numpy.ma.mask_cols() function | Python Last Updated : 22 Apr, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report In thisnumpy.ma.mask_cols() function, mask columns of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 1. Syntax : numpy.ma.mask_cols(arr, axis = None) Parameters : arr : [array_like, MaskedArray] The array to mask. 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 # Python program explaining # numpy.ma.mask_cols() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma arr = geek.zeros((3, 3), dtype = int) arr[1, 1] = 1 arr = ma.masked_equal(arr, 1) gfg = ma.mask_cols(arr) print (gfg) Output : [[0 -- 0] [0 -- 0] [0 -- 0]] Code #2 : Python3 # Python program explaining # numpy.ma.mask_cols() 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_cols(arr) print (gfg) Output : [[0 0 -- 0] [0 0 -- 0] [0 0 -- 0] [0 0 -- 0]] Comment More infoAdvertise with us Next Article numpy.ma.masked_all() function | Python S sanjoy_62 Follow Improve Article Tags : Machine Learning Python-numpy python Python Numpy-Masked Array Practice Tags : Machine Learningpython Similar Reads numpy.ma.masked_all() function | Python numpy.ma.masked_all() function return an empty masked array of the given shape and dtype, where all the data are masked. Syntax : numpy.ma.masked_all(shape, dtype) Parameter : shape : [tuple] Shape of the required MaskedArray. dtype : [dtype, optional] Data type of the output. Return : [MaskedArray] 1 min read numpy.ma.clump_masked() function | Python numpy.ma.clump_masked() function returns a list of slices corresponding to the masked clumps of a 1-D array. Syntax : numpy.ma.clump_masked(arr) Parameters : arr : [ndarray] A one-dimensional masked array. Return : [list of slice] The list of slices, one for each continuous region of masked elements 1 min read numpy.ma.mask_or() function | Python numpy.ma.mask_or() function combine two masks with the logical_or operator. The result may be a view on m1 or m2 if the other is nomask (i.e. False). Syntax : numpy.ma.mask_or(m1, m2, copy = False, shrink = True) Parameters : m1, m2 : [ array_like] Input masks. copy : [bool, optional] If copy is Fal 2 min read numpy.ma.is_mask() function | Python numpy.ma.is_mask() function return True if parameter m is a valid, standard mask. This function does not check the contents of the input, only that the type is MaskType. In particular, this function returns False if the mask has a flexible dtype. Syntax : numpy.ma.is_mask(m) Parameter : m : [array_l 1 min read numpy.ma.mask_rows() function | Python 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 2 min read numpy.ma.make_mask() function | Python numpy.ma.make_mask() function is used to create a boolean mask from an array. This function can accept any sequence that is convertible to integers, or nomask. It does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True. Return m as a boolean ma 2 min read Like