np.where()函数的详细使用介绍

看了很多博客，见到的没有一个给说清楚了，这里做个记录。这是python提供的函数说明：

Help on function where in module numpy:

where(...)
    where(condition, [x, y])
    
    Return elements chosen from `x` or `y` depending on `condition`.
    
    .. note::
        When only `condition` is provided, this function is a shorthand for
        ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be
        preferred, as it behaves correctly for subclasses. 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
        Values from which to choose. `x`, `y` and `condition` need to be
        broadcastable to some shape.
    
    Returns
    -------
    out : ndarray
        An array with elements from `x` where `condition` is True, and elements
        from `y` elsewhere.
    
    See Also
    --------
    choose
    nonzero : The function that is called when x and y are omitted
    
    Notes
    -----
    If all the arrays are 1-D, `where` is equivalent to::
    
        [xv if c else yv
         for c, xv, yv in zip(condition, x, y)]
    
    Examples
    --------
    >>> a = np.arange(10)
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    >>> np.where(a < 5, a, 10*a)
    array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])
    
    This can be used on multidimensional arrays too:
    
    >>> np.where([[True, False], [True, True]],
    ...          [[1, 2], [3, 4]],
    ...          [[9, 8], [7, 6]])
    array([[1, 8],
           [3, 4]])
    
    The shapes of x, y, and the condition are broadcast together:
    
    >>> x, y = np.ogrid[:3, :4]
    >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
    array([[10,  0,  0,  0],
           [10, 11,  1,  1],
           [10, 11, 12,  2]])
    
    >>> a = np.array([[0, 1, 2],
    ...               [0, 2, 4],
    ...               [0, 3, 6]])
    >>> np.where(a < 4, a, -1)  # -1 is broadcast
    array([[ 0,  1,  2],
           [ 0,  2, -1],
           [ 0,  3, -1]])

就是说，当只传入condition参数时，等价于np.nonzero()

Help on function nonzero in module numpy:

nonzero(a)
    Return the indices of the elements that are non-zero.
    
    Returns a tuple of arrays, one for each dimension of `a`,
    containing the indices of the non-zero elements in that
    dimension. The values in `a` are always tested and returned in
    row-major, C-style order.
    
    To group the indices by element, rather than dimension, use `argwhere`,
    which returns a row for each non-zero element.
    
    .. note::
    
       When called on a zero-d array or scalar, ``nonzero(a)`` is treated
       as ``nonzero(atleast1d(a))``.
    
       .. deprecated:: 1.17.0
    
          Use `atleast1d` explicitly if this behavior is deliberate.
    
    Parameters
    ----------
    a : array_like
        Input array.
    
    Returns
    -------
    tuple_of_arrays : tuple
        Indices of elements that are non-zero.
    
    See Also
    --------
    flatnonzero :
        Return indices that are non-zero in the flattened version of the input
        array.
    ndarray.nonzero :
        Equivalent ndarray method.
    count_nonzero :
        Counts the number of non-zero elements in the input array.
    
    Notes
    -----
    While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
    recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
    will correctly handle 0-d arrays.
    
    Examples
    --------
    >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
    >>> x
    array([[3, 0, 0],
           [0, 4, 0],
           [5, 6, 0]])
    >>> np.nonzero(x)
    (array([0, 1, 2, 2]), array([0, 1, 0, 1]))
    
    >>> x[np.nonzero(x)]
    array([3, 4, 5, 6])
    >>> np.transpose(np.nonzero(x))
    array([[0, 0],
           [1, 1],
           [2, 0],
           [2, 1]])
    
    A common use for ``nonzero`` is to find the indices of an array, where
    a condition is True.  Given an array `a`, the condition `a` > 3 is a
    boolean array and since False is interpreted as 0, np.nonzero(a > 3)
    yields the indices of the `a` where the condition is true.
    
    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> a > 3
    array([[False, False, False],
           [ True,  True,  True],
           [ True,  True,  True]])
    >>> np.nonzero(a > 3)
    (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
    
    Using this result to index `a` is equivalent to using the mask directly:
    
    >>> a[np.nonzero(a > 3)]
    array([4, 5, 6, 7, 8, 9])
    >>> a[a > 3]  # prefer this spelling
    array([4, 5, 6, 7, 8, 9])
    
    ``nonzero`` can also be called as a method of the array.
    
    >>> (a > 3).nonzero()
    (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))