通过索引将numpy数组中的值设置为NaN
我想在numpy数组中设置特定值NaN
(以将它们从按行均值计算中排除)。
我试过了
import numpyx = numpy.array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]])
cutoff = [5, 7]
for i in range(len(x)):
x[i][0:cutoff[i]:1] = numpy.nan
看着x
,我只会看到-9223372036854775808
我的期望NaN
。
我想到了一个替代方案:
for i in range(len(x)): for k in range(cutoff[i]):
x[i][k] = numpy.nan
没发生什么事。我究竟做错了什么?
回答:
[@unutbu的解决方案必须摆脱您得到的值错误。如果您希望vectorize
获得性能,可以这样使用boolean
indexing-
import numpy as np# Create mask of positions in x (with float datatype) where NaNs are to be put
mask = np.asarray(cutoff)[:,None] > np.arange(x.shape[1])
# Put NaNs into masked region of x for the desired ouput
x[mask] = np.nan
样品运行-
In [92]: x = np.random.randint(0,9,(4,7)).astype(float)In [93]: x
Out[93]:
array([[ 2., 1., 5., 2., 5., 2., 1.],
[ 2., 5., 7., 1., 5., 4., 8.],
[ 1., 1., 7., 4., 8., 3., 1.],
[ 5., 8., 7., 5., 0., 2., 1.]])
In [94]: cutoff = [5,3,0,6]
In [95]: x[np.asarray(cutoff)[:,None] > np.arange(x.shape[1])] = np.nan
In [96]: x
Out[96]:
array([[ nan, nan, nan, nan, nan, 2., 1.],
[ nan, nan, nan, 1., 5., 4., 8.],
[ 1., 1., 7., 4., 8., 3., 1.],
[ nan, nan, nan, nan, nan, nan, 1.]])
如果要获取掩盖的平均值,则可以修改较早提出的矢量化方法,以避免NaNs
完全处理,更重要的是保留x
整数值。这是修改后的方法-
# Get array version of cutoffcutoff_arr = np.asarray(cutoff)
# Mask of positions in x which are to be considered for row-wise mean calculations
mask1 = cutoff_arr[:,None] <= np.arange(x.shape[1])
# Mask x, calculate the corresponding sum and thus mean values for each row
masked_mean_vals = (mask1*x).sum(1)/(x.shape[1] - cutoff_arr)
这是这种解决方案的示例运行-
In [61]: x = np.random.randint(0,9,(4,7))In [62]: x
Out[62]:
array([[5, 0, 1, 2, 4, 2, 0],
[3, 2, 0, 7, 5, 0, 2],
[7, 2, 2, 3, 3, 2, 3],
[4, 1, 2, 1, 4, 6, 8]])
In [63]: cutoff = [5,3,0,6]
In [64]: cutoff_arr = np.asarray(cutoff)
In [65]: mask1 = cutoff_arr[:,None] <= np.arange(x.shape[1])
In [66]: mask1
Out[66]:
array([[False, False, False, False, False, True, True],
[False, False, False, True, True, True, True],
[ True, True, True, True, True, True, True],
[False, False, False, False, False, False, True]], dtype=bool)
In [67]: masked_mean_vals = (mask1*x).sum(1)/(x.shape[1] - cutoff_arr)
In [68]: masked_mean_vals
Out[68]: array([ 1. , 3.5 , 3.14285714, 8. ])
以上是 通过索引将numpy数组中的值设置为NaN 的全部内容, 来源链接: utcz.com/qa/425423.html