# numpy.where¶

`numpy.``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
conditionarray_like, bool

Where True, yield x, otherwise yield y.

x, yarray_like

Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns
outndarray

An array with elements from x where condition is True, and elements from y elsewhere.

`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]])
```