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

copybool

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

Returns

The result of masking a where condition is True.

`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

```>>> 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)
```