8.3. Tipos de dados do contêiner¶
Source code: Lib/collections/__init__.py
Este módulo implementa tipos de dados de contêiner especializados que fornecem alternativas aos contêineres integrados de uso geral do Python, dict
, list
, set
, and tuple
.
Função de fábrica para criar subclasses de tuplas com campos nomeados |
|
Contêiner semelhante a list com rápido appends e pops em qualquer fim |
|
Classe semelhante ao dict(dicionário) para criar uma visão única de vários mapeamentos |
|
Subclasse de Dict para contar objetos hashable |
|
Subclasse de Dict que lembra a ordem que as entradas foram adicionadas |
|
Subclasse de Dict que chama uma função de fábrica (factory function) para fornecer valores em faltam |
|
Envoltório em torno de objetos de dictionary para uma subclasse de dict mais fácil |
|
Invólucro em torno de objetos de list para uma subclasse de list mais fácil |
|
Invólucro em torno de objetos strings para uma subclasse mais fácil |
Alterado na versão 3.3: Moved Coleções Abstratas Classes Base to the collections.abc
module.
For backwards compatibility, they continue to be visible in this module
as well.
8.3.1. ChainMap
objects¶
Novo na versão 3.3.
Uma classe ChainMap
é fornecido para ligar rapidamente uma série de mapeamentos para que eles possam ser tratados como uma única unidade. Muitas vezes é muito mais rápido do que criar um novo dicionário e executar múltiplas chamadas update()
A classe pode ser usada para simular escopos aninhados e é útil em modelos.
-
class
collections.
ChainMap
(*maps)¶ Um grupo de múltiplos dicts
ChainMap
ou outros mapeamentos juntos para criar uma única view atualizável. Se maps não são especificados, Um diticionário vazio é fornecido para que uma nova cadeia sempre tenha pelo menos um mapeamento.Os mapeamentos subjacentes são armazenados em uma lista. Essa lista é pública e pode ser acessada ou atualizada usando o atributo * maps *. Não existe outro estado.
Faz a busca nos mapeamentos subjacentes sucessivamente até que uma chave seja encontrada. Em contraste, escrita, atualições e remoções operam apenas no primeiro mapeamento.
Uma
ChainMap
incorpora os mapeamentos subjacentes por referência. Então, se um dos mapeamentos subjacentes for atualizado, essas alterações serão refletidas em: class: ChainMap.Todos os métodos usuais do dicionário são suportados. Além disso, existe um atributo maps, um método para criar novos subcontextos e uma propriedade para acessar todos, exceto o primeiro mapeamento:
-
maps
¶ Uma lista de mapeamentos atualizáveis pelo usuário. A lista é ordenada desde o primeiro pesquisado até a última pesquisado. É o único estado armazenado e pode ser modificado para alterar quais mapeamentos são pesquisados. A lista deve sempre conter pelo menos um mapeamento.
-
new_child
(m=None)¶ Retorna uma nova: class: ‘ChainMap’ contendo um novo mapa seguido de todos os mapas na instância atual. Se
m
for especificado, torna-se o novo mapa na frente da lista de mapeamentos; Se não especificado, é usado um dicionário vazio, de modo que chamard.new_child ()
é equivalente a:ChainMap({}, *d.maps)
. Esse método é usado para criar subcontextos que podem ser atualizados sem alterar valores em nenhum dos mapeamentos pai.Alterado na versão 3.4: O parâmetro opcional “m” foi adicionado.
-
parents
¶ <nested scope>
-
Ver também
A classe MultiContext no pacote CodeTools de Enthought tem opções para suportar a escrita em qualquer mapeamento na cadeia.
Django’s Context class for templating is a read-only chain of mappings. It also features pushing and popping of contexts similar to the
new_child()
method and theparents()
property.A receita de Contextos Aninhados possui opções para controlar se escritas e outras mutações se aplicam a apenas o primeiro mapeamento ou para qualquer mapeamento na cadeia.
Uma versão muito simplificada somente leitura do Chainmap.<https://code.activestate.com/recipes/305268/>`_.
8.3.1.1. Exemplos e Receitas de ChainMap
¶
Esta seção mostra várias abordagens para trabalhar com mapas encadeados.
