python - Why does numpy have a corresponding function for many ndarray methods?

Question

A few examples:

numpy.sum()
ndarray.sum()
numpy.amax()
ndarray.max()
numpy.dot()
ndarray.dot()

... and quite a few more. Is it to support some legacy code, or is there a better reason for that? And, do I choose only on the basis of how my code 'looks', or is one of the two ways better than the other?

I can imagine that one might want numpy.dot() to use reduce (e.g., reduce(numpy.dot, A, B, C, D)) but I don't think that would be as useful for something like numpy.sum().

Answer 1

As others have noted, the identically-named NumPy functions and array methods are often equivalent (they end up calling the same underlying code). One might be preferred over the other if it makes for easier reading.

However, in some instances the two behave different slightly differently. In particular, using the ndarray method sometimes emphasises the fact that the method is modifying the array in-place.

For example, np.resize returns a new array with the specified shape. On the other hand, ndarray.resize changes the shape of the array in-place. The fill values used in each case are also different.

Similarly, a.sort() sorts the array a in-place, while np.sort(a) returns a sorted copy.