numpy.ma.masked_where

ma.masked_where(condition, a, copy=True)[source]

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.

Parameters:

conditionarray_like

Masking condition. When condition tests floating point values for equality, consider using masked_values instead.

aarray_like

Array to mask.

copybool

If True (default) make a copy of a in the result. If False modify a in place and return a view.

Returns:

resultMaskedArray

The result of masking a where condition is True.

See also

masked_values

Mask using floating point equality.

masked_equal

Mask where equal to a given value.

masked_not_equal

Mask where not equal to a given value.

masked_less_equal

Mask where less than or equal to a given value.

masked_greater_equal

Mask where greater than or equal to a given value.

masked_less

Mask where less than a given value.

masked_greater

Mask where greater than a given value.

masked_inside

Mask inside a given interval.

masked_outside

Mask outside a given interval.

masked_invalid

Mask invalid values (NaNs or infs).

Examples

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>>> import numpy as np
>>> import numpy.ma as ma
>>> a = np.arange(4)
>>> a
array([0, 1, 2, 3])
>>> ma.masked_where(a <= 2, a)
masked_array(data=[--, --, --, 3],
             mask=[ True,  True,  True, False],
       fill_value=999999)

Mask array b conditional on a.

>>> b = ['a', 'b', 'c', 'd']
>>> ma.masked_where(a == 2, b)
masked_array(data=['a', 'b', --, 'd'],
             mask=[False, False,  True, False],
       fill_value='N/A',
            dtype='<U1')

Effect of the copy argument.

>>> c = ma.masked_where(a <= 2, a)
>>> c
masked_array(data=[--, --, --, 3],
             mask=[ True,  True,  True, False],
       fill_value=999999)
>>> c[0] = 99
>>> c
masked_array(data=[99, --, --, 3],
             mask=[False,  True,  True, False],
       fill_value=999999)
>>> a
array([0, 1, 2, 3])
>>> c = ma.masked_where(a <= 2, a, copy=False)
>>> c[0] = 99
>>> c
masked_array(data=[99, --, --, 3],
             mask=[False,  True,  True, False],
       fill_value=999999)
>>> a
array([99,  1,  2,  3])

When condition or a contain masked values.

>>> a = np.arange(4)
>>> a = ma.masked_where(a == 2, a)
>>> a
masked_array(data=[0, 1, --, 3],
             mask=[False, False,  True, False],
       fill_value=999999)
>>> b = np.arange(4)
>>> b = ma.masked_where(b == 0, b)
>>> b
masked_array(data=[--, 1, 2, 3],
             mask=[ True, False, False, False],
       fill_value=999999)
>>> ma.masked_where(a == 3, b)
masked_array(data=[--, 1, --, --],
             mask=[ True, False,  True,  True],
       fill_value=999999)

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