Exemplo de simulação da cadeia de busca interna do Python:
import builtins
pylookup = ChainMap(locals(), globals(), vars(builtins))
Exemplo de como permitir que os argumentos de linha de comando especificados pelo usuário tenham precedência sobre as variáveis de ambiente que, por sua vez, têm precedência sobre os valores padrão:
import os, argparse
defaults = {'color': 'red', 'user': 'guest'}
parser = argparse.ArgumentParser()
parser.add_argument('-u', '--user')
parser.add_argument('-c', '--color')
namespace = parser.parse_args()
command_line_args = {k:v for k, v in vars(namespace).items() if v}
combined = ChainMap(command_line_args, os.environ, defaults)
print(combined['color'])
print(combined['user'])
Padrões de exemplo para utilização da classe ChainMap
para simular contextos aninhados:
c = ChainMap() # Create root context
d = c.new_child() # Create nested child context
e = c.new_child() # Child of c, independent from d
e.maps[0] # Current context dictionary -- like Python's locals()
e.maps[-1] # Root context -- like Python's globals()
e.parents # Enclosing context chain -- like Python's nonlocals
d['x'] # Get first key in the chain of contexts
d['x'] = 1 # Set value in current context
del d['x'] # Delete from current context
list(d) # All nested values
k in d # Check all nested values
len(d) # Number of nested values
d.items() # All nested items
dict(d) # Flatten into a regular dictionary
A classe ChainMap
só faz atualizações (escritas e remoções) no primeiro mapeamento na cadeia, enquanto as pesquisas irão buscar em toda a cadeia. Contudo, se há o desejo de escritas e remoções profundas, é fácil fazer uma subclasse que atualiza chaves encontradas mais a fundo na cadeia:
class DeepChainMap(ChainMap):
'Variant of ChainMap that allows direct updates to inner scopes'
def __setitem__(self, key, value):
for mapping in self.maps:
if key in mapping:
mapping[key] = value
return
self.maps[0][key] = value
def __delitem__(self, key):
for mapping in self.maps:
if key in mapping:
del mapping[key]
return
raise KeyError(key)
>>> d = DeepChainMap({'zebra': 'black'}, {'elephant': 'blue'}, {'lion': 'yellow'})
>>> d['lion'] = 'orange' # update an existing key two levels down
>>> d['snake'] = 'red' # new keys get added to the topmost dict
>>> del d['elephant'] # remove an existing key one level down
>>> d # display result
DeepChainMap({'zebra': 'black', 'snake': 'red'}, {}, {'lion': 'orange'})
8.3.2. Objetos Counter
¶
Uma ferramenta de contagem é fornecida para apoiar contas rápidas e convenientes. Por exemplo:
>>> # Tally occurrences of words in a list
>>> cnt = Counter()
>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
... cnt[word] += 1
>>> cnt
Counter({'blue': 3, 'red': 2, 'green': 1})
>>> # Find the ten most common words in Hamlet
>>> import re
>>> words = re.findall(r'\w+', open('hamlet.txt').read().lower())
>>> Counter(words).most_common(10)
[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),
('you', 554), ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]
-
class
collections.
Counter
([iterable-or-mapping])¶ A
Counter
is adict
subclass for counting hashable objects. It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. TheCounter
class is similar to bags or multisets in other languages.Os elementas são contados a partir de um iterável ou inicializado a partir de um outro mapeamento (ou contador):
>>> c = Counter() # a new, empty counter >>> c = Counter('gallahad') # a new counter from an iterable >>> c = Counter({'red': 4, 'blue': 2}) # a new counter from a mapping >>> c = Counter(cats=4, dogs=8) # a new counter from keyword args
Objetos Counter tem uma interface de dicionário, com a diferença que devolvem uma contagem zero para itens que não estão presentes em vez de levantar a excessão
KeyError
:>>> c = Counter(['eggs', 'ham']) >>> c['bacon'] # count of a missing element is zero 0
Definir uma contagem como zero não remove um elemento do contador. Use
del
para o remover completamente.>>> c['sausage'] = 0 # counter entry with a zero count >>> del c['sausage'] # del actually removes the entry
Novo na versão 3.1.
