📜  Numpy MaskedArray.any()函数| Python

📅  最后修改于: 2022-05-13 01:55:12.512000             🧑  作者: Mango

Numpy MaskedArray.any()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.any()函数返回 True 如果掩码数组的任何元素计算为 True。Masked 值在计算期间被视为 False。

代码#1:

# Python program explaining
# numpy.MaskedArray.any() 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  array :  True

代码#2:

# Python program explaining
# numpy.MaskedArray.any() 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, 20, 30, 40, 50])
print ("Input array : ", in_arr)
  
# Now we are creating a masked array by making 
# all entry as invalid. 
mask_arr = ma.masked_array(in_arr, mask ='True')
print ("Masked array : ", mask_arr)
  
# applying MaskedArray.any methods to mask array
out_arr = mask_arr.any()
print ("Output array : ", out_arr)
输出:
Input array :  [ 1 20 30 40 50]
Masked array :  [-- -- -- -- --]
Output array :  --