numpy.polynomial.polynomial.polygrid2d(x, y, c) [source] Evaluate a 2-D polynomial on the Cartesian product of x and y. This function returns the values: where the points (a, b) consist of all pairs formed by taking a from x and b from y. The resulting points form a grid with x in the first dimension and y in the second. The parameters x and y are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x and y or their eleme

The standard array can have 24 different data types (and has some support for adding your own types). These data types all have an enumerated type, an enumerated type-character, and a corresponding array scalar Python type object (placed in a hierarchy). There are also standard C typedefs to make it easier to manipulate elements of the given data type. For the numeric types, there are also bit-width equivalent C typedefs and named typenumbers that make it easier to select the precision desired

nditer.debug_print() Print the current state of the nditer instance and debug info to stdout.

generic.dtype get array data-descriptor

MaskedArray.__rand__ x.__rand__(y) <==> y&x

record.nbytes length of item in bytes

numpy.polynomial.hermite.hermfit(x, y, deg, rcond=None, full=False, w=None) [source] Least squares fit of Hermite series to data. Return the coefficients of a Hermite series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in

RandomState.normal(loc=0.0, scale=1.0, size=None) Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [R179], is often called the bell curve because of its characteristic shape (see the example below). The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a

chararray.decode(encoding=None, errors=None) [source] Calls str.decode element-wise. See also char.decode

MaskedArray.__ge__ x.__ge__(y) <==> x>=y