Basics¶

1. Python Slots Default Value Sheet
2. Python Default Value For Input
3. Python Slots Default Value Calculator
4. Python Data Class Slots Default Value

The simplest possible usage is:

So in other words: attrs is useful even without actual attributes!

Setdefault Parameters. Setdefault takes a maximum of two parameters: key - the key to be searched in the dictionary; defaultvalue (optional) - key with a value defaultvalue is inserted to the dictionary if the key is not in the dictionary. If not provided, the defaultvalue will be None. QSpinBox is designed to handle integers and discrete sets of values (e.g., month names); use QDoubleSpinBox for floating point values. QSpinBox allows the user to choose a value by clicking the up/down buttons or pressing up/down on the keyboard to increase/decrease the value currently displayed. The user can also type the value in manually. The spin box supports integer values but can. In Python every class can have instance attributes. By default Python uses a dict to store an object’s instance attributes. This is really helpful as it allows setting arbitrary new attributes at runtime. However, for small classes with known attributes it might be a bottleneck. The dict wastes a lot of RAM. Python can’t just allocate a. # SLOT: This has default parameters and can be called without a value def mycustomfn(self, a='HELLLO!' , b=5): print(a, b) app = QApplication(sys.argv) w = MainWindow w.show app.exec The.setWindowTitle call at the end of the init block changes the window title and triggers the.windowTitleChanged signal, which emits the new window. As a result, class attributes cannot be used to set default values for instance variables defined by slots; otherwise, the class attribute would overwrite the descriptor assignment. If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its.

But you’ll usually want some data on your classes, so let’s add some:

By default, all features are added, so you immediately have a fully functional data class with a nice repr string and comparison methods.

As shown, the generated __init__ method allows for both positional and keyword arguments.

If playful naming turns you off, attrs comes with serious-business aliases:

For private attributes, attrs will strip the leading underscores for keyword arguments:

If you want to initialize your private attributes yourself, you can do that too:

An additional way of defining attributes is supported too.This is useful in times when you want to enhance classes that are not yours (nice __repr__ for Django models anyone?):

Subclassing is bad for you, but attrs will still do what you’d hope for:

The order of the attributes is defined by the MRO.

In Python 3, classes defined within other classes are detected and reflected in the __repr__.In Python 2 though, it’s impossible.Therefore @attr.s comes with the repr_ns option to set it manually:

repr_ns works on both Python 2 and 3.On Python 3 it overrides the implicit detection.

Keyword-only Attributes¶

You can also add keyword-only attributes:

kw_only may also be specified at via attr.s, and will apply to all attributes:

If you create an attribute with init=False, the kw_only argument is ignored.

Keyword-only attributes allow subclasses to add attributes without default values, even if the base class defines attributes with default values:

If you don’t set kw_only=True, then there’s is no valid attribute ordering and you’ll get an error:

Converting to Collections Types¶

When you have a class with data, it often is very convenient to transform that class into a dict (for example if you want to serialize it to JSON):

Some fields cannot or should not be transformed.For that, attr.asdict offers a callback that decides whether an attribute should be included:

For the common case where you want to include or exclude certain types or attributes, attrs ships with a few helpers:

Other times, all you want is a tuple and attrs won’t let you down:

Defaults¶

Sometimes you want to have default values for your initializer.And sometimes you even want mutable objects as default values (ever used accidentally deff(arg=[])?).attrs has you covered in both cases:

More information on why class methods for constructing objects are awesome can be found in this insightful blog post.

Default factories can also be set using a decorator.The method receives the partially initialized instance which enables you to base a default value on other attributes:

And since the case of attr.ib(default=attr.Factory(f)) is so common, attrs also comes with syntactic sugar for it:

Validators¶

Although your initializers should do as little as possible (ideally: just initialize your instance according to the arguments!), it can come in handy to do some kind of validation on the arguments.

attrs offers two ways to define validators for each attribute and it’s up to you to choose which one suits your style and project better.

