Numpy MaskedArray.masked_inside() function | Python

Numpy MaskedArray.masked_invalid() 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.masked_invalid() function is used to mask an array where invalid values occur (NaNs or infs).This function is a shortcut to masked_where, with condition = ~(numpy.isfinite(arr)).

Syntax : numpy.ma.masked_invalid(arr, copy=True) Parameters: arr : [ndarray] Input array which we want to mask. copy : [bool] If True (default) make a copy of arr in the result. If False modify arr in place and return a view. Return : [ MaskedArray] The resultant array after masking.

Code #1 :

Python3

# Python program explaining
# numpy.MaskedArray.masked_invalid() method 

# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma

# creating input array with invalid values
in_arr = geek.array([1, 2, geek.nan, -1, geek.inf])
print ("Input array : ", in_arr)

# applying MaskedArray.masked_invalid  
# methods to input array 
mask_arr = ma.masked_invalid(in_arr)
print ("Masked array : ", mask_arr)

Output:

Input array :  [ 1.  2. nan -1. inf]
Masked array :  [1.0 2.0 -- -1.0 --]

Code #2 :

Python3

# Python program explaining
# numpy.MaskedArray.masked_invalid() method 

# importing numpy as geek 
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma

# creating input array with invalid element
in_arr = geek.array([5e8, 3e-5, geek.nan, 4e4, 5e2])
print ("Input array : ", in_arr)

# applying MaskedArray.masked_invalid  
# methods to input array 
mask_arr = ma.masked_invalid(in_arr)
print ("Masked array : ", mask_arr)

Output:

Input array :  [5.e+08 3.e-05    nan 4.e+04 5.e+02]
Masked array :  [500000000.0 3e-05 -- 40000.0 500.0]

Numpy MaskedArray.masked_inside() function | Python

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