itertools
— Funções que criam iteradores para laços eficientes¶
Esse módulo implementa diversos blocos de instruções com iteradores, inspirados por construções de APL, Haskell, e SML. Cada uma foi adequadamente reformulada para Python.
Esse módulo padroniza um conjunto central de ferramentas rápidas e de uso eficiente da memória, que podem ser utilizadas sozinhas ou combinadas. Juntas, eles formam uma “álgebra de iteradores” tornando possível construir ferramentas sucintas e eficientes em Python puro.
Por exemplo, SML fornece uma ferramenta para tabulação: tabulate(f)
que produz uma sequência f(0), f(1), ...
. O mesmo efeito pode ser obtido em Python combinando map()
e count()
para formar map(f, count())
.
Essa ferramentas e suas equivalências embutidas também trabalham bem com as funções de alta velocidade do módulo operator
module. Por exemplo, o operador de multiplicação pode ser mapeado em dois vetores para criar um produto escalar eficiente: sum(starmap(operator.mul, zip(vec1, vec2, strict=True)))
.
Iteradores infinitos:
Iterador |
Argumentos |
Resultado |
Exemplo |
---|---|---|---|
[start[, step]] |
start, start+step, start+2*step, … |
|
|
p |
p0, p1, … ultimo elemento de p, p0, p1, … |
|
|
elem [,n] |
elem, elem, elem, … repete infinitamente ou até n vezes |
|
Iteradores terminando na sequencia de entrada mais curta:
Iterador |
Argumentos |
Resultado |
Exemplo |
---|---|---|---|
p [,func] |
p0, p0+p1, p0+p1+p2, … |
|
|
p, n |
(p0, p1, …, p_n-1), … |
|
|
p, q, … |
p0, p1, … último elemento de p, q0, q1, … |
|
|
iterável |
p0, p1, … último elemento de p, q0, q1, … |
|
|
data, selectors |
(d[0] if s[0]), (d[1] if s[1]), … |
|
|
predicate, seq |
seq[n], seq[n+1], começando quando o predicate falha |
|
|
predicate, seq |
elementos de seq onde predicate(elem) falha |
|
|
iterable[, key] |
sub-iteradores agrupados pelo valor de key(v) |
||
seq, [start,] stop [, step] |
elementos de seq[start:stop:step] |
|
|
iterável |
(p[0], p[1]), (p[1], p[2]) |
|
|
func, seq |
func(*seq[0]), func(*seq[1]), … |
|
|
predicate, seq |
seq[0], seq[1], até predicate falhar |
|
|
it, n |
n iteradores it independentes |
||
p, q, … |
(p[0], q[0]), (p[1], q[1]), … |
|
Iteradores combinatórios:
Iterador |
Argumentos |
Resultado |
---|---|---|
p, q, … [repeat=1] |
produto cartesiano, equivalente a laços |
|
p[, r] |
tuplas de tamanho r, com todas ordenações possíveis, sem elementos repetidos |
|
p, r |
tuplas de tamanho r, ordenadas, sem elementos repetidos |
|
p, r |
tuplas de tamanho r, ordenadas, com elementos repetidos |
Exemplos |
Resultado |
---|---|
|
|
|
|
|
|
|
|
Funções do intertools¶
Todas as funções a seguir constroem e retorna iteradores. Algumas fornecem fluxos de tamanhos infinitos, assim elas devem ser acessados somente por funções ou laços que interrompem o fluxo.
- itertools.accumulate(iterable[, function, *, initial=None])¶
Constrói um iterador que retorna somas acumuladas, ou resultados acumulados de outras funções binárias.
A função function assume como padrão a adição. function deve aceitar dois argumentos, um total acumulado e um valor de iterable.
Se um valor inicial initial for fornecido, o acúmulo começará com esse valor e a saída terá um elemento a mais que o iterável de entrada.
