I think this is better:

 >>> x=[[1, 2],[3, 4],[5, 6]]                                                   
>>> sum(sum(x,[]))                                                             
21

Check out the documentation for numpy.sum, paying particular attention to the axis parameter. To sum over columns:

>>> import numpy as np
>>> a = np.arange(12).reshape(4,3)
>>> a.sum(axis=0)
array([18, 22, 26])

Or, to sum over rows:

>>> a.sum(axis=1)
array([ 3, 12, 21, 30])

Other aggregate functions, like numpy.mean, numpy.cumsum and numpy.std, e.g., also take the axis parameter.

From the Tentative Numpy Tutorial:

Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array:

Thanks for asking your question, @TMOTTM!

The way NumPy's sum semantics work are documented in the NumPy manual.

To summarize the manual while injecting my own understanding:

1. arr.sum() called without an axis argument simply sums up everything in the array. It is the most straightforward semantic operation to implement.
2. arr.sum(axis=0) will collapse axis 0 (the first axis) while summing.
3. arr.sum(axis=k) will collapse axis k while performing a summation.

Canonically, axis 0 is semantically recognized as the row-wise axis, axis 1 is the column-wise axis, and axis 2 is the depth-wise axis, and anything higher goes into hypercubes which are not easily described in words.

Made concrete:

1. In a 2D array, to collapse the rows (i.e. sum column-wise), do arr.sum(axis=0).
2. In a 2D array, to collapse the columns (i.e. sum row-wise), do arr.sum(axis=1).

At the end of the day, point 3 is the one you want to remember: reason carefully about which axis you wish to collapse, and you'll never go wrong!

What you are looking for is numpy.add

import numpy as np
arr1 = np.array([[-0.31326169, -0., -3.23995333],[-0.26328247, -0., -0.64439666]])
arr2 = np.array([[-0., -0.28733533, -0.],[-0., -2.12692801, -0.]])
arr3=np.add(arr1,arr2)
print(arr3)

Output

[[-0.31326169 -0.28733533 -3.23995333]
 [-0.26328247 -2.12692801 -0.64439666]]