flatiter.copy() Get a copy of the iterator as a 1-D array. Examples >>> x = np.arange(6).reshape(2, 3) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> fl = x.flat >>> fl.copy() array([0, 1, 2, 3, 4, 5])

flatiter.next x.next() -> the next value, or raise StopIteration

flatiter.coords An N-dimensional tuple of current coordinates. Examples >>> x = np.arange(6).reshape(2, 3) >>> fl = x.flat >>> fl.coords (0, 0) >>> fl.next() 0 >>> fl.coords (0, 1)

Simple financial functions fv(rate, nper, pmt, pv[, when]) Compute the future value. pv(rate, nper, pmt[, fv, when]) Compute the present value. npv(rate, values) Returns the NPV (Net Present Value) of a cash flow series. pmt(rate, nper, pv[, fv, when]) Compute the payment against loan principal plus interest. ppmt(rate, per, nper, pv[, fv, when]) Compute the payment against loan principal. ipmt(rate, per, nper, pv[, fv, when]) Compute the interest portion of a payment. irr(values) Retu

There is a general need for looping over not only functions on scalars but also over functions on vectors (or arrays). This concept is realized in Numpy by generalizing the universal functions (ufuncs). In regular ufuncs, the elementary function is limited to element-by-element operations, whereas the generalized version (gufuncs) supports ?sub-array? by ?sub-array? operations. The Perl vector library PDL provides a similar functionality and its terms are re-used in the following. Each general

dtype.__setstate__()

dtype.__reduce__()

dtype.subdtype Tuple (item_dtype, shape) if this dtype describes a sub-array, and None otherwise. The shape is the fixed shape of the sub-array described by this data type, and item_dtype the data type of the array. If a field whose dtype object has this attribute is retrieved, then the extra dimensions implied by shape are tacked on to the end of the retrieved array.

dtype.str The array-protocol typestring of this data-type object.

dtype.type The type object used to instantiate a scalar of this data-type.