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_where() function is used to mask an array where a condition is met.It return arr as an array masked where condition is True. Any masked values of arr or condition are also masked in the output.
Syntax :
numpy.ma.masked_where(condition, arr, copy=True)Parameters:
condition : [array_like] Masking condition. When condition tests floating point values for equality, consider using masked_values instead.
arr : [ndarray] Input array which we want to mask.
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 result of masking arr where condition is True..
Code #1 :
# Python program explaining # numpy.MaskedArray.masked_where() 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_where methods # to input array where value<= 1 mask_arr = ma.masked_where(in_arr<= 1, in_arr) print ("Masked array : ", mask_arr) |
Input array : [ 1 2 3 -1 2] Masked array : [-- 2 3 -- 2]
Code #2 :
# Python program explaining # numpy.MaskedArray.masked_where() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr1 in_arr1 = geek.arange(4) print ("1st Input array : ", in_arr1) # applying MaskedArray.masked_where methods # to input array in_arr1 where value = 1 mask_arr1 = ma.masked_where(in_arr1 == 1, in_arr1) print ("1st Masked array : ", mask_arr1) # creating input array in_arr2 in_arr2 = geek.arange(4) print ("2nd Input array : ", in_arr2) # applying MaskedArray.masked_where methods # to input array in_arr2 where value = 1 mask_arr2 = ma.masked_where(in_arr2 == 3, in_arr2) print ("2nd Masked array : ", mask_arr2) # applying MaskedArray.masked_where methods # to 1st masked array where second masked array # is used as condition res_arr = ma.masked_where(mask_arr1 == 3, mask_arr2) print("Resultant Masked array : ", res_arr) |
1st Input array : [0 1 2 3] 1st Masked array : [0 -- 2 3] 2nd Input array : [0 1 2 3] 2nd Masked array : [0 1 2 --] Resultant Masked array : [0 -- 2 --]
