generic.var() 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

record.min() 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

numpy.prod(a, axis=None, dtype=None, out=None, keepdims=False) [source] Return the product of array elements over a given axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the elements in the input array. If axis is negative it counts from the last to the first axis. New in version 1.7.0. If axis is a tuple of ints, a product is performed

generic.transpose() 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

class numpy.busdaycalendar [source] A business day calendar object that efficiently stores information defining valid days for the busday family of functions. The default valid days are Monday through Friday (?business days?). A busdaycalendar object can be specified with any set of weekly valid days, plus an optional ?holiday? dates that always will be invalid. Once a busdaycalendar object is created, the weekmask and holidays cannot be modified. New in version 1.7.0. Parameters: weekma

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

ndarray.__add__ x.__add__(y) <==> x+y

numpy.polynomial.chebyshev.chebzero = array([0])

class numpy.generic [source] Base class for numpy scalar types. Class from which most (all?) numpy scalar types are derived. For consistency, exposes the same API as ndarray, despite many consequent attributes being either ?get-only,? or completely irrelevant. This is the class from which it is strongly suggested users should derive custom scalar types. Attributes T transpose base base object data pointer to start of data dtype get array data-descriptor flags integer value of flags flat a 1

numpy.polynomial.polynomial.polyval(x, c, tensor=True) [source] Evaluate a polynomial at points x. If c is of length n + 1, this function returns the value The parameter x is converted to an array only if it is a tuple or a list, otherwise it is treated as a scalar. In either case, either x or its elements must support multiplication and addition both with themselves and with the elements of c. If c is a 1-D array, then p(x) will have the same shape as x. If c is multidimensional, then t