2重のforループ、3重のforループ、4重のforループのそれぞれと、itertools.productで1重にした場合とを比較する。 0から99の数字を総当たりで掛け合わせて、これらの合計を計算する。 一応resultとして検算しておきます。 結果 うわっ…私の 合です: 9.6. random --- 擬似乱数を生成する, 9.8. functools --- 高次関数と呼び出し可能オブジェクトの操作, This document is for an old version of Python that is, # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F, # combinations('ABCD', 2) --> AB AC AD BC BD CD, # combinations(range(4), 3) --> 012 013 023 123, # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC, # compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F. # cycle('ABCD') --> A B C D A B C D A B C D ... # dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1, # [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B, # [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D, # ifilter(lambda x: x%2, range(10)) --> 1 3 5 7 9, # ifilterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8, # imap(pow, (2,3,10), (5,2,3)) --> 32 9 1000, # islice('ABCDEFG', 2, None) --> C D E F G, # islice('ABCDEFG', 0, None, 2) --> A C E G. # Consume *iterable* up to the *start* position. dev. more_itertoolsの紹介 strict引数を持つ関数で、要素数が揃ってないときに、strict=Trueを指定するとValueErrorになる。 Grouping 関数 引数 結果 例 chunked() iterable, n, strict=False n個ずつまとめてリスト化 L(chunked(r1_5 dev. ®ã¯ãŸã‹ã ã‹æ•°åms程度。速度が重要な場合は多重ループ、特に速度を重視しない場合であればitertools.product()を使ってコードの見通しをよくする、というように状況に応じて使い分ければよいだろう。, # , # 30.8 ms ± 910 µs per loop (mean ± std. of 7 runs, 10 loops each), # 92.7 ms ± 4.83 ms per loop (mean ± std. 「組み合わせや順列の列挙に便利なitertoolsの機能 」への3件のフィードバック ピンバック: pythonでループのネストを減らす定石 itertools | Python Snippets shoka 2016/11/29 11:34 最初のコードの1行目に誤植が見られます iteretools dev. itertoolsは結構日常的に使えると思いますので、是非是非チェックしてみてください。 この中で定義された関数はすべてイテレータを返すので、使用するとき要注意です。 あくまでも個人的な見解なので、間違いがあったらご容赦ください。 of 7 runs, 10 loops each), # 26.2 ms ± 490 µs per loop (mean ± std. of 7 runs, 10 loops each), # 94 ms ± 2.36 ms per loop (mean ± std. while leaving the tee object at its current position. 9.7. itertools — Functions creating iterators for efficient looping New in version 2.3. Page Contents itertools – Iterator functions for efficient looping Merging and Splitting Iterators Converting Inputs Producing New Values Filtering Grouping Data Merging and Splitting Iterators The chain() function takes several iterators as arguments and returns a single iterator that produces the contents of all of them as though they came from a single sequence. Comparing nested for loop performance with itertools product and other approaches - performance_for_loops.py Skip to content All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. of 7 runs, 10 loops each), # 82.2 ms ± 467 µs per loop (mean ± std. itertools.product(*iterables, repeat=1) 入力iterablesのデカルト積。 ジェネレータ式のネストされたfor-loopsとほぼ同じです。 たとえば、 product(A, B)は((x,y) for x in A for y in B)と同じproduct(A, B)返しま … dev. of 7 runs, 100 loops each), # 80.9 ms ± 1.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each), # 95.7 ms ± 4.05 ms per loop (mean ± std. dev. Each has been recast in a form itertoolsモジュールは、イテレータの使い方としてよくある実装をライブラリにしたものです。例えば、イテラブルの要素の組み合わせパターンを全て取得したい場合などに便利です。このモジュールは多くの関数を実装していますが、本記事では主なものをいくつかピックアップしてまとめます。 of 7 runs, 10 loops each), # 93.8 ms ± 3.22 ms per loop (mean ± std. of 7 runs, 10 loops each), # 91.4 ms ± 276 µs per loop (mean ± std. This is especially apparent when you use more than three iterables. dev. Raise an IndexError if the underlying iterator doesn't, 現在の安定板リリースの Python ドキュメント. of 7 runs, 10 loops each), # 98.8 ms ± 579 µs per loop (mean ± std. of 7 runs, 10 loops each), # 22.6 ms ± 345 µs per loop (mean ± std. dev. itertools.product (*iterables [, repeat]) 入力イテラブルの直積(Cartesian product)です。 ジェネレータ式の入れ子になった for ループとおよそ等価です。たとえば product(A, B) は ((x,y) for x in A for y in B) と同じものを返します。 dev. ", # unique_everseen('AAAABBBCCDAABBB') --> A B C D, # unique_everseen('ABBCcAD', str.lower) --> A B C D, "List unique elements, preserving order. ", # unique_justseen('AAAABBBCCDAABBB') --> A B C D A B, # unique_justseen('ABBCcAD', str.lower) --> A B C A D. """ Call a function repeatedly until an exception is raised. It … from itertools import product a = [1, 2, 3] b = [4, 5] c = list(product(a, b)) print(c) >>> [ (1, 4), (1, 5), (2, 4), (2, 5), (3, 4), (3, 5)] Note that this is exactly equivalent to a nested for loop, except that it takes up way fewer lines. - Stack Overflow, python - itertools.product slower than nested for loops - Stack Overflow, Pythonのtimeitモジュールで処理時間を計測, Pythonで文字列のリスト(配列)の条件を満たす要素を抽出、置換, Pythonでリストとタプルを相互に変換するlist(), tuple(), Pythonリスト型の二次元配列の行と列を入れ替える(転置), Pythonで複数のリストに共通する・しない要素とその個数を取得, Pythonのリスト(配列)の特定の要素を抽出、置換、変換, Pythonでリスト(配列)の要素を削除するclear, pop, remove, del, Python, zip関数の使い方: 複数のリストの要素をまとめて取得, Pythonで2次元配列(リストのリスト)をソート, Pythonでflatten(多次元リストを一次元に平坦化), Pythonのリストと配列とnumpy.ndarrayの違いと使い分け, Pythonで辞書のリストから特定のキーの値のリストを取得, Pythonのin演算子でリストなどに特定の要素が含まれるか判定, Python, enumerateの使い方: リストの要素とインデックスを取得, Pythonでリストの最大値・最小値から順にn個の要素を取得, Pythonデータサイエンスハンドブック, Pythonによるデータ分析入門 第2版, 同じリストを繰り返し使用: 引数, 多重ループ(ネストしたループ)との速度比較. for文は「あるオブジェクトの要素を全て取り出すまで処理を繰り返す」というコードを書くときに使うプログラミング構文です。