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iCollections es un software de Shareware en la categoría de Desktop desarrollado por Naarak Studio.

La última versión de iCollections es 6.6, aparecido en 21/11/2020. Inicialmente fue agregado a nuestra base de datos en 26/02/2016.

iCollections se ejecuta en los siguientes sistemas operativos: Mac.

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New in version 2.4.

This module implements high-performance container datatypes. Currently,there are two datatypes, deque and defaultdict, andone datatype factory function, namedtuple().

Changed in version 2.5: Added defaultdict.

Changed in version 2.6: Added namedtuple().

The specialized containers provided in this module provide alternativesto Python's general purpose built-in containers, dict,list, set, and tuple.

Besides the containers provided here, the optional bsddbmodule offers the ability to create in-memory or file based ordereddictionaries with string keys using the bsddb.btopen() Downie 2 7 6 download free. method.

In addition to containers, the collections module provides some ABCs(abstract base classes) that can be used to test whether a classprovides a particular interface, for example, is it hashable ora mapping.

Changed in version 2.6: Added abstract base classes.

8.3.1. ABCs - abstract base classes¶

The collections module offers the following ABCs:

These ABCs allow us to ask classes or instances if they provideparticular functionality, for example:

Several of the ABCs are also useful as mixins that make it easier to developclasses supporting container APIs. For example, to write a class supportingthe full Set API, it only necessary to supply the three underlyingabstract methods: __contains__(), __iter__(), and __len__().The ABC supplies the remaining methods such as __and__() andisdisjoint()

Notes on using Set and MutableSet as a mixin:

1. Since some set operations create new sets, the default mixin methods needa way to create new instances from an iterable. The class constructor isassumed to have a signature in the form ClassName(iterable).That assumption is factored-out to an internal classmethod called_from_iterable() which calls cls(iterable) to produce a new set.If the Set mixin is being used in a class with a differentconstructor signature, you will need to override from_iterable()with a classmethod that can construct new instances froman iterable argument.
2. To override the comparisons (presumably for speed, as thesemantics are fixed), redefine __le__() andthen the other operations will automatically follow suit.
3. The Set mixin provides a _hash() method to compute a hash valuefor the set; however, __hash__() is not defined because not all setsare hashable or immutable. To add set hashabilty using mixins,inherit from both Set() and Hashable(), then define__hash__=Set._hash.

See also

• OrderedSet recipe for anexample built on MutableSet.
• For more about ABCs, see the abc module and PEP 3119.

8.3.2. deque objects¶

Returns a new deque object initialized left-to-right (using append()) withdata from iterable. If iterable is not specified, the new deque is empty. Djay pro 2 0 11 download.

Deques are a generalization of stacks and queues (the name is pronounced 'deck'and is short for 'double-ended queue'). Deques support thread-safe, memoryefficient appends and pops from either side of the deque with approximately thesame O(1) performance in either direction.

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Though list objects support similar operations, they are optimized forfast fixed-length operations and incur O(n) memory movement costs forpop(0) and insert(0,v) operations which change both the size andposition of the underlying data representation.

New in version 2.4.

If maxlen is not specified or is None, deques may grow to anarbitrary length. Otherwise, the deque is bounded to the specified maximumlength. Once a bounded length deque is full, when new items are added, acorresponding number of items are discarded from the opposite end. Boundedlength deques provide functionality similar to the tail filter inUnix. They are also useful for tracking transactions and other pools of datawhere only the most recent activity is of interest.

Changed in version 2.6: Added maxlen parameter.

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.

extend(iterable)¶

Extend the right side of the deque by appending elements from the iterableargument.

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 ofelements in the iterable argument.

pop()¶

Remove and return an element from the right side of the deque. If noelements are present, raises an IndexError.

popleft()¶

Remove and return an element from the left side of the deque. If noelements are present, raises an IndexError.

Removed the first occurrence of value. If not found, raises aValueError.

New in version 2.5.

rotate(n)¶

Rotate the deque n steps to the right. If n is negative, rotate tothe left. Rotating one step to the right is equivalent to:d.appendleft(d.pop()).

In addition to the above, deques support iteration, pickling, len(d),reversed(d), copy.copy(d), copy.deepcopy(d), membership testing withthe in operator, and subscript references such as d[-1]. Indexedaccess is O(1) at both ends but slows to O(n) in the middle. For fast randomaccess, use lists instead.

Example:

8.3.2.1. deque Recipes¶

This section shows various approaches to working with deques.

