Numpy MaskedArray.anom()函数| Python

在许多情况下，数据集可能不完整或因存在无效数据而受到污染。例如，传感器可能无法记录数据，或记录无效值。 numpy.ma模块通过引入掩码数组提供了一种解决此问题的便捷方法。掩码数组是可能缺少或无效条目的数组。

numpy.MaskedArray.anom()函数计算沿给定轴的异常（与算术平均值的偏差）。它返回一个异常数组，其形状与输入相同，算术平均值沿给定轴计算。

Syntax : numpy.MaskedArray.anom(axis=None, dtype=None)

Parameters:
axis : [int or None] Axis over which the anomalies are taken.
dtype : [ dtype, optional] Type to use in computing the variance.

Return : [ndarray]an array of anomalies.

代码#1：

# Python program explaining
# numpy.MaskedArray.anom() method 
  
# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
  
# creating input array 
in_arr = geek.array([1, 2, 3, -1, 5])
print ("Input array : ", in_arr)
  
# Now we are creating a masked array
# by making third entry as invalid. 
mask_arr = ma.masked_array(in_arr, mask =[0, 0, 1, 0, 0])
print ("Masked array : ", mask_arr)
  
# applying MaskedArray.anom methods to mask array
out_arr = mask_arr.anom()
print ("Output anomalies array : ", out_arr)

输出：

Input array :  [ 1  2  3 -1  5]
Masked array :  [1 2 -- -1 5]
Output anomalies array :  [-0.75 0.25 -- -2.75 3.25]

代码#2：

# Python program explaining
# numpy.MaskedArray.anom() method 
  
# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
  
# creating input array 
in_arr = geek.array([10, 20, 30, 40, 50])
print ("Input array : ", in_arr)
  
# Now we are creating a masked array by making 
# first and third entry as invalid. 
mask_arr = ma.masked_array(in_arr, mask =[1, 0, 1, 0, 0])
print ("Masked array : ", mask_arr)
  
# applying MaskedArray.anom methods to mask array
out_arr = mask_arr.anom()
print ("Output anomalies array : ", out_arr)

输出：

nput array :  [10 20 30 40 50]
Masked array :  [-- 20 -- 40 50]
Output anomalies array :  [-- -16.666666666666664 -- 3.3333333333333357 13.333333333333336]