You can use a decorator:

…or a callable…

…or both at once:

Please note that the decorator approach only works if – and only if! – the attribute in question has an attr.ib assigned.Therefore if you use @attr.s(auto_attribs=True), it is not enough to decorate said attribute with a type.

attrs ships with a bunch of validators, make sure to check them out before writing your own:

Check out Validators for more details.

Conversion¶

Attributes can have a converter function specified, which will be called with the attribute’s passed-in value to get a new value to use.This can be useful for doing type-conversions on values that you don’t want to force your callers to do.

Check out Converters for more details.

Metadata¶

All attrs attributes may include arbitrary metadata in the form of a read-only dictionary.

Metadata is not used by attrs, and is meant to enable rich functionality in third-party libraries.The metadata dictionary follows the normal dictionary rules: keys need to be hashable, and both keys and values are recommended to be immutable.

If you’re the author of a third-party library with attrs integration, please see Extending Metadata.

Types¶

attrs also allows you to associate a type with an attribute using either the type argument to attr.ib or – as of Python 3.6 – using PEP 526-annotations:

If you don’t mind annotating all attributes, you can even drop the attr.ib and assign default values instead:

The generated __init__ method will have an attribute called __annotations__ that contains this type information.

If your annotations contain strings (e.g. forward references),you can resolve these after all references have been defined by using attr.resolve_types().This will replace the type attribute in the respective fields.

Warning

attrs itself doesn’t have any features that work on top of type metadata yet.However it’s useful for writing your own validators or serialization frameworks.

Slots¶

Slotted classes have several advantages on CPython.Defining __slots__ by hand is tedious, in attrs it’s just a matter of passing slots=True:

Immutability¶

Sometimes you have instances that shouldn’t be changed after instantiation.Immutability is especially popular in functional programming and is generally a very good thing.If you’d like to enforce it, attrs will try to help:

Please note that true immutability is impossible in Python but it will get you 99% there.By themselves, immutable classes are useful for long-lived objects that should never change; like configurations for example.

In order to use them in regular program flow, you’ll need a way to easily create new instances with changed attributes.In Clojure that function is called assoc and attrs shamelessly imitates it: attr.evolve:

Other Goodies¶

Sometimes you may want to create a class programmatically.attrs won’t let you down and gives you attr.make_class :

You can still have power over the attributes if you pass a dictionary of name: attr.ib mappings and can pass arguments to @attr.s:

If you need to dynamically make a class with attr.make_class and it needs to be a subclass of something else than object, use the bases argument:

Sometimes, you want to have your class’s __init__ method do more than justthe initialization, validation, etc. that gets done for you automatically whenusing @attr.s.To do this, just define a __attrs_post_init__ method in your class.It will get called at the end of the generated __init__ method.

Python Slots Default Value Sheet

You can exclude single attributes from certain methods:

Alternatively, to influence how the generated __repr__() method formats a specific attribute, specify a custom callable to be used instead of the repr() built-in function:

In Python every class can have instance attributes. By default Pythonuses a dict to store an object’s instance attributes. This is reallyhelpful as it allows setting arbitrary new attributes at runtime.

However, for small classes with known attributes it might be abottleneck. The dict wastes a lot of RAM. Python can’t just allocatea static amount of memory at object creation to store all theattributes. Therefore it sucks a lot of RAM if you create a lot ofobjects (I am talking in thousands and millions). Still there is a wayto circumvent this issue. It involves the usage of __slots__ totell Python not to use a dict, and only allocate space for a fixed setof attributes. Here is an example with and without __slots__:

Without__slots__:

Python Default Value For Input

With__slots__:

Python Slots Default Value Calculator

The second piece of code will reduce the burden on your RAM. Some peoplehave seen almost 40 to 50% reduction in RAM usage by using thistechnique.

On a sidenote, you might want to give PyPy a try. It does all of theseoptimizations by default.

Python Data Class Slots Default Value

Below you can see an example showing exact memory usage with and without __slots__ done in IPython thanks to https://github.com/ianozsvald/ipython_memory_usage