Bounded length deques provide functionality similar to the tail filterin Unix:

Another approach to using deques is to maintain a sequence of recentlyadded elements by appending to the right and popping to the left:

The rotate() method provides a way to implement deque slicing anddeletion. For example, a pure Python implementation of deld[n] relies onthe rotate() method to position elements to be popped:

To implement deque slicing, use a similar approach applyingrotate() to bring a target element to the left side of the deque. Removeold entries with popleft(), add new entries with extend(), and thenreverse the rotation.With minor variations on that approach, it is easy to implement Forth stylestack manipulations such as dup, drop, swap, over, pick,rot, and roll.

8.3.3. defaultdict objects¶

Returns a new dictionary-like object. defaultdict is a subclass of thebuilt-in dict class. It overrides one method and adds one writableinstance 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_factoryattribute; it defaults to None. All remaining arguments are treated the sameas if they were passed to the dict constructor, including keywordarguments.

New in version 2.5.

defaultdict objects support the following method in addition to thestandard dict operations:

If the default_factory attribute is None, this raises aKeyError exception with the key as argument.

If default_factory is not None, it is called without argumentsto provide a default value for the given key, this value is inserted inthe dictionary for the key, and returned.

If calling default_factory raises an exception this exception ispropagated unchanged.

This method is called by the __getitem__() method of thedict class when the requested key is not found; whatever itreturns or raises is then returned or raised by __getitem__().

defaultdict objects support the following instance variable:

default_factory¶

This attribute is used by the __missing__() method; it isinitialized from the first argument to the constructor, if present, or toNone, if absent.

8.3.3.1. defaultdict Examples¶

Using list as the default_factory, it is easy to group asequence of key-value pairs into a dictionary of lists:

When each key is encountered for the first time, it is not already in themapping; so an entry is automatically created using the default_factoryfunction which returns an empty list. The list.append()operation then attaches the value to the new list. When keys are encounteredagain, the look-up proceeds normally (returning the list for that key) and thelist.append() operation adds another value to the list. This technique issimpler and faster than an equivalent technique using dict.setdefault():

Setting the default_factory to int makes thedefaultdict useful for counting (like a bag or multiset in otherlanguages):

When a letter is first encountered, it is missing from the mapping, so thedefault_factory function calls int() to supply a default count ofzero. The increment operation then builds up the count for each letter.

The function int() which always returns zero is just a special case ofconstant functions. A faster and more flexible way to create constant functionsis to use itertools.repeat() which can supply any constant value (not justzero):

Setting the default_factory to set makes thedefaultdict useful for building a dictionary of sets:

8.3.4. 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, andthey add the ability to access fields by name instead of position index.

Returns a new tuple subclass named typename. The new subclass is used tocreate tuple-like objects that have fields accessible by attribute lookup aswell as being indexable and iterable. Instances of the subclass also have ahelpful docstring (with typename and field_names) and a helpful __repr__()method which lists the tuple contents in a name=value format.

The field_names are a single string with each fieldname separated by whitespaceand/or commas, for example 'xy' or 'x,y'. Alternatively, field_namescan be a sequence of strings such as ['x','y'].

Any valid Python identifier may be used for a fieldname except for namesstarting with an underscore. Valid identifiers consist of letters, digits,and underscores but do not start with a digit or underscore and cannot bea keyword such as class, for, return, global, pass, print,or raise.

If verbose is true, the class definition is printed just before being built.

Named tuple instances do not have per-instance dictionaries, so they arelightweight and require no more memory than regular tuples.

New in version 2.6.

Example:

Named tuples are especially useful for assigning field names to result tuples returnedby the csv or sqlite3 modules:

In addition to the methods inherited from tuples, named tuples supportthree additional methods and one attribute. To prevent conflicts withfield names, the method and attribute names start with an underscore.

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Class method that makes a new instance from an existing sequence or iterable.

Return a new dict which maps field names to their corresponding values:

Return a new instance of the named tuple replacing specified fields with newvalues:

Tuple of strings listing the field names. Useful for introspectionand for creating new named tuple types from existing named tuples.

To retrieve a field whose name is stored in a string, use the getattr()function:

To convert a dictionary to a named tuple, use the double-star-operator(as described in Unpacking Argument Lists):

Since a named tuple is a regular Python class, it is easy to add or changefunctionality with a subclass. Here is how to add a calculated field anda fixed-width print format:

The subclass shown above sets __slots__ to an empty tuple. This helpskeep memory requirements low by preventing the creation of instance dictionaries.

Subclassing is not useful for adding new, stored fields. Instead, simplycreate a new named tuple type from the _fields attribute:

Default values can be implemented by using _replace() tocustomize a prototype instance:

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Enumerated constants can be implemented with named tuples, but it is simplerand more efficient to use a simple class declaration:

See also

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Named tuple recipeadapted for Python 2.4.