numpy.ma.make_mask_none() function | Python Last Updated : 22 Apr, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report numpy.ma.make_mask_none() function return a boolean mask of the given shape, filled with False. This function returns a boolean ndarray with all entries False, that can be used in common mask manipulations. If a complex dtype is specified, the type of each field is converted to a boolean type. Syntax : numpy.ma.make_mask_none(newshape, dtype = None) Parameters : newshape : [tuple] A tuple indicating the shape of the mask. dtype : [{None, dtype}, optional] By default, the dtype is None. Otherwise, use a new datatype with the same fields as dtype, converted to boolean types. Return : [ndarray] An ndarray of appropriate shape and dtype, filled with False. Code #1 : Python3 # Python program explaining # numpy.ma.make_mask_none() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma gfg = ma.make_mask_none(4) print (gfg) Output : [False False False False] Code #2 : Python3 # Python program explaining # numpy.ma.make_mask_none() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma gfg = ma.make_mask_none(4, dtype = None) print (gfg) Output : [False False False False] Comment More infoAdvertise with us Next Article numpy.ma.is_mask() 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.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 numpy.ma.is_masked() function | Python numpy.ma.is_masked() function determine whether input has masked values & accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values. Syntax : numpy.ma.is_masked(arr) Parameters : arr : [array_like] Array to check for masked values. Return : 1 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.MaskedArray.nonzero() function - Python numpy.ma.MaskedArray.nonzero() function return the indices of unmasked elements that are not zero. This function returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. Syntax : numpy.ma.MaskedArray.nonzero(self) Return : [tuple] Indices 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.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 Like