On 3/20/2023 11:21 AM, Edmondo Giovannozzi wrote:
def sum1():
s = 0
for i in range(1000000):
s += i
return s
def sum2():
return sum(range(1000000))
Here you already have the numbers you want to add.
Actually using numpy you'll be much faster in this case:
§ import numpy as np
§ def sum3():
§ return np.arange(1_000_000, dtype=np.int64).sum()
On my computer sum1 takes 44 ms, while the numpy version just 2.6 ms
One problem is that sum2 gives the wrong result. This is why I used np.arange
with dtype=np.int64.
On my computer they all give the same result.
Python 3.10.9, PyQt version 6.4.1
Windows 10 AMD64 (build 10.0.19044) SP0
Processor: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz, 1690 Mhz, 4
Core(s), 8 Logical Processor(s)
sum2 evidently doesn't uses the python "big integers" e restrict the result to
32 bits.
What about your system? Let's see if we can figure the reason for the
difference.
--
https://mail.python.org/mailman/listinfo/python-list