Aproximadamente equivalente a:
def accumulate(iterable, function=operator.add, *, initial=None): 'Retorna totais atuais' # accumulate([1,2,3,4,5]) → 1 3 6 10 15 # accumulate([1,2,3,4,5], initial=100) → 100 101 103 106 110 115 # accumulate([1,2,3,4,5], operator.mul) → 1 2 6 24 120 iterator = iter(iterable) total = initial if initial is None: try: total = next(iterator) except StopIteration: return yield total for element in iterator: total = function(total, element) yield total
The function argument can be set to
min()
for a running minimum,max()
for a running maximum, oroperator.mul()
for a running product. Amortization tables can be built by accumulating interest and applying payments:>>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8] >>> list(accumulate(data, max)) # máximo atual [3, 4, 6, 6, 6, 9, 9, 9, 9, 9] >>> list(accumulate(data, operator.mul)) # produto atual [3, 12, 72, 144, 144, 1296, 0, 0, 0, 0] # Amortiza um empréstimo de 5% de 1000 com 10 pagamentos anuais de 90 >>> update = lambda balance, payment: round(balance * 1.05) - payment >>> list(accumulate(repeat(90, 10), update, initial=1_000)) [1000, 960, 918, 874, 828, 779, 728, 674, 618, 559, 497]
Veja
functools.reduce()
para uma função similar que devolve apenas o valor acumulado final.Adicionado na versão 3.2.
Alterado na versão 3.3: Adicionado o parâmetro opcional function.
Alterado na versão 3.8: Adicionado o parâmetro opcional initial.
- itertools.batched(iterable, n)¶
Batch data from the iterable into tuples of length n. The last batch may be shorter than n.
Loops over the input iterable and accumulates data into tuples up to size n. The input is consumed lazily, just enough to fill a batch. The result is yielded as soon as the batch is full or when the input iterable is exhausted:
>>> flattened_data = ['roses', 'red', 'violets', 'blue', 'sugar', 'sweet'] >>> unflattened = list(batched(flattened_data, 2)) >>> unflattened [('roses', 'red'), ('violets', 'blue'), ('sugar', 'sweet')]
Aproximadamente equivalente a:
def batched(iterable, n): # batched('ABCDEFG', 3) → ABC DEF G if n < 1: raise ValueError('n must be at least one') iterator = iter(iterable) while batch := tuple(islice(iterator, n)): yield batch
Adicionado na versão 3.12.
- itertools.chain(*iterables)¶
Cria um iterador que devolve elementos do primeiro iterável até o esgotamento, então continua com o próximo iterável, até que todos os iteráveis sejam esgotados. Usando para tratar sequências consecutivas como uma única sequencia. aproximadamente equivalente a:
def chain(*iterables): # chain('ABC', 'DEF') → A B C D E F for iterable in iterables: yield from iterable
- classmethod chain.from_iterable(iterable)¶
Construtor alternativo para
chain()
. Obtém entradas encadeadas a partir de um único argumento iterável que avaliado preguiçosamente. Aproximadamente equivalente a:def from_iterable(iterables): # chain.from_iterable(['ABC', 'DEF']) → A B C D E F for iterable in iterables: yield from iterable
- itertools.combinations(iterable, r)¶
Devolve subsequências de elementos com comprimento r a partir da entrada iterável
The output is a subsequence of
product()
keeping only entries that are subsequences of the iterable. The length of the output is given bymath.comb()
which computesn! / r! / (n - r)!
when0 ≤ r ≤ n
or zero whenr > n
.The combination tuples are emitted in lexicographic order according to the order of the input iterable. If the input iterable is sorted, the output tuples will be produced in sorted order.
Elements are treated as unique based on their position, not on their value. If the input elements are unique, there will be no repeated values within each combination.
Aproximadamente equivalente a:
def combinations(iterable, r): # combinations('ABCD', 2) → AB AC AD BC BD CD # combinations(range(4), 3) → 012 013 023 123 pool = tuple(iterable) n = len(pool) if r > n: return indices = list(range(r)) yield tuple(pool[i] for i in indices) while True: for i in reversed(range(r)): if indices[i] != i + n - r: break else: return indices[i] += 1 for j in range(i+1, r): indices[j] = indices[j-1] + 1 yield tuple(pool[i] for i in indices)
- itertools.combinations_with_replacement(iterable, r)¶
Devolve subsequências de comprimento r de elementos do iterável de entrada permitindo que elementos individuais sejam repetidos mais de uma vez.
The output is a subsequence of
product()
that keeps only entries that are subsequences (with possible repeated elements) of the iterable. The number of subsequence returned is(n + r - 1)! / r! / (n - 1)!
whenn > 0
.The combination tuples are emitted in lexicographic order according to the order of the input iterable. if the input iterable is sorted, the output tuples will be produced in sorted order.
Elements are treated as unique based on their position, not on their value. If the input elements are unique, the generated combinations will also be unique.
