In this numpy.ma.mask_rowcols() function, mask rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter.
If axis is None, rows and columns are masked.
If axis is 0, only rows are masked.
If axis is 1 or -1, only columns are masked.
Syntax : numpy.ma.mask_rowcols(arr, axis = None)
Parameters :
arr : [array_like, MaskedArray] The array to mask. The result is a MaskedArray with mask set to nomask (False). Must be a 2D array.
axis : [int, optional] Axis along which to perform the operation. Default is None.Return : [MaskedArray] A modified version of the input array, masked depending on the value of the axis parameter.
Code #1 :
# Python program explaining # numpy.ma.mask_rowcols() 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_rowcols(arr) print (gfg) |
Output :
[[0 0 -- 0] [0 0 -- 0] [-- -- -- --] [0 0 -- 0]]
Code #2 :
# Python program explaining # numpy.ma.mask_rowcols() 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_rowcols(arr) print (gfg) |
Output :
[[0 0 0 -- 0] [0 0 0 -- 0] [0 0 0 -- 0] [-- -- -- -- --] [0 0 0 -- 0]]
