What's the difference between "numpy.add(a,b)" and "a+b"?

Question

Is there any difference between numpy.add(a,b) and a+b when adding two ndarrays a and b? The documentation says that numpy.add is the "Equivalent to x1 + x2 in terms of array broadcasting.". But I don't unserstand what this means, since numpy.add(numpy.array([1,2,3]),4) also works.

Answer 1

In Python syntax, a+b is translated to a.__add__(b). a.__add__ is a method that implements addition for objects of type a. A number has such a method, a list does as well ([1,3]+[4]) and so does a string ('abc'+'d').

numpy has implemented __add__ (and other standard __... methods) for its ndarray class (at least for the numeric dtypes).

That's all standard Python and numpy and been around for ever.

np.add is a ufunc. Look at its doc - see the out parameter, and the Binary ufuncs: section. It's a function, and has some methods like reduce, reduceat, etc that a.__add__ (and +) does not.

If you have two arrays or numbers and you want to sum them, the natural thing is to use +, a+b. np.add is useful in some special cases

Compare, for example, what happens to two lists:

In [16]: [1,2,3]+[4]
Out[16]: [1, 2, 3, 4]     # default list concatenation
In [17]: np.add([1,2,3],[4])
Out[17]: array([5, 6, 7])   # convert lists to arrays and sum

or an example using 2d broadcasting:

In [19]: np.add([[1],[2],[3]],[4,1])
Out[19]: 
array([[5, 2],
       [6, 3],
       [7, 4]])
In [20]: np.array([1,2,3])[:,None]+np.array([4,1])
Out[20]: 
array([[5, 2],
       [6, 3],
       [7, 4]])

And your example:

In [21]: numpy.add(numpy.array([1,2,3]),4)
Out[21]: array([5, 6, 7])
In [22]: numpy.array([1,2,3])+4
Out[22]: array([5, 6, 7])

"Equivalent to x1 + x2 in terms of array broadcasting." means, they both work and do the same thing.

broadcasting is another subject.

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The @ operator and np.matmul parallel isn't quite the same. The @ operator is a recent addition to the Python interpreter. It is translated to a call to the __matmul__ method - if defined. New numpy versions have such a definition. But the method is not defined for Python numbers or lists. There is also a function version, np.matmul, referencing the same code. There is also a x.dot and np.dot pairing, but no Python recognized operator or x.__dot__.