numpy.busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None) First adjusts the date to fall on a valid day according to the roll rule, then applies offsets to the given dates counted in valid days. New in version 1.7.0. Parameters: dates : array_like of datetime64[D] The array of dates to process. offsets : array_like of int The array of offsets, which is broadcast with dates. roll : {?raise?, ?nat?, ?forward?, ?following?, ?backward?

record.strides tuple of bytes steps in each dimension

MaskedArray.__copy__([order]) Return a copy of the array. Parameters: order : {?C?, ?F?, ?A?}, optional If order is ?C? (False) then the result is contiguous (default). If order is ?Fortran? (True) then the result has fortran order. If order is ?Any? (None) then the result has fortran order only if the array already is in fortran order.

numpy.require(a, dtype=None, requirements=None) [source] Return an ndarray of the provided type that satisfies requirements. This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes). Parameters: a : array_like The object to be converted to a type-and-requirement-satisfying array. dtype : data-type The required data-type. If None preserve the current dtype. If your application requires the data to be in nati

numpy.polynomial.polynomial.polyfromroots(roots) [source] Generate a monic polynomial with given roots. Return the coefficients of the polynomial where the r_n are the roots specified in roots. If a zero has multiplicity n, then it must appear in roots n times. For instance, if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, then roots looks something like [2, 2, 2, 3, 3]. The roots can appear in any order. If the returned coefficients are c, then The coefficient

numpy.ma.std(self, axis=None, dtype=None, out=None, ddof=0) = Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. Parameters: a : array_like Calculate the standard deviation of these values. axis : None or int or tuple of ints, optional Axis or axes along which the standard deviat

nditer.iternext() Check whether iterations are left, and perform a single internal iteration without returning the result. Used in the C-style pattern do-while pattern. For an example, see nditer. Returns: iternext : bool Whether or not there are iterations left.

MaskedArray.take(indices, axis=None, out=None, mode='raise') [source]

numpy.linalg.eigvals(a) [source] Compute the eigenvalues of a general matrix. Main difference between eigvals and eig: the eigenvectors aren?t returned. Parameters: a : (..., M, M) array_like A complex- or real-valued matrix whose eigenvalues will be computed. Returns: w : (..., M,) ndarray The eigenvalues, each repeated according to its multiplicity. They are not necessarily ordered, nor are they necessarily real for real matrices. Raises: LinAlgError If the eigenvalue computatio

generic.copy() 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