Numpy MaskedArray.masked_inside() function | Python

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_inside() function is used to mask an array inside a given interval.This function is a Shortcut to masked_where, where condition is True for arr inside the interval [v1, v2] (v1 <= arr <= v2). The boundaries v1 and v2 can be given in either order.

Syntax : numpy.ma.masked_inside(arr, v1, v2, copy=True) Parameters: arr : [ndarray] Input array which we want to mask. v1, v2 : [int] Lower and upper range. 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_inside() 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_inside methods 
# to input array in the range[-1, 1]
mask_arr = ma.masked_inside(in_arr, -1, 1)
print ("Masked array : ", mask_arr)

  Code #2 : Python3

# Python program explaining
# numpy.MaskedArray.masked_inside() 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_inside methods 
# to input array in the range[5e2, 5e8]
mask_arr = ma.masked_inside(in_arr, 5e2, 5e8)
print ("Masked array : ", mask_arr)