__slots__ has a mixed reputation in the Python community. On the one hand, they are considered to be popular. Lots of people like using them. Others say that they are badly understood, tricky to get right, and don't have much of an effect unless there are many instances of objects that use them. This article will explain what they are, how, and why to use them, and when not to use them.
What Is `__slots__` ?
__slots__ are discussed in the Python Language Reference under section 3.3.2, Customizing Attribute Access. The first thing we should understand is that __slots__ is only used in the context of Python classes. __slots__ is a class variable that is usually assigned a sequence of strings that are variable names used by instances. For example:
class Example(): __slots__ = ('_slot_0', '_slot_1')
This declaration allows us to explicitly declare data members, causes Python to reserve space for them in memory, and prevents the creation of __dict__ and __weakref__ attributes. It also prevents the creation of any variables that aren't declared in __slots__.
Why Use `__slots__`?
The short answer is slots are more efficient in terms of memory space and speed of access, and a bit safer than the default Python method of data access. By default, when Python creates a new instance of a class, it creates a __dict__ attribute for the class. The __dict__ attribute is a dictionary whose keys are the variable names and whose values are the variable values. For example, here is a simple class definition:
class Example(): def __init__(self): self.var_0 = 'This is variable 0' self.var_1 = 'This is variable 1'
An interactive session looks like this:
>>> x = Example() >>> print(x.__dict__.keys()) dict_keys(['var_0', 'var_1']) >>> print(x.__dict__.values()) dict_values(['This is variable 0', 'This is variable 1'])
We can now assign values to the variables with dot notation.
>>> x.var_0 = 'zero' >>> x.var1 = 'one' >>> print(x.__dict__.keys()) dict_keys(['var_0', 'var_1', 'var1']) >>> print(x.__dict__.values()) dict_values(['zero', 'This is variable 1', 'one'])
The output here is not what was expected. The underscore was left out of the var_1 assignment and as a result we now have an extra variable in our dictionary named 'var1'. With __slots__, a misspelled variable name results in an Attribute Error. __slots__ uses less memory space than __dict__, and direct memory access is faster than dictionary lookups. These are some of the reasons why you might want to use slots.
In the basic case, slots are easy to use. For example, here is a simple class definition:
class Example(): __slots__ = ('_slot_0', '_slot_1') def __init__(self): self._slot_0 = 'This is slot 0' self._slot_1 = 'This is slot 1'
An interpreter session looks like this:
>>> x = Example() >>> print(x._slot_0) This is slot 0 >>> print(x._slot_1) This is slot 1 >>> x._slot_0 = 'Is _slot_1 here?' >>> x._slot_1 = "_slot_1 is here." >>> print(x._slot_0, "\n", x._slot_1) Is _slot_1 here? _slot_1 is here.
That's it. To the user who is writing code, slots look and behave exactly like the default. Once we've declared and initialized our variables, we can reference and assign to them using dot notation.
Using A List For Slots
The previous example used a tuple for the __slots__ declaration. The Python documentation states that any non-string iterable can be used. This section describes how slots behavior can change when using a list for the declaration.
For the most part, using a list for the __slots__ declaration looks and works exactly like using a tuple.
class Example(): __slots__ = ['_slot_0', '_slot_1'] def __init__(self): self._slot_0 = 'This is slot 0' self._slot_1 = 'This is slot 1'
Using the same code as the previous interpreter session will produce exactly the same results. But unlike a tuple, a list is mutable, and there may be cases when we want to use the list. For example, we can implement get and set functions that access the list, as shown below.
def _set(self, _slot, _value): self.__slots__[_slot] = _value return None def _get(self, _slot): return self.__slots__[_slot]
Attempting to use both dot notation and getters and setters will produce results that may be surprising. Using the two previous examples,
>>> x = Example() >>> print(x._slot_0) This is slot 0 >>> print(x._get(0)) _slot_0 >>> x._set(0, 'zero') >>> print(x._slot_0) This is slot 0 >>> print(x._get(0)) zero
This session shows that accessing x._slot_0 works as it did before, but attempting to access _slot_0 with the get function returns only the name of the variable. If we then use the set function to insert a value, we see that the value returned by x._slot_0 hasn't changed, but the get function retrieves the value inserted by the set function.
Python does not allow access through the list to data that has been set through __slots__ variables, and we cannot overwrite __slots__ variables by writing to the list directly. But we can still use the list like any other list without altering the variable names or the data they contain. This behavior can be useful in some cases. For example, when collecting data to be written to a file, you may want to assign values using the variable names. When reading the same data from a file, it may be more efficient to use the list, allowing you to return the entire data structure.
The following text appears in the Python Language Reference section 22.214.171.124.1:
"__slots__ are implemented at the class level by creating descriptors ... for each variable name. 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."
It is not necessary to explicitly implement descriptors in order to use slots. The point here is that default values cannot be set using class attributes.
class Example(): __slots__ = ['_slot_0', '_slot_1'] _slot_0 = 'This is slot 0' _slot_1 = 'This is slot 1' Out: Value Error: '_slot_0' in __slots__ conflicts with class variable
class Example(): __slots__ = ['_slot_0', '_slot_1'] __slots__ = 'This is slot 0' __slots__ = 'This is slot 1' Out: Type Error: __slots__ must be identifiers
If desired, default values for __slots__ variables may be set in the class __init__() method, as shown in previous examples, but it is not necessary to do so. Values may be assigned to slots variables that have not been initialized with default values.
class Example(): __slots__ = ['_slot_0', '_slot_1'] def __init__(self): return None >>> x = Example() >>> x._slot_0 = 'zero' >>> print(x._slot_0) Out: zero
Why Not Use Slots?
There may be cases when you might not want to use __slots__; for example, if you would like for your class to use dynamic attribute creation or weak references. In those cases, you can add '__dict__' or '__weakref__' as the last element in the __slots__ declaration.
Certain Python objects may depend on the __dict__ attribute. For example, descriptor classes depend on the __dict__ attribute being present in the owner class. Programmers may want to avoid __slots__ in any case where another Python object requires __dict__ or __weak_ref__to be present. According to the Descriptor How To Guide for Python 3.9, the functools.cached_property() is another example that requires an instance dictionary to function correctly.
Beyond The Basics
There are a few things to be aware of when going beyond the basics. Slots variables declared in parents are available in child classes. However, child subclasses will get a __dict__ and __weakref__ unless they also define __slots__, which should only contain names of additional slots. Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots. The other bases must have empty slot layouts. For additional details, please see the Python Language Reference, section 126.96.36.199.1.
Slots are a simple, easy to use, efficient, and safe alternative to Python's default method of data access. The only known exception is when another object requires access to the __dict__ attribute.