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# numpy.ma.masked_where

`numpy.ma.``masked_where`(condition, a, copy=True)

  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: | 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.
  Returns: | result : MaskedArray 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

      >>> 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)

  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)