Aproximadamente equivalente a:
def combinations_with_replacement(iterable, r): # combinations_with_replacement('ABC', 2) → AA AB AC BB BC CC pool = tuple(iterable) n = len(pool) if not n and r: return indices = [0] * r yield tuple(pool[i] for i in indices) while True: for i in reversed(range(r)): if indices[i] != n - 1: break else: return indices[i:] = [indices[i] + 1] * (r - i) yield tuple(pool[i] for i in indices)
Adicionado na versão 3.1.
- itertools.compress(data, selectors)¶
Make an iterator that returns elements from data where the corresponding element in selectors is true. Stops when either the data or selectors iterables have been exhausted. Roughly equivalent to:
def compress(data, selectors): # compress('ABCDEF', [1,0,1,0,1,1]) → A C E F return (datum for datum, selector in zip(data, selectors) if selector)
Adicionado na versão 3.1.
- itertools.count(start=0, step=1)¶
Make an iterator that returns evenly spaced values beginning with start. Can be used with
map()
to generate consecutive data points or withzip()
to add sequence numbers. Roughly equivalent to:def count(start=0, step=1): # count(10) → 10 11 12 13 14 ... # count(2.5, 0.5) → 2.5 3.0 3.5 ... n = start while True: yield n n += step
Quando é feita uma contagem usando números de ponto flutuante, é possível ter melhor precisão substituindo código multiplicativo como
(start + step * i for i in count())
.Alterado na versão 3.1: Adicionou argumento step e permitiu argumentos não-inteiros.
- itertools.cycle(iterable)¶
Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Roughly equivalent to:
def cycle(iterable): # cycle('ABCD') → A B C D A B C D A B C D ... saved = [] for element in iterable: yield element saved.append(element) while saved: for element in saved: yield element
This itertool may require significant auxiliary storage (depending on the length of the iterable).
- itertools.dropwhile(predicate, iterable)¶
Make an iterator that drops elements from the iterable while the predicate is true and afterwards returns every element. Roughly equivalent to:
def dropwhile(predicate, iterable): # dropwhile(lambda x: x<5, [1,4,6,3,8]) → 6 3 8 iterator = iter(iterable) for x in iterator: if not predicate(x): yield x break for x in iterator: yield x
Note this does not produce any output until the predicate first becomes false, so this itertool may have a lengthy start-up time.
- itertools.filterfalse(predicate, iterable)¶
Make an iterator that filters elements from the iterable returning only those for which the predicate returns a false value. If predicate is
None
, returns the items that are false. Roughly equivalent to:def filterfalse(predicate, iterable): # filterfalse(lambda x: x<5, [1,4,6,3,8]) → 6 8 if predicate is None: predicate = bool for x in iterable: if not predicate(x): yield x
- itertools.groupby(iterable, key=None)¶
Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is
None
, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function.The operation of
groupby()
is similar to theuniq
filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order.The returned group is itself an iterator that shares the underlying iterable with
groupby()
. Because the source is shared, when thegroupby()
object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list:groups = [] uniquekeys = [] data = sorted(data, key=keyfunc) for k, g in groupby(data, keyfunc): groups.append(list(g)) # Store group iterator as a list uniquekeys.append(k)
groupby()
é aproximadamente equivalente a:def groupby(iterable, key=None): # [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 keyfunc = (lambda x: x) if key is None else key iterator = iter(iterable) exhausted = False def _grouper(target_key): nonlocal curr_value, curr_key, exhausted yield curr_value for curr_value in iterator: curr_key = keyfunc(curr_value) if curr_key != target_key: return yield curr_value exhausted = True try: curr_value = next(iterator) except StopIteration: return curr_key = keyfunc(curr_value) while not exhausted: target_key = curr_key curr_group = _grouper(target_key) yield curr_key, curr_group if curr_key == target_key: for _ in curr_group: pass
- itertools.islice(iterable, stop)¶
- itertools.islice(iterable, start, stop[, step])
Make an iterator that returns selected elements from the iterable. Works like sequence slicing but does not support negative values for start, stop, or step.
