Numpy MaskedArray.masked_less() function | Python

Numpy MaskedArray.masked_less() 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_less() function is used to mask an array where less than a given value.This function is a shortcut to masked_where, with condition = (arr < value).

Syntax : numpy.ma.masked_less(arr, value, copy=True) Parameters: arr : [ndarray] Input array which we want to mask. value : [int] It is used to mask the array element which are < value. 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_less() 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, 2])
print ("Input array : ", in_arr)

# applying MaskedArray.masked_less methods 
# to input array where value<2
mask_arr = ma.masked_less(in_arr, 2)
print ("Masked array : ", mask_arr)

Output:

Input array :  [ 1  2  3 -1  2]
Masked array :  [-- 2 3 -- 2]

Code #2 :

Python3

# Python program explaining
# numpy.MaskedArray.masked_less() 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([5e8, 3e-5, -45.0, 4e4, 5e2])
print ("Input array : ", in_arr)

# applying MaskedArray.masked_less methods 
# to input array where value<5e2
mask_arr = ma.masked_less(in_arr, 5e2)
print ("Masked array : ", mask_arr)

Output:

Input array :  [ 5.0e+08  3.0e-05 -4.5e+01  4.0e+04  5.0e+02]
Masked array :  [500000000.0 -- -- 40000.0 500.0]

Numpy MaskedArray.masked_less() function | Python

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