numpy.ma.mask_cols() function | Python

numpy.ma.mask_rows() function | Python

Last Updated : 13 Mar, 2021

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 :

Python3

Python3

# 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 :

Python3

Python3

# 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]]

numpy.ma.mask_cols() function | Python

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