Numpy MaskedArray.masked_less() function | Python (original) (raw)

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 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([ 1 , 2 , 3 , - 1 , 2 ])

print ( "Input array : " , in_arr)

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 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([ 5e8 , 3e - 5 , - 45.0 , 4e4 , 5e2 ])

print ( "Input array : " , in_arr)

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]