numpy.ma.sort(a, axis=-1, kind='quicksort', order=None, endwith=True, fill_value=None)

RandomState.lognormal(mean=0.0, sigma=1.0, size=None) Draw samples from a log-normal distribution. Draw

RandomState.laplace(loc=0.0, scale=1.0, size=None) Draw samples from the Laplace or double exponential distribution

numpy.random.negative_binomial(n, p, size=None) Draw samples from a negative binomial distribution. Samples

numpy.issubclass_(arg1, arg2)

numpy.bmat(obj, ldict=None, gdict=None)

numpy.getbuffer(obj[, offset[, size]]) Create a buffer object from the given object referencing a slice of length size starting

RandomState.chisquare(df, size=None) Draw samples from a chi-square distribution. When df

numpy.ma.getmaskarray(arr)

class numpy.nditer