numpy.ma.core:masked_where

Summary

Mask an array where a condition is met.

Return a as an array masked where condition is True. Any masked values of a or condition are also masked in the output.

condition : array_like: Masking condition. When condition tests floating point values for equality, consider using masked_values instead.
a : array_like: Array to mask.
copy : bool: If True (default) make a copy of a in the result. If False modify a in place and return a view.

result : MaskedArray: The result of masking a where condition is True.

import numpy as np
import numpy.ma as ma
a = np.arange(4)
a
ma.masked_where(a <= 2, a)

Mask array `b` conditional on `a`.

b = ['a', 'b', 'c', 'd']
ma.masked_where(a == 2, b)

Effect of the `copy` argument.

c = ma.masked_where(a <= 2, a)
c
c[0] = 99
c
a
c = ma.masked_where(a <= 2, a, copy=False)
c[0] = 99
c
a

When `condition` or `a` contain masked values.

a = np.arange(4)
a = ma.masked_where(a == 2, a)
a
b = np.arange(4)
b = ma.masked_where(b == 0, b)
b
ma.masked_where(a == 3, b)