If start is zero or
None
, iteration starts at zero. Otherwise, elements from the iterable are skipped until start is reached.If stop is
None
, iteration continues until the iterator is exhausted, if at all. Otherwise, it stops at the specified position.If step is
None
, the step defaults to one. Elements are returned consecutively unless step is set higher than one which results in items being skipped.Aproximadamente equivalente a:
def islice(iterable, *args): # islice('ABCDEFG', 2) → A B # islice('ABCDEFG', 2, 4) → C D # islice('ABCDEFG', 2, None) → C D E F G # islice('ABCDEFG', 0, None, 2) → A C E G s = slice(*args) start = 0 if s.start is None else s.start stop = s.stop step = 1 if s.step is None else s.step if start < 0 or (stop is not None and stop < 0) or step <= 0: raise ValueError indices = count() if stop is None else range(max(start, stop)) next_i = start for i, element in zip(indices, iterable): if i == next_i: yield element next_i += step
- itertools.pairwise(iterable)¶
Return successive overlapping pairs taken from the input iterable.
The number of 2-tuples in the output iterator will be one fewer than the number of inputs. It will be empty if the input iterable has fewer than two values.
Aproximadamente equivalente a:
def pairwise(iterable): # pairwise('ABCDEFG') → AB BC CD DE EF FG iterator = iter(iterable) a = next(iterator, None) for b in iterator: yield a, b a = b
Adicionado na versão 3.10.
- itertools.permutations(iterable, r=None)¶
Return successive r length permutations of elements from the iterable.
If r is not specified or is
None
, then r defaults to the length of the iterable and all possible full-length permutations are generated.The output is a subsequence of
product()
where entries with repeated elements have been filtered out. The length of the output is given bymath.perm()
which computesn! / (n - r)!
when0 ≤ r ≤ n
or zero whenr > n
.The permutation tuples are emitted in lexicographic order according to the order of the input iterable. If the input iterable is sorted, the output tuples will be produced in sorted order.
Elements are treated as unique based on their position, not on their value. If the input elements are unique, there will be no repeated values within a permutation.
Aproximadamente equivalente a:
def permutations(iterable, r=None): # permutations('ABCD', 2) → AB AC AD BA BC BD CA CB CD DA DB DC # permutations(range(3)) → 012 021 102 120 201 210 pool = tuple(iterable) n = len(pool) r = n if r is None else r if r > n: return indices = list(range(n)) cycles = list(range(n, n-r, -1)) yield tuple(pool[i] for i in indices[:r]) while n: for i in reversed(range(r)): cycles[i] -= 1 if cycles[i] == 0: indices[i:] = indices[i+1:] + indices[i:i+1] cycles[i] = n - i else: j = cycles[i] indices[i], indices[-j] = indices[-j], indices[i] yield tuple(pool[i] for i in indices[:r]) break else: return
- itertools.product(*iterables, repeat=1)¶
Produto cartesiano de iteráveis de entrada
Aproximadamente equivalente a laços for aninhados em uma expressão geradora. Por exemplo,
product(A, B)
devolve o mesmo que((x,y) for x in A for y in B)
.Os laços aninhados circulam como um hodômetro com o elemento mais à direita avançando a cada iteração. Este padrão cria uma ordenação lexicográfica de maneira que se os iteráveis de entrada estiverem ordenados, as tuplas produzidas são emitidas de maneira ordenada.
To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example,
product(A, repeat=4)
means the same asproduct(A, A, A, A)
.This function is roughly equivalent to the following code, except that the actual implementation does not build up intermediate results in memory:
def product(*iterables, repeat=1): # product('ABCD', 'xy') → Ax Ay Bx By Cx Cy Dx Dy # product(range(2), repeat=3) → 000 001 010 011 100 101 110 111 pools = [tuple(pool) for pool in iterables] * repeat result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] for prod in result: yield tuple(prod)
Before
product()
runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs.
- itertools.repeat(object[, times])¶
Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified.
Aproximadamente equivalente a:
def repeat(object, times=None): # repeat(10, 3) → 10 10 10 if times is None: while True: yield object else: for i in range(times): yield object
Um uso comum de repeat é para fornecer um fluxo de valores constantes para map ou zip.
>>> list(map(pow, range(10), repeat(2))) [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
- itertools.starmap(function, iterable)¶
Make an iterator that computes the function using arguments obtained from the iterable. Used instead of
map()
when argument parameters have already been “pre-zipped” into tuples.The difference between
map()
andstarmap()
parallels the distinction betweenfunction(a,b)
andfunction(*c)
. Roughly equivalent to:def starmap(function, iterable): # starmap(pow, [(2,5), (3,2), (10,3)]) → 32 9 1000 for args in iterable: yield function(*args)
- itertools.takewhile(predicate, iterable)¶
Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to:
def takewhile(predicate, iterable): # takewhile(lambda x: x<5, [1,4,6,3,8]) → 1 4 for x in iterable: if not predicate(x): break yield x
Note, the element that first fails the predicate condition is consumed from the input iterator and there is no way to access it. This could be an issue if an application wants to further consume the input iterator after takewhile has been run to exhaustion. To work around this problem, consider using more-itertools before_and_after() instead.
