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🎰 10. __slots__ Magic — Python Tips 0.1 documentation

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It produces a subclass of mutabletuple with namedtuple-like API. structclass is an analog of recordclass. It produces a class with less memory footprint (less than both recordclass-based class instances and instances of class with __slots__) and namedtuple-like API. Click to Play!

Unfortunately, __slots__ aren't something that you can add dynamically. The class needs them when it's defined. In fact, the only way namedtuple works around the fact that __slots__ are so hard to setup dynamically is that it creates a format string containing Python code and calls exec to create the class at run-time. Click to Play!

Per Python bug #24931: [__dict__] disappeared because it was fundamentally broken in Python 3, so it had to be removed. Providing __dict__ broke subclassing and produced odd behaviors. Revision that made the change. Specifically, subclasses without __slots__ defined would behave weirdly: Click to Play!

4. namedtuple ¶ You might already be acquainted with tuples. A tuple is a lightweight object type which allows to store a sequence of immutable Python objects. They are just like lists but have a few key differences. The major one is that unlike lists, you can not change a value in a tuple. In order to access the value in a tuple you use. Click to Play!


python - Usage of __slots__? - Stack Overflow


4. namedtuple ¶ You might already be acquainted with tuples. A tuple is a lightweight object type which allows to store a sequence of immutable Python objects. They are just like lists but have a few key differences. The major one is that unlike lists, you can not change a value in a tuple. In order to access the value in a tuple you use.
Furthermore, attrs has been around for a while and is supported in Python 2.7 as well as Python 3.4 and up. However, as attrs is not a part of the standard library, it does add an external dependency to your projects. Through data classes, similar functionality will be available everywhere.
collections.namedtuple(typename, field_names, verbose=False, rename=False)¶ 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.


Raymond Hettinger, "Being a Core Developer in Python", PyBay2016


10. __slots__ Magic — Python Tips 0.1 documentation Python namedtuple __slots__


Namedtuple objects are implemented as regular Python classes internally. When it comes to memory usage they are also “better” than regular classes and just as memory efficient as regular tuples. A good way to view them is to think that namedtuples are a memory-efficient shortcut to defining an immutable class in Python manually.
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__. Just for completeness of my notes, note that there is a one-time cost per slot in the class's namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use.
Specifying __slots__. For namedlist.namedlist, by default, the returned class sets __slots__ which is initialized to the field names. While this decreases memory usage by eliminating the instance dict, it also means that you cannot create new instance members.



12. Collections — Python Tips 0.1 documentation


python namedtuple __slots__
collections.namedtuple(typename, field_names, verbose=False, rename=False)¶ 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.
Mutable Named Tuple - or - Slotted Data Structure. use __slots__. Further, a namedtuple is semantically just a record of data (maybe with some functionality.