Objetos Counter permitem três métodos além dos disponíveis para todos os dicionário:
-
elements
()¶ Return an iterator over elements repeating each as many times as its count. Elements are returned in arbitrary order. If an element’s count is less than one,
elements()
will ignore it.>>> c = Counter(a=4, b=2, c=0, d=-2) >>> sorted(c.elements()) ['a', 'a', 'a', 'a', 'b', 'b']
-
most_common
([n])¶ Return a list of the n most common elements and their counts from the most common to the least. If n is omitted or
None
,most_common()
returns all elements in the counter. Elements with equal counts are ordered arbitrarily:>>> Counter('abracadabra').most_common(3) [('a', 5), ('r', 2), ('b', 2)]
-
subtract
([iterable-or-mapping])¶ Os elementos são subtraídos de um iterável ou de outro mapeamento (ou contador). Funciona como
dict.update()
, mas subtraindo contagens ao invés de substituí-las. Tanto as entradas quanto as saídas podem ser zero ou negativas.>>> c = Counter(a=4, b=2, c=0, d=-2) >>> d = Counter(a=1, b=2, c=3, d=4) >>> c.subtract(d) >>> c Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
Novo na versão 3.2.
Os métodos usuais de dicionário estão disponíveis para objetos
Counter
, exceto por dois que funcionam de forma diferente para contadores.-
update
([iterable-or-mapping])¶ Elementos são contados a partir de um iterável ou adicionados de outro mapeamento (ou contador). Funciona como
dict.update()
mas adiciona contagens em vez de substituí-las. Além disso, é esperado que o iterável seja uma sequência de elementos, e não uma sequência de pares(key, value)
.
-
Padrões comuns para trabalhar com objetos Counter
:
sum(c.values()) # total of all counts
c.clear() # reset all counts
list(c) # list unique elements
set(c) # convert to a set
dict(c) # convert to a regular dictionary
c.items() # convert to a list of (elem, cnt) pairs
Counter(dict(list_of_pairs)) # convert from a list of (elem, cnt) pairs
c.most_common()[:-n-1:-1] # n least common elements
+c # remove zero and negative counts
Várias operações matemáticas são fornecidas para combinar: class: objetos Counter para produzir multisets (counters que têm contagens maiores que zero). A adição e a subtração combinam counters adicionando ou subtraindo as contagens dos elementos correspondentes. A intersecção e a união retornam o mínimo e o máximo das contagens correspondentes. Cada operação pode aceitar entradas com contagens assinadas, mas a saída excluirá resultados com contagens de zero ou menos.
>>> c = Counter(a=3, b=1)
>>> d = Counter(a=1, b=2)
>>> c + d # add two counters together: c[x] + d[x]
Counter({'a': 4, 'b': 3})
>>> c - d # subtract (keeping only positive counts)
Counter({'a': 2})
>>> c & d # intersection: min(c[x], d[x])
Counter({'a': 1, 'b': 1})
>>> c | d # union: max(c[x], d[x])
Counter({'a': 3, 'b': 2})
A adição e subtração unárias são atalhos para adicionar um contador vazio ou subtrair de um contador vazio.
>>> c = Counter(a=2, b=-4)
>>> +c
Counter({'a': 2})
>>> -c
Counter({'b': 4})
Novo na versão 3.3: Adicionado suporte para operador unário mais, unário menos e operação in-place multiset.
Nota
Os contadores foram projetados principalmente para funcionar com números inteiros positivos para representar contagens contínuas; no entanto, foi tomado cuidado para não impedir desnecessariamente os casos de uso que precisassem de outros tipos ou valores negativos. Para ajudar nesses casos de uso, esta seção documenta o intervalo mínimo e as restrições de tipo.
A própria classe
Counter
é uma subclasse de dicionário sem restrições em suas chaves e valores. Os valores pretendem ser números que representam contagens, mas você pode armazenar qualquer coisa no campo de valor.The
most_common()
method requires only that the values be orderable.For in-place operations such as
c[key] += 1
, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true forupdate()
andsubtract()
which allow negative and zero values for both inputs and outputs.Os métodos multiset são projetados apenas para casos de uso com valores positivos. As entradas podem ser negativas ou zero, mas apenas saídas com valores positivos são criadas. Não há restrições de tipo, mas o tipo de valor precisa suportar adição, subtração e comparação.
The
elements()
method requires integer counts. It ignores zero and negative counts.
Ver também
Classe Bag <https://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html> `_ em Smalltalk.
