MaskedArray.__long__() <==> long(x)

chararray.startswith(prefix, start=0, end=None) [source] Returns a boolean array which is True where the string element in self starts with prefix, otherwise False. See also char.startswith

numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None) [source] Estimate a covariance matrix, given data and weights. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, , then the covariance matrix element is the covariance of and . The element is the variance of . See the notes for an outline of the algorithm. Parameters: m : array_like A 1-D or 2-D array containing multiple variables and observatio

MaskedArray.__int__() [source] Convert to int.

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