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Smith Status: Final Type: Standards Track Created: 02-Jun-2017 Python-Version: 3.
more info commenting in a public forum please at least read the listed at the end of this PEP.
This PEP describes an addition to the standard library called Data Https://casinos-free-list.site/1/380.html />Although they use a very different mechanism, Data Classes can be thought of as "mutable namedtuples プレーリーバンドカジノの空きスロット defaults".
Because Data Classes use python namedtuple __slots__ class definition syntax, you are free to use inheritance, metaclasses, docstrings, user-defined methods, class factories, and other Python class features.
A class decorator is provided which inspects a class definition for variables with type annotations as defined in"Syntax for Variable Annotations".
In this document, such variables are called fields.
Using these fields, the decorator adds generated method definitions to the class to support instance initialization, a repr, comparison methods, and optionally other methods as described in the section.
Such a class is called a Data Class, but there's really nothing special about the class: the decorator adds generated methods to the class and returns the same class it was given.
As an example: dataclass class InventoryItem: '''Class for keeping track of an item in inventory.
There have been numerous attempts to define classes which exist primarily to store values which are accessible by attribute lookup.
NamedTuple in the standard library.
David Beazley used a form of data classes as the motivating example in a PyCon 2013 metaclass talk.
So, why is this PEP needed?
With the addition ofPython has a concise way to specify the type of class members.
This PEP leverages that syntax to provide a simple, unobtrusive way to describe Data Classes.
With two exceptions, the specified attribute type annotation is completely ignored by Data Classes.
No base classes or metaclasses are used by Data Classes.
Users of these classes are free to use inheritance and metaclasses without any interference from Data Classes.
The decorated classes are truly "normal" Python classes.
The Data Class decorator should not interfere https://casinos-free-list.site/1/1364.html any usage of the class.
One main design goal of Data Classes is to support static type checkers.
The use of syntax is one example of this, but so is the design of the fields function and the dataclass decorator.
Due to their very dynamic nature, some of the libraries mentioned above are https://casinos-free-list.site/1/648.html to use with static type checkers.
Data Classes are not, and are not intended to be, a replacement mechanism for all of the above libraries.
But being in the standard library will allow many of the simpler use cases to instead leverage Data Classes.
Many of the libraries listed have different feature sets, and will of course continue to exist and prosper.
Where is it not appropriate to use Data Classes?
All of the functions described in this PEP will live in a module named dataclasses.
A function dataclass which is typically used as a class decorator is provided to post-process classes and add generated methods, described below.
The dataclass decorator examines the class to find fields.
That is, a variable that has a type annotation.
With two exceptions described below, none of the Data Class machinery examines the python namedtuple __slots__ specified in the annotation.
The order of the fields in all of the generated methods is the order in which they appear in the class.
The dataclass decorator will add various "dunder" methods to the class, described below.
If any of the added methods already exist on the https://casinos-free-list.site/1/511.html, a TypeError will be raised.
The decorator returns the same class that is called on: no new class is created.
The dataclass decorator is typically used with no parameters and no parentheses.
That is, these three uses of dataclass are equivalent: dataclass class C:.
The generated repr string will have the class name and the name and repr of each field, in the order they are defined in the class.
Fields that are marked as being excluded from the repr are not included.
This method compares the class as if it were a tuple of its fields, in order.
Both instances in the comparison ゲームバーガーショップ3 be of the identical type.
These compare the class as if it python namedtuple __slots__ a tuple of its fields, in order.
Both instances in the comparison must be of the identical type.
If order is true and eq is false, a ValueError is raised.
This might be the case if your class is logically immutable but can nonetheless be mutated.
This is python namedtuple __slots__ specialized use case and should be considered carefully.
See the Python documentation for more information.
This emulates read-only frozen instances.
See the python namedtuple __slots__ below.
This is true either when this occurs in a single class, or as a result of class inheritance.
For common and simple use cases, no other functionality is required.
There are, however, some Data Class features that require additional per-field information.
To satisfy this need for additional information, you can replace the default field value with a call to the provided field function.
This sentinel is used because None is a valid value for default.
This is needed because the field call itself replaces the normal position of the default value.
Among other purposes, this can be used to specify fields with mutable default values, as discussed below.
If None the defaultuse the value of compare: this would normally be the expected behavior.
A field should be considered in the hash if it's used for comparisons.
Setting this value to anything other than None is discouraged.
Even if a field is excluded from the hash, it will still be used for comparisons.
None is treated as an empty dict.
This value is wrapped in types.
MappingProxyType to make it read-only, and exposed on the Field object.
It is not used at all by Data Classes, and is provided as a third-party extension mechanism.
Multiple third-parties can each have their own key, to use as a namespace in the metadata.
If the default value of a field is specified by a call to fieldmore info the class attribute for this field will be replaced by the specified default value.