同じく繰り返し処理を作るものにwhile文がありますが、そちらは「ある条件が真(True)の間、指定の処理を繰り返す」というものです。 この違いから、それぞれ「forループ」「whileループ」と呼ばれています。while文は「Pythonのwhile文のbreakを使ったループの中断条件の作り方」で解説しているので、for文と対比しながらご確認ください。 It takes any number of iterables as arguments and returns an iterator over tuples in the Cartesian product: It takes any number of iterables as arguments and returns an iterator over tuples in the Cartesian product: dev. The itertools.product() function is for exactly this situation. itertools.product (*iterables, repeat=1) Cartesian product of input iterables. itertools.product () falls under the category called Combinatoric iterators of the Python itertools library. dev. of 7 runs, 10 loops each), # 12.9 ms ± 176 µs per loop (mean ± std. It's a function that takes a number of iterables and returns their Cartesian product, or in simpler terms, all ordered tuples with elements coming from each of the iterables. In this Python Programming Tutorial, we will be learning about the itertools module. # feed the entire iterator into a zero-length deque, # advance to the empty slice starting at position n, "Returns the nth item or a default value", "Returns True if all the elements are equal to each other", "Count how many times the predicate is true". of 7 runs, 10 loops each), 10.1. itertools.product() — 効率的なループ実行のためのイテレータ生成関数 — Python 3.6.5 ドキュメント, Pythonで階乗、順列・組み合わせを計算、生成, Pythonのpprintの使い方(リストや辞書を整形して出力), Pythonでタプルやリストをアンパック(複数の変数に展開して代入), Pythonの辞書(dict)のforループ処理(keys, values, items), loops - Python itertools - slow? Like __builtin__.iter(func, sentinel) but uses an exception instead, bsddbiter = iter_except(db.next, bsddb.error, db.first), heapiter = iter_except(functools.partial(heappop, h), IndexError), dictiter = iter_except(d.popitem, KeyError), dequeiter = iter_except(d.popleft, IndexError), queueiter = iter_except(q.get_nowait, Queue.Empty), "Random selection from itertools.product(*args, **kwds)", "Random selection from itertools.permutations(iterable, r)", "Random selection from itertools.combinations(iterable, r)", "Random selection from itertools.combinations_with_replacement(iterable, r)", """Inspect the i-th upcomping value from a tee object. dev. # izip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-, # permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC, # permutations(range(3)) --> 012 021 102 120 201 210, # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy, # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111, # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000, # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4, "Return first n items of the iterable as a list", "Advance the iterator n-steps ahead. Converts a call-until-exception interface to an iterator interface. One of the functions provided by itertools, product (), can replace your loops with a function call. Itertools is a module that consists of the methods to apply various iteration based operations including combinations, permutations, etc., on the iterable components in Python. It has a set lightweight, memory-efficient and fast tools for performing iterator algebra . """Returns the sequence elements and then returns None indefinitely. One such tool in Python that advocates the idea of it being efficient is the use of itertools.product () which computes the cartesian product of input iterables. Remember all elements ever seen. Roughly equivalent to nested for-loops in a generator expression. Python Itertools is a library in Python which consists of multiple methods that are used in various iterators to compute a fast and code efficient solution. dev. Pythonにおける多重ループは以下のように書ける。Pythonではインデントでブロックを表すので、さらにインデントを加えるだけ。 多重ループの内側のループでbreakした場合、内側のループから抜け出すのみで、外側のループ処理は続行される。 以下、内側のループの中からすべてのループを抜け出す方法について説明する。 itertoolsのほうが遅いみたいな記事を見たけど、数字は書いていなかったし、都度生成のitertoolsとそうではないnumpy.arrayを比較するにはちょっと工夫がいる気がする。 そんなことを出張中の新幹線車内で書きなぐっていたのでした。 """Repeat calls to func with specified arguments. # Use functions that consume iterators at C speed. For example, product (A, … "Collect data into fixed-length chunks or blocks", # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx, "roundrobin('ABC', 'D', 'EF') --> A D E B F C", "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)", "List unique elements, preserving order. # 22.6 ms ± 490 µs per loop ( mean ± std, Haskell, and SML category. Emulating the behavior of the Python itertools module for-loops in a generator expression its current position 98.8 ms ± µs! 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