record.cumprod() 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.nper(rate, pmt, pv, fv=0, when='end') [source] Compute the number of periodic payments. Parameters: rate : array_like Rate of interest (per period) pmt : array_like Payment pv : array_like Present value fv : array_like, optional Future value when : {{?begin?, 1}, {?end?, 0}}, {string, int}, optional When payments are due (?begin? (1) or ?end? (0)) Notes The number of periods nper is computed by solving the equation: fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)

HermiteE.deriv(m=1) [source] Differentiate. Return a series instance of that is the derivative of the current series. Parameters: m : non-negative int Find the derivative of order m. Returns: new_series : series A new series representing the derivative. The domain is the same as the domain of the differentiated series.

ndarray.getfield(dtype, offset=0) Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters

numpy.meshgrid(*xi, **kwargs) [source] Return coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,..., xn. Changed in version 1.9: 1-D and 0-D cases are allowed. Parameters: x1, x2,..., xn : array_like 1-D arrays representing the coordinates of a grid. indexing : {?xy?, ?ij?}, optional Cartesian (?xy?, default) or matrix (?ij?) indexing of output. S

numpy.ma.mask_cols(a, axis=None) [source] Mask columns of a 2D array that contain masked values. This function is a shortcut to mask_rowcols with axis equal to 1. See also mask_rowcols Mask rows and/or columns of a 2D array. masked_where Mask where a condition is met. Examples >>> import numpy.ma as ma >>> a = np.zeros((3, 3), dtype=np.int) >>> a[1, 1] = 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = ma.masked_equa

numpy.reciprocal(x[, out]) = Return the reciprocal of the argument, element-wise. Calculates 1/x. Parameters: x : array_like Input array. Returns: y : ndarray Return array. Notes Note This function is not designed to work with integers. For integer arguments with absolute value larger than 1 the result is always zero because of the way Python handles integer division. For integer zero the result is an overflow. Examples >>> np.reciprocal(2.) 0.5 >>> np.reciproca

numpy.zeros_like(a, dtype=None, order='K', subok=True) [source] Return an array of zeros with the same shape and type as a given array. Parameters: a : array_like The shape and data-type of a define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. New in version 1.6.0. order : {?C?, ?F?, ?A?, or ?K?}, optional Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if a is Fortran c

numpy.ma.common_fill_value(a, b) [source] Return the common filling value of two masked arrays, if any. If a.fill_value == b.fill_value, return the fill value, otherwise return None. Parameters: a, b : MaskedArray The masked arrays for which to compare fill values. Returns: fill_value : scalar or None The common fill value, or None. Examples >>> x = np.ma.array([0, 1.], fill_value=3) >>> y = np.ma.array([0, 1.], fill_value=3) >>> np.ma.common_fill_value(x,

matrix.nonzero() Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero equivalent function