Numpy MaskedArray.masked_less() function | Python Last Updated : 27 Sep, 2019 Comments Improve Suggest changes Like Article Like Report In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries. numpy.MaskedArray.masked_less() function is used to mask an array where less than a given value.This function is a shortcut to masked_where, with condition = (arr < value). Syntax : numpy.ma.masked_less(arr, value, copy=True) Parameters: arr : [ndarray] Input array which we want to mask. value : [int] It is used to mask the array element which are < value. copy : [bool] If True (default) make a copy of arr in the result. If False modify arr in place and return a view. Return : [ MaskedArray] The resultant array after masking. Code #1 : Python3 # Python program explaining # numpy.MaskedArray.masked_less() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([1, 2, 3, -1, 2]) print ("Input array : ", in_arr) # applying MaskedArray.masked_less methods # to input array where value<2 mask_arr = ma.masked_less(in_arr, 2) print ("Masked array : ", mask_arr) Output: Input array : [ 1 2 3 -1 2] Masked array : [-- 2 3 -- 2] Code #2 : Python3 # Python program explaining # numpy.MaskedArray.masked_less() method # importing numpy as geek # and numpy.ma module as ma import numpy as geek import numpy.ma as ma # creating input array in_arr = geek.array([5e8, 3e-5, -45.0, 4e4, 5e2]) print ("Input array : ", in_arr) # applying MaskedArray.masked_less methods # to input array where value<5e2 mask_arr = ma.masked_less(in_arr, 5e2) print ("Masked array : ", mask_arr) Output: Input array : [ 5.0e+08 3.0e-05 -4.5e+01 4.0e+04 5.0e+02] Masked array : [500000000.0 -- -- 40000.0 500.0] Comment More infoAdvertise with us Next Article Numpy MaskedArray.masked_less() function | Python jana_sayantan Follow Improve Article Tags : Python Python-numpy Practice Tags : python Similar Reads Numpy MaskedArray.masked_less_equal() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray.masked_inside() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray.masked_greater() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray.masked_invalid() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray.masked_where() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 3 min read Numpy MaskedArray.masked_equal() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray masked_outside() function | Python numpy.MaskedArray.masked_outside() function is used to mask an array outside of a given interval. This function is a Shortcut to masked_where, where condition is True for arr outside the interval [v1, v2] (arr <v1)|(arr > v2). The boundaries v1 and v2 can be given in either order. Syntax : num 2 min read Numpy MaskedArray.masked_greater_equal() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 2 min read Numpy MaskedArray.masked_not_equal() function | Python In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arra 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 Like