Python deepcopy（）vs只是在运行时速度方面启动一个numpy数组？

Question

我很好奇

elevation_arr = numpy.zeros([900, 1600], numpy.float32)
climate_arr = copy.deepcopy(elevation_arr)
rainfall_arr = copy.deepcopy(elevation_arr)

比执行更快或更慢

elevation_arr = numpy.zeros([900, 1600], numpy.float32)
climate_arr = numpy.zeros([900, 1600], numpy.float32)
rainfall_arr = numpy.zeros([900, 1600], numpy.float32)

Answer 1

numpy_zeros对于较小的阵列执行稍微好一些，对于较大的阵列则更好，如下所示

import copy
import numpy as np

def deep_copy():
    elevation_arr = np.zeros([900, 1600], np.float32)
    climate_arr = copy.deepcopy(elevation_arr)
    rainfall_arr = copy.deepcopy(elevation_arr)
    return 

def numpy_zeros():
    elevation_arr = np.zeros([900, 1600], np.float32)
    climate_arr = np.zeros([900, 1600], np.float32)
    rainfall_arr = np.zeros([900, 1600], np.float32)
    return

%timeit deep_copy()
# 4.13 ms ± 585 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit numpy_zeros()
# 3.01 ms ± 195 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

对于10000 x 10000阵列，以下是时序。 numpy_zeros表现优异

%timeit deep_copy()
# 569 ms ± 50 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit numpy_zeros()
# 15.6 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)