通过索引将numpy数组中的值设置为NaN

Question

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 numpy

x = 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？

Answer 1

将适当元素设置为NaN的矢量化方法

@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.]])

---

向量化方法可直接计算适当元素的按行平均值

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 cutoff
cutoff_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.        ])