- itertools.tee(iterable, n=2)¶
Return n independent iterators from a single iterable.
Aproximadamente equivalente a:
def tee(iterable, n=2): if n < 0: raise ValueError if n == 0: return () iterator = _tee(iterable) result = [iterator] for _ in range(n - 1): result.append(_tee(iterator)) return tuple(result) class _tee: def __init__(self, iterable): it = iter(iterable) if isinstance(it, _tee): self.iterator = it.iterator self.link = it.link else: self.iterator = it self.link = [None, None] def __iter__(self): return self def __next__(self): link = self.link if link[1] is None: link[0] = next(self.iterator) link[1] = [None, None] value, self.link = link return value
tee
iterators are not threadsafe. ARuntimeError
may be raised when simultaneously using iterators returned by the sametee()
call, even if the original iterable is threadsafe.This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use
list()
instead oftee()
.
- itertools.zip_longest(*iterables, fillvalue=None)¶
Make an iterator that aggregates elements from each of the iterables.
If the iterables are of uneven length, missing values are filled-in with fillvalue. If not specified, fillvalue defaults to
None
.Iteration continues until the longest iterable is exhausted.
Aproximadamente equivalente a:
def zip_longest(*iterables, fillvalue=None): # zip_longest('ABCD', 'xy', fillvalue='-') → Ax By C- D- iterators = list(map(iter, iterables)) num_active = len(iterators) if not num_active: return while True: values = [] for i, iterator in enumerate(iterators): try: value = next(iterator) except StopIteration: num_active -= 1 if not num_active: return iterators[i] = repeat(fillvalue) value = fillvalue values.append(value) yield tuple(values)
If one of the iterables is potentially infinite, then the
zip_longest()
function should be wrapped with something that limits the number of calls (for exampleislice()
ortakewhile()
).
Receitas com itertools¶
Esta seção mostra receitas para criação de um ferramental ampliado usando as ferramentas existentes de itertools como elementos construtivos.
The primary purpose of the itertools recipes is educational. The recipes show
various ways of thinking about individual tools — for example, that
chain.from_iterable
is related to the concept of flattening. The recipes
also give ideas about ways that the tools can be combined — for example, how
starmap()
and repeat()
can work together. The recipes also show patterns
for using itertools with the operator
and collections
modules as
well as with the built-in itertools such as map()
, filter()
,
reversed()
, and enumerate()
.
A secondary purpose of the recipes is to serve as an incubator. The
accumulate()
, compress()
, and pairwise()
itertools started out as
recipes. Currently, the sliding_window()
, iter_index()
, and sieve()
recipes are being tested to see whether they prove their worth.
Substantially all of these recipes and many, many others can be installed from the more-itertools project found on the Python Package Index:
python -m pip install more-itertools
Many of the recipes offer the same high performance as the underlying toolset. Superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead.
import collections
import contextlib
import functools
import math
import operator
import random
def take(n, iterable):
"Return first n items of the iterable as a list."
return list(islice(iterable, n))
def prepend(value, iterable):
"Prepend a single value in front of an iterable."
# prepend(1, [2, 3, 4]) → 1 2 3 4
return chain([value], iterable)
def tabulate(function, start=0):
"Return function(0), function(1), ..."
return map(function, count(start))
def repeatfunc(func, times=None, *args):
"Repeat calls to func with specified arguments."
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
def flatten(list_of_lists):
"Flatten one level of nesting."
return chain.from_iterable(list_of_lists)
def ncycles(iterable, n):
"Returns the sequence elements n times."
return chain.from_iterable(repeat(tuple(iterable), n))
def tail(n, iterable):
"Return an iterator over the last n items."
# tail(3, 'ABCDEFG') → E F G
return iter(collections.deque(iterable, maxlen=n))
def consume(iterator, n=None):
"Advance the iterator n-steps ahead. If n is None, consume entirely."
