创建填充NaN的numpy矩阵

在numpy中,向量操作很少需要循环。 您可以创建一个未初始化的数组,并一次分配给所有条目:

>>> a = numpy.empty((3,3,))
>>> a[:] = numpy.nan
>>> a
array([[ NaN,  NaN,  NaN],
       [ NaN,  NaN,  NaN],
       [ NaN,  NaN,  NaN]])

a[:] = numpy.nan 在这里和 a.fill(numpy.nan) 由Blaenk发布:

$ python -mtimeit "import numpy as np; a = np.empty((100,100));" "a.fill(np.nan)"
10000 loops, best of 3: 54.3 usec per loop
$ python -mtimeit "import numpy as np; a = np.empty((100,100));" "a[:] = np.nan" 
10000 loops, best of 3: 88.8 usec per loop

计时显示了以下偏好 ndarray.fill(..)

请注意 ndarray.fill numpy.empty((3,3,)).fill(numpy.nan) 将返回 None .

numpy.full

a = np.full([height, width, 9], np.nan)

我比较了建议的速度替代方案,发现对于足够大的向量/矩阵,除了 val * ones 和 array(n * [val])

再现情节的代码:

import numpy
import perfplot

val = 42.0


def fill(n):
    a = numpy.empty(n)
    a.fill(val)
    return a


def colon(n):
    a = numpy.empty(n)
    a[:] = val
    return a


def full(n):
    return numpy.full(n, val)


def ones_times(n):
    return val * numpy.ones(n)


def list(n):
    return numpy.array(n * [val])


b = perfplot.bench(
    setup=lambda n: n,
    kernels=[fill, colon, full, ones_times, list],
    n_range=[2 ** k for k in range(20)],
    xlabel="len(a)",
)
b.save("out.png")