Entrada da Wikipedia para Multisets <https://en.wikipedia.org/wiki/Multiset> _.
Tutorial com exemplos C++ multisets.
Para operações matemáticas em multisets e seus casos de uso, consulte * Knuth, Donald. The Art of Computer Programming Volume II, Seção 4.6.3, Exercício 19 *.
To enumerate all distinct multisets of a given size over a given set of elements, see
itertools.combinations_with_replacement()
:map(Counter, combinations_with_replacement(‘ABC’, 2)) –> AA AB AC BB BC CC
8.3.3. Objetos deque
¶
-
class
collections.
deque
([iterable[, maxlen]])¶ Returns a new deque object initialized left-to-right (using
append()
) with data from iterable. If iterable is not specified, the new deque is empty.Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.
Though
list
objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs forpop(0)
andinsert(0, v)
operations which change both the size and position of the underlying data representation.If maxlen is not specified or is
None
, deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to thetail
filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest.Deque objects support the following methods:
-
append
(x)¶ Add x to the right side of the deque.
-
appendleft
(x)¶ Add x to the left side of the deque.
-
clear
()¶ Remove all elements from the deque leaving it with length 0.
-
copy
()¶ Create a shallow copy of the deque.
Novo na versão 3.5.
-
count
(x)¶ Count the number of deque elements equal to x.
Novo na versão 3.2.
-
extend
(iterable)¶ Extend the right side of the deque by appending elements from the iterable argument.
-
extendleft
(iterable)¶ Extend the left side of the deque by appending elements from iterable. Note, the series of left appends results in reversing the order of elements in the iterable argument.
-
index
(x[, start[, stop]])¶ Return the position of x in the deque (at or after index start and before index stop). Returns the first match or raises
ValueError
if not found.Novo na versão 3.5.
-
insert
(i, x)¶ Insert x into the deque at position i.
If the insertion would cause a bounded deque to grow beyond maxlen, an
IndexError
is raised.Novo na versão 3.5.
-
pop
()¶ Remove and return an element from the right side of the deque. If no elements are present, raises an
IndexError
.
-
popleft
()¶ Remove and return an element from the left side of the deque. If no elements are present, raises an
IndexError
.
-
remove
(value)¶ Remove the first occurrence of value. If not found, raises a
ValueError
.
-
reverse
()¶ Reverse the elements of the deque in-place and then return
None
.Novo na versão 3.2.
-
rotate
(n=1)¶ Rotate the deque n steps to the right. If n is negative, rotate to the left.
When the deque is not empty, rotating one step to the right is equivalent to
d.appendleft(d.pop())
, and rotating one step to the left is equivalent tod.append(d.popleft())
.
Deque objects also provide one read-only attribute:
-
maxlen
¶ Maximum size of a deque or
None
if unbounded.Novo na versão 3.1.
-
In addition to the above, deques support iteration, pickling, len(d)
,
reversed(d)
, copy.copy(d)
, copy.deepcopy(d)
, membership testing with
the in
operator, and subscript references such as d[-1]
. Indexed
access is O(1) at both ends but slows to O(n) in the middle. For fast random
access, use lists instead.
Starting in version 3.5, deques support __add__()
, __mul__()
,
and __imul__()
.
Exemplo:
>>> from collections import deque
>>> d = deque('ghi') # make a new deque with three items
>>> for elem in d: # iterate over the deque's elements
... print(elem.upper())
G
H
I
>>> d.append('j') # add a new entry to the right side
>>> d.appendleft('f') # add a new entry to the left side
>>> d # show the representation of the deque
deque(['f', 'g', 'h', 'i', 'j'])
>>> d.pop() # return and remove the rightmost item
'j'
>>> d.popleft() # return and remove the leftmost item
'f'
>>> list(d) # list the contents of the deque
['g', 'h', 'i']
>>> d[0] # peek at leftmost item
'g'
>>> d[-1] # peek at rightmost item
'i'
>>> list(reversed(d)) # list the contents of a deque in reverse
['i', 'h', 'g']
>>> 'h' in d # search the deque
True
>>> d.extend('jkl') # add multiple elements at once
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> d.rotate(1) # right rotation
>>> d
deque(['l', 'g', 'h', 'i', 'j', 'k'])
>>> d.rotate(-1) # left rotation
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> deque(reversed(d)) # make a new deque in reverse order
deque(['l', 'k', 'j', 'i', 'h', 'g'])
>>> d.clear() # empty the deque
>>> d.pop() # cannot pop from an empty deque
Traceback (most recent call last):
File "<pyshell#6>", line 1, in -toplevel-
d.pop()
IndexError: pop from an empty deque
>>> d.extendleft('abc') # extendleft() reverses the input order
>>> d
deque(['c', 'b', 'a'])
8.3.3.1. Receitas de deque
¶
This section shows various approaches to working with deques.
