To compute the product of an iterable with itself, specify the number of The input’s iterables are sorted, the product tuples are emitted in sorted This pattern creates a lexicographic ordering so that if The nested loops cycle like an odometer with the rightmost element advancing

Product(A, B) returns the same as ((x,y) for x in A for y in B). Roughly equivalent to nested for-loops in a generator expression. The number of items returned is n! / (n-r)! when 0 n. Multi-line report may list a name field on every third line).ĭef permutations ( iterable, r = None ): pool = tuple ( iterable ) n = len ( pool ) r = n if r is None else r for indices in product ( range ( n ), repeat = r ): if len ( set ( indices )) = r : yield tuple ( pool for i in indices ) Can be used to extract related fields fromĭata where the internal structure has been flattened (for example, a Unlike regular slicing, islice() does not support negative values for If start is None, then iteration starts at zero. If stop is None, then iterationĬontinues until the iterator is exhausted, if at all otherwise, it stops at the One which results in items being skipped. Non-zero, then elements from the iterable are skipped until start is reached.Īfterward, elements are returned consecutively unless step is set higher than Make an iterator that returns selected elements from the iterable. id )) def _grouper ( self, tgtkey, id ): while self. currvalue = object () def _iter_ ( self ): return self def _next_ ( self ): self. Is needed later, it should be stored as a list:Ĭlass groupby : # -> A B C D A B # -> AAAA BBB CC D def _init_ ( self, iterable, key = None ): if key is None : key = lambda x : x self. Object is advanced, the previous group is no longer visible. Because the source is shared, when the groupby() The returned group is itself an iterator that shares the underlying iterable That behavior differs from SQL’s GROUP BY which aggregates commonĮlements regardless of their input order. (which is why it is usually necessary to have sorted the data using the same keyįunction). Generates a break or new group every time the value of the key function changes The operation of groupby() is similar to the uniq filter in Unix. Generally, the iterable needs to already be sorted on Specified or is None, key defaults to an identity function and returns The key is a function computing a key value for each element. Make an iterator that returns consecutive keys and groups from the iterable. Has one more element than the input iterable.ĭef filterfalse ( predicate, iterable ): # filterfalse(lambda x: x%2, range(10)) -> 0 2 4 6 8 if predicate is None : predicate = bool for x in iterable : if not predicate ( x ): yield x itertools. 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(starmap(operator.mul, zip(vec1, vec2, strict=True))).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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