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 elementwise.
 Parameters
 x1, x2array_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). outndarray, 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 wherearray_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 keywordonly arguments, see the ufunc docs.
 Returns
 yndarray
The product of x1 and x2, elementwise. 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.]])
The
*
operator can be used as a shorthand fornp.multiply
on ndarrays.>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 * x2 array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]])