Bounded length deques provide functionality similar to the tail
filter
in Unix:
def tail(filename, n=10):
'Return the last n lines of a file'
with open(filename) as f:
return deque(f, n)
Another approach to using deques is to maintain a sequence of recently added elements by appending to the right and popping to the left:
def moving_average(iterable, n=3):
# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
# http://en.wikipedia.org/wiki/Moving_average
it = iter(iterable)
d = deque(itertools.islice(it, n-1))
d.appendleft(0)
s = sum(d)
for elem in it:
s += elem - d.popleft()
d.append(elem)
yield s / n
The rotate()
method provides a way to implement deque
slicing and
deletion. For example, a pure Python implementation of del d[n]
relies on
the rotate()
method to position elements to be popped:
def delete_nth(d, n):
d.rotate(-n)
d.popleft()
d.rotate(n)
To implement deque
slicing, use a similar approach applying
rotate()
to bring a target element to the left side of the deque. Remove
old entries with popleft()
, add new entries with extend()
, and then
reverse the rotation.
With minor variations on that approach, it is easy to implement Forth style
stack manipulations such as dup
, drop
, swap
, over
, pick
,
rot
, and roll
.
8.3.4. Objetos defaultdict
¶
-
class
collections.
defaultdict
([default_factory[, ...]])¶ Returns a new dictionary-like object.
defaultdict
is a subclass of the built-indict
class. It overrides one method and adds one writable instance variable. The remaining functionality is the same as for thedict
class and is not documented here.The first argument provides the initial value for the
default_factory
attribute; it defaults toNone
. All remaining arguments are treated the same as if they were passed to thedict
constructor, including keyword arguments.defaultdict
objects support the following method in addition to the standarddict
operations:-
__missing__
(key)¶ If the
default_factory
attribute isNone
, this raises aKeyError
exception with the key as argument.If
default_factory
is notNone
, it is called without arguments to provide a default value for the given key, this value is inserted in the dictionary for the key, and returned.If calling
default_factory
raises an exception this exception is propagated unchanged.This method is called by the
__getitem__()
method of thedict
class when the requested key is not found; whatever it returns or raises is then returned or raised by__getitem__()
.Note that
__missing__()
is not called for any operations besides__getitem__()
. This means thatget()
will, like normal dictionaries, returnNone
as a default rather than usingdefault_factory
.
defaultdict
objects support the following instance variable:-
default_factory
¶ This attribute is used by the
__missing__()
method; it is initialized from the first argument to the constructor, if present, or toNone
, if absent.
-
8.3.4.1. defaultdict
Examples¶
Using list
as the default_factory
, it is easy to group a
sequence of key-value pairs into a dictionary of lists:
>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
>>> d = defaultdict(list)
>>> for k, v in s:
... d[k].append(v)
...
>>> sorted(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
When each key is encountered for the first time, it is not already in the
mapping; so an entry is automatically created using the default_factory
function which returns an empty list
. The list.append()
operation then attaches the value to the new list. When keys are encountered
again, the look-up proceeds normally (returning the list for that key) and the
list.append()
operation adds another value to the list. This technique is
simpler and faster than an equivalent technique using dict.setdefault()
:
>>> d = {}
>>> for k, v in s:
... d.setdefault(k, []).append(v)
...
>>> sorted(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
Setting the default_factory
to int
makes the
defaultdict
useful for counting (like a bag or multiset in other
languages):
>>> s = 'mississippi'
>>> d = defaultdict(int)
>>> for k in s:
... d[k] += 1
...
>>> sorted(d.items())
[('i', 4), ('m', 1), ('p', 2), ('s', 4)]
When a letter is first encountered, it is missing from the mapping, so the
default_factory
function calls int()
to supply a default count of
zero. The increment operation then builds up the count for each letter.
