使用Python的方式是：
x = [None] * numElements
或您希望预先弹出的任何默认值，例如
bottles = [Beer()] * 99
sea = [Fish()] * many
vegetarianPizzas = [None] * peopleOrderingPizzaNotQuiche
[编辑：买者自负的[Beer()] * 99语法创建一个 Beer，然后填充与99次的引用数组相同的单个实例]
Python的默认方法可能非常有效，尽管随着您增加元素数量，效率会下降。
比较
import time
class Timer(object):
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
end = time.time()
secs = end - self.start
msecs = secs * 1000 # millisecs
print('%fms' % msecs)
Elements = 100000
Iterations = 144
print('Elements: %d, Iterations: %d' % (Elements, Iterations))
def doAppend():
result = []
i = 0
while i < Elements:
result.append(i)
i += 1
def doAllocate():
result = [None] * Elements
i = 0
while i < Elements:
result[i] = i
i += 1
def doGenerator():
return list(i for i in range(Elements))
def test(name, fn):
print("%s: " % name, end="")
with Timer() as t:
x = 0
while x < Iterations:
fn()
x += 1
test('doAppend', doAppend)
test('doAllocate', doAllocate)
test('doGenerator', doGenerator)
与
#include <vector>
typedef std::vector<unsigned int> Vec;
static const unsigned int Elements = 100000;
static const unsigned int Iterations = 144;
void doAppend()
{
Vec v;
for (unsigned int i = 0; i < Elements; ++i) {
v.push_back(i);
}
}
void doReserve()
{
Vec v;
v.reserve(Elements);
for (unsigned int i = 0; i < Elements; ++i) {
v.push_back(i);
}
}
void doAllocate()
{
Vec v;
v.resize(Elements);
for (unsigned int i = 0; i < Elements; ++i) {
v[i] = i;
}
}
#include <iostream>
#include <chrono>
using namespace std;
void test(const char* name, void(*fn)(void))
{
cout << name << ": ";
auto start = chrono::high_resolution_clock::now();
for (unsigned int i = 0; i < Iterations; ++i) {
fn();
}
auto end = chrono::high_resolution_clock::now();
auto elapsed = end - start;
cout << chrono::duration<double, milli>(elapsed).count() << "ms\n";
}
int main()
{
cout << "Elements: " << Elements << ", Iterations: " << Iterations << '\n';
test("doAppend", doAppend);
test("doReserve", doReserve);
test("doAllocate", doAllocate);
}
在我的Windows 7 i7上，64位Python提供
Elements: 100000, Iterations: 144
doAppend: 3587.204933ms
doAllocate: 2701.154947ms
doGenerator: 1721.098185ms
尽管C ++提供（使用MSVC构建，64位，启用了优化）
Elements: 100000, Iterations: 144
doAppend: 74.0042ms
doReserve: 27.0015ms
doAllocate: 5.0003ms
C ++调试版本产生：
Elements: 100000, Iterations: 144
doAppend: 2166.12ms
doReserve: 2082.12ms
doAllocate: 273.016ms
这里的要点是，使用Python可以将性能提高7-8％，并且如果您认为自己正在编写高性能的应用程序（或者正在编写用于Web服务的内容或其他内容），那么这是不容小at的，但是您可能需要重新考虑您选择的语言。
另外，这里的Python代码并不是真正的Python代码。在此切换到真正的Pythonesque代码可提供更好的性能：
import time
class Timer(object):
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
end = time.time()
secs = end - self.start
msecs = secs * 1000 # millisecs
print('%fms' % msecs)
Elements = 100000
Iterations = 144
print('Elements: %d, Iterations: %d' % (Elements, Iterations))
def doAppend():
for x in range(Iterations):
result = []
for i in range(Elements):
result.append(i)
def doAllocate():
for x in range(Iterations):
result = [None] * Elements
for i in range(Elements):
result[i] = i
def doGenerator():
for x in range(Iterations):
result = list(i for i in range(Elements))
def test(name, fn):
print("%s: " % name, end="")
with Timer() as t:
fn()
test('doAppend', doAppend)
test('doAllocate', doAllocate)
test('doGenerator', doGenerator)
这使
Elements: 100000, Iterations: 144
doAppend: 2153.122902ms
doAllocate: 1346.076965ms
doGenerator: 1614.092112ms
（在32位中，doGenerator比doAllocate做得更好）。
在这里，doAppend和doAllocate之间的差距明显更大。
显然，这里的差异仅适用于您执行多次以上的操作，或者在负载较重的系统上执行的操作，这些数据将按数量级进行扩展，或者正在处理更大的清单。
这里的重点是：用pythonic方式获得最佳性能。
但是，如果您担心常规的高级性能，则Python是错误的语言。最根本的问题是，由于装饰器等（https://wiki.python.org/moin/PythonSpeed/PerformanceTips#Data_Aggregation#Data_Aggregation）等Python功能，Python函数调用传统上比其他语言慢300倍。