通过指数numpy的阵列设置值为NaN [英] Set values in numpy array to NaN by index

问题描述

我想在numpy的阵列特定值设置为 NaN的（从行明智的均值计算排除它们）。

I want to set specific values in a numpy array to NaN (to exclude them from a row-wise mean calculation).

我试过

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 ，我只看到 -9223372036854775808 其中，我希望 NaN的。

Looking at x, I only see -9223372036854775808 where I expect NaN.

我想过一种替代方案：

for i in range(len(x)):
    for k in range(cutoff[i]):
        x[i][k] = numpy.nan

什么也没有发生。我在做什么错了？

Nothing happens. What am I doing wrong?

推荐答案

矢量化的方法来设置适当的元素的NaN

@ unutbu的解决方案必须摆脱你所获得的价值的错误。如果您正在寻找矢量性能，你可以使用的 布尔索引 像这样 -

@unutbu's solution must get rid of the value error you were getting. If you are looking to vectorize for performance, you can use boolean indexing like so -

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.]])

---

矢量化的方法来直接计算出相应的元素逐行平均

如果你试图让蒙面平均值，可以修改较早提出量化的方法，以避免与的NaN 处理完全和更重要的是保持 X 与整数值。下面是修改的方法 -

If you were trying to get the masked mean values, you can modify the earlier proposed vectorized approach to avoid dealing with NaNs altogether and more importantly keep x with integer values. Here's the modified approach -

# 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)

下面对这样的解决方案运行示例 -

Here's a sample run for such a solution -

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.        ])

这篇关于通过指数numpy的阵列设置值为NaN的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！