The function int()
which always returns zero is just a special case of
constant functions. A faster and more flexible way to create constant functions
is to use a lambda function which can supply any constant value (not just
zero):
>>> def constant_factory(value):
... return lambda: value
>>> d = defaultdict(constant_factory('<missing>'))
>>> d.update(name='John', action='ran')
>>> '%(name)s %(action)s to %(object)s' % d
'John ran to <missing>'
Setting the default_factory
to set
makes the
defaultdict
useful for building a dictionary of sets:
>>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
>>> d = defaultdict(set)
>>> for k, v in s:
... d[k].add(v)
...
>>> sorted(d.items())
[('blue', {2, 4}), ('red', {1, 3})]
8.3.5. namedtuple()
Factory Function for Tuples with Named Fields¶
Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.
-
collections.
namedtuple
(typename, field_names, *, verbose=False, rename=False, module=None)¶ Returns a new tuple subclass named typename. The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with typename and field_names) and a helpful
__repr__()
method which lists the tuple contents in aname=value
format.The field_names are a sequence of strings such as
['x', 'y']
. Alternatively, field_names can be a single string with each fieldname separated by whitespace and/or commas, for example'x y'
or'x, y'
.Any valid Python identifier may be used for a fieldname except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a
keyword
such as class, for, return, global, pass, or raise.If rename is true, invalid fieldnames are automatically replaced with positional names. For example,
['abc', 'def', 'ghi', 'abc']
is converted to['abc', '_1', 'ghi', '_3']
, eliminating the keyworddef
and the duplicate fieldnameabc
.If verbose is true, the class definition is printed after it is built. This option is outdated; instead, it is simpler to print the
_source
attribute.If module is defined, the
__module__
attribute of the named tuple is set to that value.Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.
Alterado na versão 3.1: Added support for rename.
Alterado na versão 3.6: The verbose and rename parameters became keyword-only arguments.
Alterado na versão 3.6: Added the module parameter.
>>> # Basic example
>>> Point = namedtuple('Point', ['x', 'y'])
>>> p = Point(11, y=22) # instantiate with positional or keyword arguments
>>> p[0] + p[1] # indexable like the plain tuple (11, 22)
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> p # readable __repr__ with a name=value style
Point(x=11, y=22)
Named tuples are especially useful for assigning field names to result tuples returned
by the csv
or sqlite3
modules:
EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
import csv
for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
print(emp.name, emp.title)
import sqlite3
conn = sqlite3.connect('/companydata')
cursor = conn.cursor()
cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
for emp in map(EmployeeRecord._make, cursor.fetchall()):
print(emp.name, emp.title)
In addition to the methods inherited from tuples, named tuples support three additional methods and two attributes. To prevent conflicts with field names, the method and attribute names start with an underscore.
-
classmethod
somenamedtuple.
_make
(iterable)¶ Class method that makes a new instance from an existing sequence or iterable.
>>> t = [11, 22] >>> Point._make(t) Point(x=11, y=22)
-
somenamedtuple.
_asdict
()¶ Return a new
OrderedDict
which maps field names to their corresponding values:>>> p = Point(x=11, y=22) >>> p._asdict() OrderedDict([('x', 11), ('y', 22)])
Alterado na versão 3.1: Returns an
OrderedDict
instead of a regulardict
.
-
somenamedtuple.
_replace
(**kwargs)¶ Return a new instance of the named tuple replacing specified fields with new values:
>>> p = Point(x=11, y=22) >>> p._replace(x=33) Point(x=33, y=22) >>> for partnum, record in inventory.items(): ... inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())
-
somenamedtuple.
_source
¶ A string with the pure Python source code used to create the named tuple class. The source makes the named tuple self-documenting. It can be printed, executed using
exec()
, or saved to a file and imported.Novo na versão 3.3.
-
somenamedtuple.
_fields
¶ Tuple of strings listing the field names. Useful for introspection and for creating new named tuple types from existing named tuples.
