mul )) # running product > list ( accumulate ( data, max )) # running maximum # Amortize a 5% loan of 1000 with 4 annual payments of 90 > cashflows = > list ( accumulate ( cashflows, lambda bal, pmt : bal * 1.05 + pmt )) # Chaotic recurrence relation > logistic_map = lambda x, _ : r * x * ( 1 - x ) > r = 3.8 > x0 = 0.4 > inputs = repeat ( x0, 36 ) # only the initial value is used > > data = > list ( accumulate ( data, operator. Has one more element than the input iterable. However, if the keyword argument initial is provided, theĪccumulation leads off with the initial value so that the output Usually, the number of elements output matches the input iterable. The default operation of addition, elements may be any addable That can be accepted as arguments to func. Elements of the input iterable may be any type If func is supplied, it should be a function Results of other binary functions (specified via the optional Make an iterator that returns accumulated sums, or accumulated

Streams of infinite length, so they should only be accessed by functions or The following module functions all construct and return iterators. R-length tuples, in sorted order, with repeated elementsĪA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD R-length tuples, in sorted order, no repeated elements R-length tuples, all possible orderings, no repeated elements Zip_longest('ABCD', 'xy', fillvalue='-') -> Ax By C- D-Ĭartesian product, equivalent to a nested for-loop It1, it2, … itn splits one iterator into n Seq, seq, starting when pred failsĮlements of seq where pred(elem) is falseįilterfalse(lambda x: x%2, range(10)) -> 0 2 4 6 8 Iterators terminating on the shortest input sequence:Ĭom_iterable() -> A B C D E FĬompress('ABCDEF', ) -> A C E F Sum(map(operator.mul, vector1, vector2)).Įlem, elem, elem, … endlessly or up to n times Operator can be mapped across two vectors to form an efficient dot-product: These tools and their built-in counterparts also work well with the high-speedįunctions in the operator module. The same effect can be achieved in Pythonīy combining map() and count() to form map(f, count()).

Together, they form an “iteratorĪlgebra” making it possible to construct specialized tools succinctly andįor instance, SML provides a tabulation tool: tabulate(f) which produces a The module standardizes a core set of fast, memory efficient tools that are This module implements a number of iterator building blocks inspiredīy constructs from APL, Haskell, and SML. Itertools - Functions creating iterators for efficient looping ¶

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