在numpy数组上映射函数的最有效方法 [英] Most efficient way to map function over numpy array

问题描述

在numpy数组上映射函数的最有效方法是什么?我在当前项目中一直采用的方式如下:

What is the most efficient way to map a function over a numpy array? The way I've been doing it in my current project is as follows:

import numpy as np 

x = np.array([1, 2, 3, 4, 5])

# Obtain array of square of each element in x
squarer = lambda t: t ** 2
squares = np.array([squarer(xi) for xi in x])

但是，这似乎效率很低，因为我使用列表理解将新数组构造为Python列表，然后再将其转换回numpy数组.

However, this seems like it is probably very inefficient, since I am using a list comprehension to construct the new array as a Python list before converting it back to a numpy array.

我们可以做得更好吗?

推荐答案

我已经用np.array(map(f, x)) > perfplot (属于我的一个小项目).

I've tested all suggested methods plus np.array(map(f, x)) with perfplot (a small project of mine).

消息1:如果可以使用numpy的本机函数，则执行此操作.

Message #1: If you can use numpy's native functions, do that.

如果您要向量化的函数已经被 进行了向量化(例如原始帖子中的x**2示例)，则使用它的速度将比其他任何东西都要快 (请注意对数刻度):

If the function you're trying to vectorize already is vectorized (like the x**2 example in the original post), using that is much faster than anything else (note the log scale):

如果您确实需要向量化，那么使用哪种变体并没有多大关系.

If you actually need vectorization, it doesn't really matter much which variant you use.

用于重现绘图的代码:

import numpy as np
import perfplot
import math


def f(x):
    # return math.sqrt(x)
    return np.sqrt(x)


vf = np.vectorize(f)


def array_for(x):
    return np.array([f(xi) for xi in x])


def array_map(x):
    return np.array(list(map(f, x)))


def fromiter(x):
    return np.fromiter((f(xi) for xi in x), x.dtype)


def vectorize(x):
    return np.vectorize(f)(x)


def vectorize_without_init(x):
    return vf(x)


perfplot.show(
    setup=lambda n: np.random.rand(n),
    n_range=[2 ** k for k in range(20)],
    kernels=[f, array_for, array_map, fromiter, vectorize, vectorize_without_init],
    xlabel="len(x)",
)

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