If no default is provided, then the class attribute will be deleted.
The intent is that after the dataclass decorator runs, the class attributes will all contain the default values for the fields, just as if the default value itself were specified.
Field objects describe each defined field.
These objects are created internally, and are returned by the fields module-level method see below.
Users should never instantiate a Field object directly.
Other attributes may exist, but they are private and must not be inspected or relied on.
It will be called as self.
Among other uses, this allows for initializing field values that depend on one or more other fields.
One place where dataclass actually inspects the type of a field is to determine if a field is a class variable as defined in.
It does this by checking if the type of the here is typing.
If a field is a ClassVar, it is excluded from consideration as a field and is ignored by the Data Class mechanisms.
For more discussion, see.
Such ClassVar pseudo-fields are not returned by the module-level fields function.
The other place where dataclass inspects a type annotation is to determine if a field is an init-only variable.
It does this by seeing if the type of a field is of type dataclasses.
If a field is an InitVar, it is considered a pseudo-field called an init-only field.
As it is not a true field, it is not returned by the module-level fields function.
They are not otherwise used by Data Classes.
It is not possible to create truly immutable Python objects.
These methods will raise a FrozenInstanceError when invoked.
When the Data Class is being created by the dataclass decorator, it looks through all of the class's base classes in reverse MRO that is, starting at object and, for each Data Class that it finds, adds the fields from that base class to an ordered mapping of fields.
After all of the base class fields are added, it adds its own fields to the ordered mapping.
All of the generated methods will use this combined, calculated ordered mapping of fields.
Because the fields are in insertion order, derived classes override base classes.
The final click of x is int, as specified in class C.
This happens because there is no other way to give the field an initial value.
Python stores default member variable values in class attributes.
That is, two instances of class D that do not specify a value for x when creating a class instance will share the same copy of x.
Because Python namedtuple __slots__ Classes just use normal Python class creation they also share this problem.
There is no python namedtuple __slots__ way for Data Classes to detect this condition.
Instead, Data Classes will raise a TypeError if it detects a default parameter of type list, dict, or set.
This is a partial solution, but it does protect against many common errors.
See in the Rejected Ideas section for more details.
Accepts either a Data Class, or an instance of a Data Class.
Raises ValueError if not passed a Data Class or instance of one.
Does not return pseudo-fields which are ClassVar or InitVar.
Each Data Class is converted to a dict of its fields, as name:value pairs.
Data Classes, dicts, lists, and tuples are recursed into.
Each Data Class is converted to a tuple of its field values.
Data Classes, dicts, lists, and tuples are recursed into.
If just name is supplied, typing.
Any is used for type.
This function is provided as a convenience.
If instance is not a Data Class, raises TypeError.
If values in changes do not specify fields, raises TypeError.
A ValueError will be raised in this case.
If they are used, it might be wise to have alternate class constructors, or perhaps a custom replace or similarly named method which handles instance copying.
As part of this discussion, we made the decision to use syntax to drive the discovery of fields.
For more discussion, see.
With Data Classes, this would return False.
With Data Classes, this would return False.
For classes with article source defined fields, it does support similar syntax to Data Classes, using type annotations.
This produces a namedtuple, so it shares namedtuples benefits and some of its downsides.
Data Classes, unlike typing.
NamedTuple, support combining fields via inheritance.
Data Classes makes a tradeoff to achieve simplicity by not implementing these features.
For more discussion, see.
The normal way of doing parameterized initialization and not just with Data Classes is to provide an alternate classmethod constructor.
The only real difference between alternate classmethod constructors and InitVar pseudo-fields is in regards to required non-field parameters during object creation.
Consider the case where a context object is needed to create an instance, but isn't click the following article as a field.
Which approach is more appropriate will be application-specific, but both approaches are supported.
This is especially important with regular fields and InitVar fields that have default values, as all fields with defaults must come after all fields without defaults.
A previous design had all init-only fields coming after regular fields.
This meant that if any field had a default value, then all init-only fields would have to have defaults values, too.
There were undesirable side effects of this decision, so the final decision is to disallow the 3 known built-in mutable types: list, dict, and set.
For a complete discussion of this and other options, see.
The following people provided invaluable input during the development of this PEP and code: Ivan Levkivskyi, Guido van Rossum, Hynek Schlawack, Raymond Hettinger, and Lisa Roach.
I thank them for their time and expertise.
A special mention must be made about the attrs project.
It was a true inspiration for this PEP, and I respect the design decisions they made.
Become a member of the PSF and help advance the software and our mission.


Python Scripting Tutorial Namedtuple When and why should you use namedtuples


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Furthermore, attrs has been around for a while and is supported in Python 2.7 as well as Python 3.4 and up. However, as attrs is not a part of the standard library, it does add an external dependency to your projects. Through data classes, similar functionality will be available everywhere.


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