Skip to content
Related Articles

Related Articles

Improve Article
Save Article
Like Article

Numpy MaskedArray.anom() function | Python

  • Last Updated : 27 Sep, 2019

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.

numpy.MaskedArray.anom() function Compute the anomalies (deviations from the arithmetic mean) along the given axis.It returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis.

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

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.

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

 

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



My Personal Notes arrow_drop_up
Recommended Articles
Page :

Start Your Coding Journey Now!