# numpy.where¶

numpy.where(condition[, x, y])

Return elements, either from x or y, depending on condition.

If only condition is given, return condition.nonzero().

Parameters: condition : array_like, bool When True, yield x, otherwise yield y. x, y : array_like, optional Values from which to choose. x and y need to have the same shape as condition. out : ndarray or tuple of ndarrays If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere. If only condition is given, return the tuple condition.nonzero(), the indices where condition is True.

Notes

If x and y are given and input arrays are 1-D, where is equivalent to:

`[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]`

Examples

```>>> np.where([[True, False], [True, True]],
...          [[1, 2], [3, 4]],
...          [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
```
```>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
```
```>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )]               # Note: result is 1D.
array([ 4.,  5.,  6.,  7.,  8.])
>>> np.where(x < 5, x, -1)               # Note: broadcasting.
array([[ 0.,  1.,  2.],
[ 3.,  4., -1.],
[-1., -1., -1.]])
```

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