如何在 Python 3.9.0 中提高此列表迭代的速度？

Question

以下代码是从我正在编写的一些代码中提炼出来的经过测量的热点。 我试图弄清楚如何在 Python 3.9.0 中加速这个循环。 我在 VC++ 2019 中使用std::vector测量相同的循环速度超过 30 倍。

如您所见，我尝试了几种不同的方法。 map()函数似乎返回一个迭代器，所以我将它转换为一个列表来衡量执行的全部成本。

我觉得这是表示我的数据的一种相当自然的方式。 我当然可以在这里进行一些代表性或算法改进。 但是，我有点惊讶在这种情况下迭代如此之慢，我想先看看它是否可以改进。

执行的性能结果： python listIteration.py

Iteration by index
66.66 ms
60.90 ms
62.74 ms
Total: 124998250000
Iteration by index -- just integers
55.22 ms
55.27 ms
80.84 ms
Total: 124998250000
Iteration by object
56.48 ms
60.30 ms
55.77 ms
Total: 124998250000
List comprehension
235.34 ms
328.15 ms
272.47 ms
Total: 124998250000
Map
310.81 ms
353.87 ms
300.27 ms
Total: 124998250000

代码：

import time

def makeList():
    data = []
    for i in range(500000):
        data.append([i, i, i])
    return data

def makeListOfInts():
    data = []
    for i in range(500000):
        data.append(i)
    return data    

def dumpTime(delta):
    print("{:.2f}".format(1000.0*delta) + " ms")


NUM_TRIALS = 3

print("Iteration by index");
data = makeList()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    
    for j in range(len(data)):
        data[j][0] -= 1

    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum([x[0] for x in data])
print("Total: "+ str(total))

print("Iteration by index -- just integers");
data = makeListOfInts()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    
    for j in range(len(data)):
        data[j] -= 1

    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum(data)
print("Total: "+ str(total))

print("Iteration by object");
data = makeList()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    
    for v in data:
        v[0] -= 1

    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum([x[0] for x in data])    
print("Total: "+ str(total))

print("List comprehension");
data = makeList()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    
    data = [[x[0]-1, x[1], x[2]] for x in data]
    
    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum([x[0] for x in data])
print("Total: "+ str(total))    

print("Map");
data = makeList()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    
    # here we convert the map object to a list, because apparently
    # map() returns an iterator, and we want to measure the full cost
    # of the computation
    data = list(map(lambda x: [x[0]-1, x[1], x[2]], data))
    
    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum([x[0] for x in data])
print("Total: "+ str(total))    

Answer 1

Python 代码会比 C++ 慢。 没有办法解决它，除非您将迭代消除/外包给 C 后端，这就是numpy所做的。

例如，你可以做

import numpy as np

def makeArray():
    data = np.vstack((np.arange(500000), np.arange(500000), np.arange(500000))).T
    return data

def makeArrayOfInts():
    data = np.arange(500000)
    return data

然后，您根本不需要迭代。

data = makeArray()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    data[:, 0] = data[:, 0] - 1
    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum(data[:, 0])
print("Total: "+ str(total))

data = makeArrayOfInts()
for t in range(NUM_TRIALS):
    x1 = time.perf_counter()
    data = data - 1
    x2 = time.perf_counter()
    dumpTime(x2-x1)
total = sum(data)
print("Total: "+ str(total))

这两个都是超快的：每个试验需要大约 1 毫秒，而不是迭代列表需要大约 50 毫秒。