.float64(1) is 32
> 32 / 26 = 1.23
>
Note that `sys.getsizeof()` is returning the size of the given Python
object in bytes. `np.float16(1)` and `np.float64(1)` are so-called "numpy
scalar object
Ideally float64 uses 64 bits for each number while float16 uses 16 bits.
64/16=4. However, there is some additional overhead. This overhead makes
up a large portion of small arrays, but becomes negligible as the array
gets bigger.
On Sat, Mar 13, 2021, 16:01 wrote:
> Dear colleagues!
>
> Size
Dear colleagues! Size of np.float16(1) is 26Size of np.float64(1) is 3232 / 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)