>>> p._fields # view the field names ('x', 'y') >>> Color = namedtuple('Color', 'red green blue') >>> Pixel = namedtuple('Pixel', Point._fields + Color._fields) >>> Pixel(11, 22, 128, 255, 0) Pixel(x=11, y=22, red=128, green=255, blue=0)
To retrieve a field whose name is stored in a string, use the getattr()
function:
>>> getattr(p, 'x')
11
To convert a dictionary to a named tuple, use the double-star-operator (as described in Desempacotando listas de argumentos):
>>> d = {'x': 11, 'y': 22}
>>> Point(**d)
Point(x=11, y=22)
Since a named tuple is a regular Python class, it is easy to add or change functionality with a subclass. Here is how to add a calculated field and a fixed-width print format:
>>> class Point(namedtuple('Point', ['x', 'y'])):
... __slots__ = ()
... @property
... def hypot(self):
... return (self.x ** 2 + self.y ** 2) ** 0.5
... def __str__(self):
... return 'Point: x=%6.3f y=%6.3f hypot=%6.3f' % (self.x, self.y, self.hypot)
>>> for p in Point(3, 4), Point(14, 5/7):
... print(p)
Point: x= 3.000 y= 4.000 hypot= 5.000
Point: x=14.000 y= 0.714 hypot=14.018
The subclass shown above sets __slots__
to an empty tuple. This helps
keep memory requirements low by preventing the creation of instance dictionaries.
Subclassing is not useful for adding new, stored fields. Instead, simply
create a new named tuple type from the _fields
attribute:
>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
Docstrings can be customized by making direct assignments to the __doc__
fields:
>>> Book = namedtuple('Book', ['id', 'title', 'authors'])
>>> Book.__doc__ += ': Hardcover book in active collection'
>>> Book.id.__doc__ = '13-digit ISBN'
>>> Book.title.__doc__ = 'Title of first printing'
>>> Book.authors.__doc__ = 'List of authors sorted by last name'
Alterado na versão 3.5: Property docstrings became writeable.
Default values can be implemented by using _replace()
to
customize a prototype instance:
>>> Account = namedtuple('Account', 'owner balance transaction_count')
>>> default_account = Account('<owner name>', 0.0, 0)
>>> johns_account = default_account._replace(owner='John')
>>> janes_account = default_account._replace(owner='Jane')
Ver também
Recipe for named tuple abstract base class with a metaclass mix-in by Jan Kaliszewski. Besides providing an abstract base class for named tuples, it also supports an alternate metaclass-based constructor that is convenient for use cases where named tuples are being subclassed.
See
types.SimpleNamespace()
for a mutable namespace based on an underlying dictionary instead of a tuple.See
typing.NamedTuple()
for a way to add type hints for named tuples.
8.3.6. Objetos OrderedDict
¶
Ordered dictionaries are just like regular dictionaries but they remember the order that items were inserted. When iterating over an ordered dictionary, the items are returned in the order their keys were first added.
-
class
collections.
OrderedDict
([items])¶ Return an instance of a dict subclass, supporting the usual
dict
methods. An OrderedDict is a dict that remembers the order that keys were first inserted. If a new entry overwrites an existing entry, the original insertion position is left unchanged. Deleting an entry and reinserting it will move it to the end.Novo na versão 3.1.
-
popitem
(last=True)¶ The
popitem()
method for ordered dictionaries returns and removes a (key, value) pair. The pairs are returned in LIFO order if last is true or FIFO order if false.
-
move_to_end
(key, last=True)¶ Move an existing key to either end of an ordered dictionary. The item is moved to the right end if last is true (the default) or to the beginning if last is false. Raises
KeyError
if the key does not exist:>>> d = OrderedDict.fromkeys('abcde') >>> d.move_to_end('b') >>> ''.join(d.keys()) 'acdeb' >>> d.move_to_end('b', last=False) >>> ''.join(d.keys()) 'bacde'
Novo na versão 3.2.
-
Além dos métodos usuais de mapeamento, dicionários ordenados também suportam iteração reversa usando a função reversed()
.
Equality tests between OrderedDict
objects are order-sensitive
and are implemented as list(od1.items())==list(od2.items())
.
Equality tests between OrderedDict
objects and other
Mapping
objects are order-insensitive like regular
dictionaries. This allows OrderedDict
objects to be substituted
anywhere a regular dictionary is used.
Alterado na versão 3.5: The items, keys, and values views
of OrderedDict
now support reverse iteration using reversed()
.
Alterado na versão 3.6: With the acceptance of PEP 468, order is retained for keyword arguments
passed to the OrderedDict
constructor and its update()
method.
