Relationship between SciPy and NumPy

Question

SciPy appears to provide most (but not all [1]) of NumPy's functions in its own namespace. In other words, if there's a function named numpy.foo, there's almost certainly a scipy.foo. Most of the time, the two appear to be exactly the same, oftentimes even pointing to the same function object.

Sometimes, they're different. To give an example that came up recently:

• numpy.log10 is a ufunc that returns NaNs for negative arguments;

• scipy.log10 returns complex values for negative arguments and doesn't appear to be a ufunc.

The same can be said about log, log2 and logn, but not about log1p [2].

On the other hand, numpy.exp and scipy.exp appear to be different names for the same ufunc. This is also true of scipy.log1p and numpy.log1p.

Another example is numpy.linalg.solve vs scipy.linalg.solve. They're similar, but the latter offers some additional features over the former.

Why the apparent duplication? If this is meant to be a wholesale import of numpy into the scipy namespace, why the subtle differences in behaviour and the missing functions? Is there some overarching logic that would help clear up the confusion?

[1] numpy.min, numpy.max, numpy.abs and a few others have no counterparts in the scipy namespace.

[2] Tested using NumPy 1.5.1 and SciPy 0.9.0rc2.

Answer 1

From the SciPy Reference Guide:

all of the Numpy functions have been subsumed into the scipy namespace so that all of those functions are available without additionally importing Numpy.

The intention is for users not to have to know the distinction between the scipy and numpy namespaces, though apparently you've found an exception.