Laguerre.trim(tol=0) [source] Remove trailing coefficients Remove trailing coefficients until a coefficient is reached whose absolute value greater than tol or the beginning of the series is reached. If all the coefficients would be removed the series is set to [0]. A new series instance is returned with the new coefficients. The current instance remains unchanged. Parameters: tol : non-negative number. All trailing coefficients less than tol will be removed. Returns: new_series : seri

New in version 1.6. Array Iterator The array iterator encapsulates many of the key features in ufuncs, allowing user code to support features like output parameters, preservation of memory layouts, and buffering of data with the wrong alignment or type, without requiring difficult coding. This page documents the API for the iterator. The iterator is named NpyIter and functions are named NpyIter_*. There is an introductory guide to array iteration which may be of interest for those using thi

RandomState.standard_t(df, size=None) Draw samples from a standard Student?s t distribution with df degrees of freedom. A special case of the hyperbolic distribution. As df gets large, the result resembles that of the standard normal distribution (standard_normal). Parameters: df : int Degrees of freedom, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single

generic.reshape() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

ndarray.__ipow__ x.__ipow__(y) <==> x**=y

numpy.random.chisquare(df, size=None) Draw samples from a chi-square distribution. When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing. Parameters: df : int Number of degrees of freedom. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn

ndarray.__rshift__ x.__rshift__(y) <==> x>>y

numpy.polysub(a1, a2) [source] Difference (subtraction) of two polynomials. Given two polynomials a1 and a2, returns a1 - a2. a1 and a2 can be either array_like sequences of the polynomials? coefficients (including coefficients equal to zero), or poly1d objects. Parameters: a1, a2 : array_like or poly1d Minuend and subtrahend polynomials, respectively. Returns: out : ndarray or poly1d Array or poly1d object of the difference polynomial?s coefficients. See also polyval, polydiv, pol

ndarray.__int__() <==> int(x)

record.conj()