numpy.ma.masked_values() function | Python Last Updated : 05 May, 2020 Comments Improve Suggest changes Like Article Like Report numpy.ma.masked_values() function return a MaskedArray, masked where the data in array arr are approximately equal to value, determined using isclose. The default tolerances for masked_values are the same as those for isclose. Syntax : numpy.ma.masked_values(arr, value, rtol = 1e-05, atol = 1e-08, copy = True, shrink = True) Parameter : arr : [array_like] Array to mask. value : [float] Masking value. rtol, atol : [float, optional] Must be convertible to an array of booleans with the same shape as data. True indicates a masked data. copy : [bool, optional] Whether to return a copy of arr. shrink : [bool, optional] Whether to collapse a mask full of False to nomask. Return : [MaskedArray] The result of masking arr where approximately equal to value. Code #1 : Python3 # Python program explaining # numpy.ma.masked_values() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma arr = geek.array([1, 1.5, 2, 1.5, 3]) gfg = ma.masked_values(arr, 1.5) print (gfg) Output : [1.0 -- 2.0 -- 3.0] Code #2 : Python3 # Python program explaining # numpy.ma.masked_values() function # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma arr = geek.array([1, 2, 3, 4, 5, 6]) gfg = ma.masked_values(arr, 4) print (gfg) Output : [1 2 3 -- 5 6] Comment More infoAdvertise with us Next Article numpy.ma.masked_values() 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.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.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.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.mask_rowcols() function | Python 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. Synta 2 min read numpy.ma.MaskedArray.tolist() function - Python numpy.ma.MaskedArray.tolist() function return the data portion of the masked array as a hierarchical Python list. Syntax : numpy.ma.MaskedArray.tolist(fill_value = None) Parameters : axis : [scalar, optional] The value to use for invalid entries. Default is None. Return : [list] The Python list repr 1 min read Like