Function where

numpy/ma/core.py:7930–8019

Return a masked array with elements from `x` or `y`, depending on condition. .. note:: When only `condition` is provided, this function is identical to `nonzero`. The rest of this documentation covers only the case where all three arguments are provided. Parame

(condition, x=_NoValue, y=_NoValue)

def where(condition, x=_NoValue, y=_NoValue):
    """
    Return a masked array with elements from `x` or `y`, depending on condition.

    .. note::
        When only `condition` is provided, this function is identical to
        `nonzero`. The rest of this documentation covers only the case where
        all three arguments are provided.

    Parameters
    ----------
    condition : array_like, bool
        Where True, yield `x`, otherwise yield `y`.
    x, y : array_like, optional
        Values from which to choose. `x`, `y` and `condition` need to be
        broadcastable to some shape.

    Returns
    -------
    out : MaskedArray
        A masked array with `masked` elements where the condition is masked,
        elements from `x` where `condition` is True, and elements from `y`
        elsewhere.

    See Also
    --------
    numpy.where : Equivalent function in the top-level NumPy module.
    nonzero : The function that is called when x and y are omitted

    Examples
    --------
    >>> import numpy as np
    >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],
    ...                                                    [1, 0, 1],
    ...                                                    [0, 1, 0]])
    >>> x
    masked_array(
      data=[[0.0, --, 2.0],
            [--, 4.0, --],
            [6.0, --, 8.0]],
      mask=[[False,  True, False],
            [ True, False,  True],
            [False,  True, False]],
      fill_value=1e+20)
    >>> np.ma.where(x > 5, x, -3.1416)
    masked_array(
      data=[[-3.1416, --, -3.1416],
            [--, -3.1416, --],
            [6.0, --, 8.0]],
      mask=[[False,  True, False],
            [ True, False,  True],
            [False,  True, False]],
      fill_value=1e+20)

    """

    # handle the single-argument case
    missing = (x is _NoValue, y is _NoValue).count(True)