In this numpy.ma.mask_rows() function, mask rows of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 0.
Syntax : numpy.ma.mask_rows(arr, axis = None)
Parameters :
arr : [array_like, MaskedArray] The array to mask. The result is a MaskedArray.
axis : [int, optional] Axis along which to perform the operation. Default is None.
Return : [MaskedArray] A modified version of the input array.
Code #1 :
# Python program explaining
# numpy.ma.mask_rows() function
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
arr = geek.zeros((4, 4), dtype = int)
arr[2, 2] = 1
arr = ma.masked_equal(arr, 1)
gfg = ma.mask_rows(arr)
print (gfg)
Output :
[[0 0 0 0] [0 0 0 0] [-- -- -- --] [0 0 0 0]]
Code #2 :
# Python program explaining
# numpy.ma.mask_rows() function
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
arr = geek.zeros((5, 5), dtype = int)
arr[3, 3] = 1
arr = ma.masked_equal(arr, 1)
gfg = ma.mask_rows(arr)
print (gfg)
Output :
[[0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0] [-- -- -- -- --] [0 0 0 0 0]]