Slots

Avoiding Dynamically Created Attributes

The attributes of objects are stored in a dictionary __dict__. Like any other dictionary, a dictionary used for attribute storage doesn't have a fixed number of elements. In other words, you can add elements to dictionaries after they are defined, as we have seen in our chapter on dictionaries. This is the reason, why you can dynamically add attributes to objects of classes that we have created so far:

Python Slots Default Values

The dictionary containing the attributes of 'a' can be accessed like this:

Python Data Class Slots Default Value

You might have wondered that you can dynamically add attributes to the classes, we have defined so far, but that you can't do this with built-in classes like 'int', or 'list':

Values: This option represents the valid values contained in the tuple for the widget. This option can override from, to and increment. Vcmd: This option represents the callback of validation. There is no default value for this option. Wrap: There is a wrapping of up and down buttons if the condition is true. Relief: SUNKEN is the default value. 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. 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.

Using a dictionary for attribute storage is very convenient, but it can mean a waste of space for objects, which have only a small amount of instance variables. The space consumption can become critical when creating large numbers of instances. Slots are a nice way to work around this space consumption problem. Instead of having a dynamic dict that allows adding attributes to objects dynamically, slots provide a static structure which prohibits additions after the creation of an instance.

When we design a class, we can use slots to prevent the dynamic creation of attributes. To define slots, you have to define a list with the name __slots__. The list has to contain all the attributes, you want to use. We demonstrate this in the following class, in which the slots list contains only the name for an attribute 'val'.

If we start this program, we can see, that it is not possible to create dynamically a new attribute. We fail to create an attribute 'new'.

We mentioned in the beginning that slots are preventing a waste of space with objects. Since Python 3.3 this advantage is not as impressive any more. With Python 3.3 Key-Sharing Dictionaries are used for the storage of objects. The attributes of the instances are capable of sharing part of their internal storage between each other, i.e. the part which stores the keys and their corresponding hashes. This helps reducing the memory consumption of programs, which create many instances of non-builtin types.