Pythonic 的方式是：
x = [None] * numElements
或您希望预填充的任何默认值，例如
bottles = [Beer()] * 99
sea = [Fish()] * many
vegetarianPizzas = [None] * peopleOrderingPizzaNotQuiche
（Caveat Emptor：[Beer()] * 99 语法创建 一个 Beer，然后用 99 个对同一单个实例的引用填充数组）
Python 的默认方法可能非常有效，尽管随着元素数量的增加，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 Core i7 上，64 位 Python 提供
Elements: 100000, Iterations: 144
doAppend: 3587.204933ms
doAllocate: 2701.154947ms
doGenerator: 1721.098185ms
虽然 C++ 提供（使用 Microsoft Visual C++ 构建，64 位，已启用优化）
Elements: 100000, Iterations: 144
doAppend: 74.0042ms
doReserve: 27.0015ms
doAllocate: 5.0003ms
C++ 调试生成：
Elements: 100000, Iterations: 144
doAppend: 2166.12ms
doReserve: 2082.12ms
doAllocate: 273.016ms
这里的重点是，使用 Python，您可以实现 7-8% 的性能提升，并且如果您认为自己正在编写高性能应用程序（或者如果您正在编写用于 Web 服务或一些东西）那么这不是被嗤之以鼻的，但你可能需要重新考虑你选择的语言。
另外，这里的 Python 代码并不是真正的 Python 代码。在这里切换到真正的 Pythonesque 代码可以获得更好的性能：
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)
这给了
Elements: 100000, Iterations: 144
doAppend: 2153.122902ms
doAllocate: 1346.076965ms
doGenerator: 1614.092112ms
（在 32 位中，doGenerator 比 doAllocate 做得更好）。
这里 doAppend 和 doAllocate 之间的差距要大得多。
显然，这里的差异仅适用于您执行此操作的次数超过几次，或者您在负载重的系统上执行此操作时，这些数字将按数量级扩展，或者如果您正在处理更大的列表。
这里的重点：以 Python 式的方式实现最佳性能。
但是，如果您担心一般的高级性能，那么 Python 是错误的语言。最根本的问题是，由于装饰器等 Python 特性，Python 函数调用通常比其他语言慢 300 倍。(PythonSpeed/PerformanceTips, Data Aggregation)。