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 :

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]