# Use functions that consume iterators at C speed.
if n is None:
collections.deque(iterator, maxlen=0)
else:
next(islice(iterator, n, n), None)
def nth(iterable, n, default=None):
"Returns the nth item or a default value."
return next(islice(iterable, n, None), default)
def quantify(iterable, predicate=bool):
"Given a predicate that returns True or False, count the True results."
return sum(map(predicate, iterable))
def first_true(iterable, default=False, predicate=None):
"Returns the first true value or the *default* if there is no true value."
# first_true([a,b,c], x) → a or b or c or x
# first_true([a,b], x, f) → a if f(a) else b if f(b) else x
return next(filter(predicate, iterable), default)
def all_equal(iterable, key=None):
"Returns True if all the elements are equal to each other."
# all_equal('4٤௪౪໔', key=int) → True
return len(take(2, groupby(iterable, key))) <= 1
def unique_justseen(iterable, key=None):
"Yield unique elements, preserving order. Remember only the element just seen."
# unique_justseen('AAAABBBCCDAABBB') → A B C D A B
# unique_justseen('ABBcCAD', str.casefold) → A B c A D
if key is None:
return map(operator.itemgetter(0), groupby(iterable))
return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
def unique_everseen(iterable, key=None):
"Yield unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') → A B C D
# unique_everseen('ABBcCAD', str.casefold) → A B c D
seen = set()
if key is None:
for element in filterfalse(seen.__contains__, iterable):
seen.add(element)
yield element
else:
for element in iterable:
k = key(element)
if k not in seen:
seen.add(k)
yield element
def unique(iterable, key=None, reverse=False):
"Yield unique elements in sorted order. Supports unhashable inputs."
# unique([[1, 2], [3, 4], [1, 2]]) → [1, 2] [3, 4]
return unique_justseen(sorted(iterable, key=key, reverse=reverse), key=key)
def sliding_window(iterable, n):
"Collect data into overlapping fixed-length chunks or blocks."
# sliding_window('ABCDEFG', 4) → ABCD BCDE CDEF DEFG
iterator = iter(iterable)
window = collections.deque(islice(iterator, n - 1), maxlen=n)
for x in iterator:
window.append(x)
yield tuple(window)
def grouper(iterable, n, *, incomplete='fill', fillvalue=None):
"Collect data into non-overlapping fixed-length chunks or blocks."
# grouper('ABCDEFG', 3, fillvalue='x') → ABC DEF Gxx
# grouper('ABCDEFG', 3, incomplete='strict') → ABC DEF ValueError
# grouper('ABCDEFG', 3, incomplete='ignore') → ABC DEF
iterators = [iter(iterable)] * n
match incomplete:
case 'fill':
return zip_longest(*iterators, fillvalue=fillvalue)
case 'strict':
return zip(*iterators, strict=True)
case 'ignore':
return zip(*iterators)
case _:
raise ValueError('Expected fill, strict, or ignore')
def roundrobin(*iterables):
"Visit input iterables in a cycle until each is exhausted."
# roundrobin('ABC', 'D', 'EF') → A D E B F C
# Algorithm credited to George Sakkis
iterators = map(iter, iterables)
for num_active in range(len(iterables), 0, -1):
iterators = cycle(islice(iterators, num_active))
yield from map(next, iterators)
def partition(predicate, iterable):
"""Partition entries into false entries and true entries.
If *predicate* is slow, consider wrapping it with functools.lru_cache().
"""
# partition(is_odd, range(10)) → 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = tee(iterable)
return filterfalse(predicate, t1), filter(predicate, t2)
def subslices(seq):
"Return all contiguous non-empty subslices of a sequence."
# subslices('ABCD') → A AB ABC ABCD B BC BCD C CD D
slices = starmap(slice, combinations(range(len(seq) + 1), 2))
return map(operator.getitem, repeat(seq), slices)
def iter_index(iterable, value, start=0, stop=None):
"Return indices where a value occurs in a sequence or iterable."
# iter_index('AABCADEAF', 'A') → 0 1 4 7
seq_index = getattr(iterable, 'index', None)
if seq_index is None:
iterator = islice(iterable, start, stop)
for i, element in enumerate(iterator, start):
if element is value or element == value:
yield i
else:
stop = len(iterable) if stop is None else stop
i = start
with contextlib.suppress(ValueError):
while True:
yield (i := seq_index(value, i, stop))
i += 1
def iter_except(func, exception, first=None):
"Convert a call-until-exception interface to an iterator interface."
