如果单个整数具有 pythonnative int 类型，则将单个整数更快地添加到 numpy 数组

我将一个整数添加到包含numpy.int640个元素的整数数组中.当我第一次将单个整数从numpy.int64转换为python本机int时，速度加快了25%.

为什么？作为一般的经验法则，我是否应该将单个数字转换为本机python格式，以用于具有大约这个大小的数组的单个数字到数组操作？

注:可能与我之前的问题Conjugating a complex number much faster if number has python-native complex type有关.

import numpy as np

nnu = 10418
nnu_use = 5210
a = np.random.randint(nnu,size=1000)
b = np.random.randint(nnu_use,size=1)[0]

%timeit a + b                            # --> 3.9 µs ± 19.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit a + int(b)                       # --> 2.87 µs ± 8.07 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

请注意，对于标量到标量的操作，速度可能会非常快(factor 50)，如下所示:

np.random.seed(100)

a = (np.random.rand(1))[0]
a_native = float(a)
b = complex(np.random.rand(1)+1j*np.random.rand(1))
c = (np.random.rand(1)+1j*np.random.rand(1))[0]
c_native = complex(c)

%timeit a * (b - b.conjugate() * c)                # 6.48 µs ± 49.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit a_native * (b - b.conjugate() * c_native)  # 283 ns ± 7.78 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
%timeit a * b                                      # 5.07 µs ± 17.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit a_native * b                               # 94.5 ns ± 0.868 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

Update:是不是最新的numpy版本修复了速度差异？numpy 1.23的发行说明提到标量运算现在快得多，请参见https://numpy.org/devdocs/release/1.23.0-notes.html#performance-improvements-and-changes和https://github.com/numpy/numpy/pull/21188.我用python 3.7.6, numpy 1.21.2.