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 arrays that may have missing or invalid entries.
numpy.MaskedArray.masked_where() function is used to mask an array where a condition is met.It return arr as an array masked where condition is True. Any masked values of arr or condition are also masked in the output.
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numpy.ma.masked_where(condition, arr, copy=True)
condition : [array_like] Masking condition. When condition tests floating point values for equality, consider using masked_values instead.
arr : [ndarray] Input array which we want to mask.
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 result of masking arr where condition is True..
Code #1 :
Input array : [ 1 2 3 -1 2] Masked array : [-- 2 3 -- 2]
Code #2 :
1st Input array : [0 1 2 3] 1st Masked array : [0 -- 2 3] 2nd Input array : [0 1 2 3] 2nd Masked array : [0 1 2 --] Resultant Masked array : [0 -- 2 --]