numpy.polynomial.hermite_e.hermemul(c1, c2)

chararray.rsplit(sep=None, maxsplit=None)

class numpy.polynomial.legendre.Legendre(coef, domain=None, window=None)

MaskedArray.__imod__ x.__imod__(y) <==> x%=y

class numpy.polynomial.laguerre.Laguerre(coef, domain=None, window=None)

numpy.polynomial.hermite_e.hermevander3d(x, y, z, deg)

numpy.ma.corrcoef(x, y=None, rowvar=True, bias=, allow_masked=True, ddof=)

numpy.apply_along_axis(func1d, axis, arr, *args, **kwargs)

numpy.find_common_type(array_types, scalar_types)

There is a general need for looping over not only functions on scalars but also over functions on vectors (or arrays). This concept is realized in Numpy by generalizing the universal functions (ufuncs)