numpy.add¶

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:

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 the ufunc docs.
Returns:	add : ndarray or scalar The sum of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars.

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 the ufunc docs.

Returns:

add : ndarray or scalar

The sum of x1 and x2, element-wise. Returns 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.]])