numpy.ma.make_mask() function | Python Last Updated : 20 Jun, 2021 Comments Improve Suggest changes Like Article Like Report 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 mask. Syntax : numpy.ma.make_mask(m, copy = False, shrink = True, dtype = bool )Parameters : arr : [ array_like] Potential mask. copy : [bool, optional] Whether to return a copy of m (True) or m itself (False). shrink : [bool, optional] Whether to shrink m to nomask if all its values are False. dtype : [dtype, optional] Data-type of the output mask. By default, the output mask has a dtype of MaskType (bool).Return : [ndarray] A boolean mask derived from m. Code #1 : Python3 # Python program explaining # numpy.ma.make_mask() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma m = [1, 1, 0, 1] gfg = ma.make_mask(m) print (gfg) Output : [ True True False True] Code #2 : Python3 # Python program explaining # numpy.ma.make_mask() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma m = [2, -3, 0, 1] gfg = ma.make_mask(m) print (gfg) Output : [ True True False True] Code #3 : Python3 # Python program explaining # numpy.ma.make_mask() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma m = [True, True, True, False] gfg = ma.make_mask(m) print (gfg) Output : [ True True True False] Comment More infoAdvertise with us Next Article Introduction to Machine Learning S sanjoy_62 Follow Improve Article Tags : Machine Learning Python-numpy python Python Numpy-Masked Array Practice Tags : Machine Learningpython Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. 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