numpy.polynomial.polynomial.polydomain = array([-1, 1])

generic.imag imaginary part of scalar

apply_along_axis(func1d, axis, arr, *args, ...) Apply a function to 1-D slices along the given axis. apply_over_axes(func, a, axes) Apply a function repeatedly over multiple axes. vectorize(pyfunc[, otypes, doc, excluded, cache]) Generalized function class. frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. piecewise(x, condlist, funclist, *args, **kw) Evaluate a piecewise-defined function.

nditer.remove_multi_index() When the ?multi_index? flag was specified, this removes it, allowing the internal

numpy.polynomial.polynomial.polyroots(c)

numpy.testing.assert_array_less(x, y, err_msg='', verbose=True)

numpy.ma.masked_greater_equal(x, value, copy=True)

numpy.ma.std(self, axis=None, dtype=None, out=None, ddof=0) = Compute the standard deviation along the specified axis. Returns

recarray.sort(axis=-1, kind='quicksort', order=None) Sort an array, in-place.

numpy.random.sample(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the