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 :
# 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) |
Input array : [ 1 2 3 -1 2] Masked array : [-- 2 3 -- 2]
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Code #2 :
# 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) |
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]
