SciPy SVD vs. Numpy SVD

Question

Both SciPy and Numpy have built in functions for singular value decomposition (SVD). The commands are basically scipy.linalg.svd and numpy.linalg.svd. What is the difference between these two? Is any of them better than the other one?

Answer 1

Apart from the error checking, the actual work seems to be done within lapack both with numpy and scipy.

Without having done any benchmarking, I guess the performance should be identical.