The Pythonic way for this is:
x = [None] * numElements
Or whatever default value you wish to prepopulate with, e.g.
bottles = [Beer()] * 99
sea = [Fish()] * many
vegetarianPizzas = [None] * peopleOrderingPizzaNotQuiche
(Caveat Emptor: The [Beer()] * 99 syntax creates one Beer and then populates an array with 99 references to the same single instance)
Python's default approach can be pretty efficient, although that efficiency decays as you increase the number of elements.
Compare
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)
with
#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);
}
On my Windows 7 Core i7, 64-bit Python gives
Elements: 100000, Iterations: 144
doAppend: 3587.204933ms
doAllocate: 2701.154947ms
doGenerator: 1721.098185ms
While C++ gives (built with Microsoft Visual C++, 64-bit, optimizations enabled)
Elements: 100000, Iterations: 144
doAppend: 74.0042ms
doReserve: 27.0015ms
doAllocate: 5.0003ms
C++ debug build produces:
Elements: 100000, Iterations: 144
doAppend: 2166.12ms
doReserve: 2082.12ms
doAllocate: 273.016ms
The point here is that with Python you can achieve a 7-8% performance improvement, and if you think you're writing a high-performance application (or if you're writing something that is used in a web service or something) then that isn't to be sniffed at, but you may need to rethink your choice of language.
Also, the Python code here isn't really Python code. Switching to truly Pythonesque code here gives better performance:
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)
Which gives
Elements: 100000, Iterations: 144
doAppend: 2153.122902ms
doAllocate: 1346.076965ms
doGenerator: 1614.092112ms
(in 32-bit, doGenerator does better than doAllocate).
Here the gap between doAppend and doAllocate is significantly larger.
Obviously, the differences here really only apply if you are doing this more than a handful of times or if you are doing this on a heavily loaded system where those numbers are going to get scaled out by orders of magnitude, or if you are dealing with considerably larger lists.
The point here: Do it the Pythonic way for the best performance.
But if you are worrying about general, high-level performance, Python is the wrong language. The most fundamental problem being that Python function calls has traditionally been up to 300x slower than other languages due to Python features like decorators, etc. (PythonSpeed/PerformanceTips, Data Aggregation).
whileloops with unclear or nondeterministic termination conditions,itertoolsand generators can rescue the logic back to list comprehension land much of the time. – Docent