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Numpy MaskedArray.masked_less() function | Python

Last Updated : 27 Sep, 2019
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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 :
Output:
Input array :  [ 1  2  3 -1  2]
Masked array :  [-- 2 3 -- 2]
  Code #2 :
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]

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