8.3.6.1. OrderedDict
Examples and Recipes¶
Since an ordered dictionary remembers its insertion order, it can be used in conjunction with sorting to make a sorted dictionary:
>>> # regular unsorted dictionary
>>> d = {'banana': 3, 'apple': 4, 'pear': 1, 'orange': 2}
>>> # dictionary sorted by key
>>> OrderedDict(sorted(d.items(), key=lambda t: t[0]))
OrderedDict([('apple', 4), ('banana', 3), ('orange', 2), ('pear', 1)])
>>> # dictionary sorted by value
>>> OrderedDict(sorted(d.items(), key=lambda t: t[1]))
OrderedDict([('pear', 1), ('orange', 2), ('banana', 3), ('apple', 4)])
>>> # dictionary sorted by length of the key string
>>> OrderedDict(sorted(d.items(), key=lambda t: len(t[0])))
OrderedDict([('pear', 1), ('apple', 4), ('orange', 2), ('banana', 3)])
The new sorted dictionaries maintain their sort order when entries are deleted. But when new keys are added, the keys are appended to the end and the sort is not maintained.
It is also straight-forward to create an ordered dictionary variant that remembers the order the keys were last inserted. If a new entry overwrites an existing entry, the original insertion position is changed and moved to the end:
class LastUpdatedOrderedDict(OrderedDict):
'Store items in the order the keys were last added'
def __setitem__(self, key, value):
if key in self:
del self[key]
OrderedDict.__setitem__(self, key, value)
An ordered dictionary can be combined with the Counter
class
so that the counter remembers the order elements are first encountered:
class OrderedCounter(Counter, OrderedDict):
'Counter that remembers the order elements are first encountered'
def __repr__(self):
return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
def __reduce__(self):
return self.__class__, (OrderedDict(self),)
8.3.7. UserDict
objects¶
The class, UserDict
acts as a wrapper around dictionary objects.
The need for this class has been partially supplanted by the ability to
subclass directly from dict
; however, this class can be easier
to work with because the underlying dictionary is accessible as an
attribute.
-
class
collections.
UserDict
([initialdata])¶ Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the
data
attribute ofUserDict
instances. If initialdata is provided,data
is initialized with its contents; note that a reference to initialdata will not be kept, allowing it be used for other purposes.In addition to supporting the methods and operations of mappings,
UserDict
instances provide the following attribute:
8.3.8. UserList
objects¶
This class acts as a wrapper around list objects. It is a useful base class for your own list-like classes which can inherit from them and override existing methods or add new ones. In this way, one can add new behaviors to lists.
The need for this class has been partially supplanted by the ability to
subclass directly from list
; however, this class can be easier
to work with because the underlying list is accessible as an attribute.
-
class
collections.
UserList
([list])¶ Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the
data
attribute ofUserList
instances. The instance’s contents are initially set to a copy of list, defaulting to the empty list[]
. list can be any iterable, for example a real Python list or aUserList
object.In addition to supporting the methods and operations of mutable sequences,
UserList
instances provide the following attribute:
Subclassing requirements: Subclasses of UserList
are expected to
offer a constructor which can be called with either no arguments or one
argument. List operations which return a new sequence attempt to create an
instance of the actual implementation class. To do so, it assumes that the
constructor can be called with a single parameter, which is a sequence object
used as a data source.
If a derived class does not wish to comply with this requirement, all of the special methods supported by this class will need to be overridden; please consult the sources for information about the methods which need to be provided in that case.
8.3.9. UserString
objects¶
The class, UserString
acts as a wrapper around string objects.
The need for this class has been partially supplanted by the ability to
subclass directly from str
; however, this class can be easier
to work with because the underlying string is accessible as an
attribute.
-
class
collections.
UserString
([sequence])¶ Class that simulates a string or a Unicode string object. The instance’s content is kept in a regular string object, which is accessible via the
data
attribute ofUserString
instances. The instance’s contents are initially set to a copy of sequence. The sequence can be an instance ofbytes
,str
,UserString
(or a subclass) or an arbitrary sequence which can be converted into a string using the built-instr()
function.Alterado na versão 3.5: New methods
__getnewargs__
,__rmod__
,casefold
,format_map
,isprintable
, andmaketrans
.