为了跟进我的评论，这里有3个选项（numpy和C和C ++标准库选项）

from libcpp.algorithm cimport sort
from libc.stdlib cimport qsort

import numpy as np

def sort_numpy(double[:] a, kind):
    np.asarray(a).sort(kind=kind)

# needs to be compiled with C++        
def sort_cpp(double[::1] a):
    # a must be c continuous (enforced with [::1])
    sort(&a[0], (&a[0]) + a.shape[0])

# The C version requires a comparator function
# which is a little slower since it requires calling function pointers
# and passing pointers rather than numbers
cdef int cmp_func(const void* a, const void* b) nogil:
    cdef double a_v = (<double*>a)[0]
    cdef double b_v = (<double*>b)[0]
    if a_v < b_v:
        return -1
    elif a_v == b_v:
        return 0
    else:
        return 1

def sort_c(double[:] a):
    # a needn't be C continuous because strides helps
    qsort(&a[0], a.shape[0], a.strides[0], &cmp_func)

结果将取决于您使用的C / C ++标准库，因此不要对我的结果了解太多。对于1000个长数组（排序5000次），我得到：

np quick:  0.11296762199890509
np merge:  0.20624926299933577
np heap:  0.2944786230000318
c++:  0.12071316699984891
c:  0.33728832399901876

即numpy版本最快。对于100个长数组，我得到

np quick:  0.022608489000049303
np merge:  0.023513408999860985
np heap:  0.024136934998750803
c++:  0.008449130998997134
c:  0.01909676999821386

即，如果您要对许多小数组进行排序，则调用numpy sort的开销很大，您应该使用C
++（或可能使用C）。如果要对大型数组进行排序，您可能会发现很难击败numpy。