add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Notes

Equivalent to x1 + :None:None:`x2` in terms of array broadcasting.

Parameters

x1, x2 : array_like

The arrays to be added. 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 :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

add : ndarray or scalar

The sum of x1 and :None:None:`x2`, element-wise. This is a scalar if both x1 and :None:None:`x2` are scalars.

Add arguments element-wise.

Examples

This example is valid syntax, but we were not able to check execution

>>> np.add(1.0, 4.0)
5.0

This example is valid syntax, but we were not able to check execution

>>> 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.]])

The + operator can be used as a shorthand for np.add on ndarrays.

This example is valid syntax, but we were not able to check execution

>>> x1 = np.arange(9.0).reshape((3, 3))
... x2 = np.arange(3.0)
... x1 + x2
array([[ 0.,  2.,  4.],
       [ 3.,  5.,  7.],
       [ 6.,  8., 10.]])

See :

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GitHub : /numpy/ma/core.py#None
type: <class 'numpy.ma.core._MaskedBinaryOperation'>
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