numpy.negative

numpy.divide

numpy.multiply¶

`numpy.``multiply`(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'multiply'>

Multiply arguments element-wise.

Parameters: x1, x2 : array_like Input arrays to be multiplied. If `x1.shape != x2.shape`, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default `out=None`, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the ufunc docs. y : ndarray The product of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.

Notes

Equivalent to x1 * x2 in terms of array broadcasting.

Examples

```>>> np.multiply(2.0, 4.0)
8.0
```
```>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.multiply(x1, x2)
array([[  0.,   1.,   4.],
[  0.,   4.,  10.],
[  0.,   7.,  16.]])
```