numpy.add — NumPy v1.15 Manual (original) (raw)

numpy. add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, _extobj_]) = <ufunc 'add'>¶

Add arguments element-wise.

Parameters:	x1, x2 : array_like The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). 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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.
Returns:	add : ndarray or scalar The sum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.

Parameters:

x1, x2 : array_like The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). 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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

add : ndarray or scalar The sum 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.add(1.0, 4.0) 5.0 x1 = np.arange(9.0).reshape((3, 3)) x2 = np.arange(3.0) np.add(x1, x2) array([[ 0., 2., 4.], [ 3., 5., 7.], [ 6., 8., 10.]])