# iter_except(d.popitem, KeyError) → non-blocking dictionary iterator
with contextlib.suppress(exception):
if first is not None:
yield first()
while True:
yield func()
The following recipes have a more mathematical flavor:
def powerset(iterable):
"powerset([1,2,3]) → () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
def sum_of_squares(iterable):
"Add up the squares of the input values."
# sum_of_squares([10, 20, 30]) → 1400
return math.sumprod(*tee(iterable))
def reshape(matrix, cols):
"Reshape a 2-D matrix to have a given number of columns."
# reshape([(0, 1), (2, 3), (4, 5)], 3) → (0, 1, 2), (3, 4, 5)
return batched(chain.from_iterable(matrix), cols)
def transpose(matrix):
"Swap the rows and columns of a 2-D matrix."
# transpose([(1, 2, 3), (11, 22, 33)]) → (1, 11) (2, 22) (3, 33)
return zip(*matrix, strict=True)
def matmul(m1, m2):
"Multiply two matrices."
# matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]) → (49, 80), (41, 60)
n = len(m2[0])
return batched(starmap(math.sumprod, product(m1, transpose(m2))), n)
def convolve(signal, kernel):
"""Discrete linear convolution of two iterables.
Equivalent to polynomial multiplication.
Convolutions are mathematically commutative; however, the inputs are
evaluated differently. The signal is consumed lazily and can be
infinite. The kernel is fully consumed before the calculations begin.
Article: https://betterexplained.com/articles/intuitive-convolution/
Video: https://www.youtube.com/watch?v=KuXjwB4LzSA
"""
# convolve([1, -1, -20], [1, -3]) → 1 -4 -17 60
# convolve(data, [0.25, 0.25, 0.25, 0.25]) → Moving average (blur)
# convolve(data, [1/2, 0, -1/2]) → 1st derivative estimate
# convolve(data, [1, -2, 1]) → 2nd derivative estimate
kernel = tuple(kernel)[::-1]
n = len(kernel)
padded_signal = chain(repeat(0, n-1), signal, repeat(0, n-1))
windowed_signal = sliding_window(padded_signal, n)
return map(math.sumprod, repeat(kernel), windowed_signal)
def polynomial_from_roots(roots):
"""Compute a polynomial's coefficients from its roots.
(x - 5) (x + 4) (x - 3) expands to: x³ -4x² -17x + 60
"""
# polynomial_from_roots([5, -4, 3]) → [1, -4, -17, 60]
factors = zip(repeat(1), map(operator.neg, roots))
return list(functools.reduce(convolve, factors, [1]))
def polynomial_eval(coefficients, x):
"""Evaluate a polynomial at a specific value.
Computes with better numeric stability than Horner's method.
"""
# Evaluate x³ -4x² -17x + 60 at x = 5
# polynomial_eval([1, -4, -17, 60], x=5) → 0
n = len(coefficients)
if not n:
return type(x)(0)
powers = map(pow, repeat(x), reversed(range(n)))
return math.sumprod(coefficients, powers)
def polynomial_derivative(coefficients):
"""Compute the first derivative of a polynomial.
f(x) = x³ -4x² -17x + 60
f'(x) = 3x² -8x -17
"""
# polynomial_derivative([1, -4, -17, 60]) → [3, -8, -17]
n = len(coefficients)
powers = reversed(range(1, n))
return list(map(operator.mul, coefficients, powers))
def sieve(n):
"Primes less than n."
# sieve(30) → 2 3 5 7 11 13 17 19 23 29
if n > 2:
yield 2
data = bytearray((0, 1)) * (n // 2)
for p in iter_index(data, 1, start=3, stop=math.isqrt(n) + 1):
data[p*p : n : p+p] = bytes(len(range(p*p, n, p+p)))
yield from iter_index(data, 1, start=3)
def factor(n):
"Prime factors of n."
# factor(99) → 3 3 11
# factor(1_000_000_000_000_007) → 47 59 360620266859
# factor(1_000_000_000_000_403) → 1000000000000403
for prime in sieve(math.isqrt(n) + 1):
while not n % prime:
yield prime
n //= prime
if n == 1:
return
if n > 1:
yield n
def totient(n):
"Count of natural numbers up to n that are coprime to n."
# https://mathworld.wolfram.com/TotientFunction.html
# totient(12) → 4 because len([1, 5, 7, 11]) == 4
for prime in set(factor(n)):
n -= n // prime
return n