Python classes

PyO3 exposes a group of attributes powered by Rust's proc macro system for defining Python classes as Rust structs.

The main attribute is #[pyclass], which is placed upon a Rust struct or a fieldless enum (a.k.a. C-like enum) to generate a Python type for it. They will usually also have one #[pymethods]-annotated impl block for the struct, which is used to define Python methods and constants for the generated Python type. (If the multiple-pymethods feature is enabled each #[pyclass] is allowed to have multiple #[pymethods] blocks.) #[pymethods] may also have implementations for Python magic methods such as __str__.

This chapter will discuss the functionality and configuration these attributes offer. Below is a list of links to the relevant section of this chapter for each:

• #[pyclass]
  • #[pyo3(get, set)]
• #[pymethods]
  • #[new]
  • #[getter]
  • #[setter]
  • #[staticmethod]
  • #[classmethod]
  • #[classattr]
  • #[args]
• Magic methods and slots

Defining a new class

To define a custom Python class, add the #[pyclass] attribute to a Rust struct or a fieldless enum.

#![allow(unused)]
fn main() {
#![allow(dead_code)]
use pyo3::prelude::*;

#[pyclass]
struct Integer{
    inner: i32
}

// A "tuple" struct
#[pyclass]
struct Number(i32);

// PyO3 supports custom discriminants in enums
#[pyclass]
enum HttpResponse {
    Ok = 200,
    NotFound = 404,
    Teapot = 418,
    // ...
}

#[pyclass]
enum MyEnum {
    Variant,
    OtherVariant = 30, // PyO3 supports custom discriminants.
}
}

The above example generates implementations for PyTypeInfo and PyClass for MyClass and MyEnum. To see these generated implementations, refer to the implementation details at the end of this chapter.

Restrictions

To integrate Rust types with Python, PyO3 needs to place some restrictions on the types which can be annotated with #[pyclass]. In particular, they must have no lifetime parameters, no generic parameters, and must implement Send. The reason for each of these is explained below.

No lifetime parameters

Rust lifetimes are used by the Rust compiler to reason about a program's memory safety. They are a compile-time only concept; there is no way to access Rust lifetimes at runtime from a dynamic language like Python.

As soon as Rust data is exposed to Python, there is no guarantee which the Rust compiler can make on how long the data will live. Python is a reference-counted language and those references can be held for an arbitrarily long time which is untraceable by the Rust compiler. The only possible way to express this correctly is to require that any #[pyclass] does not borrow data for any lifetime shorter than the 'static lifetime, i.e. the #[pyclass] cannot have any lifetime parameters.

When you need to share ownership of data between Python and Rust, instead of using borrowed references with lifetimes consider using reference-counted smart pointers such as Arc or Py.

No generic parameters

A Rust struct Foo<T> with a generic parameter T generates new compiled implementations each time it is used with a different concrete type for T. These new implementations are generated by the compiler at each usage site. This is incompatible with wrapping Foo in Python, where there needs to be a single compiled implementation of Foo which is integrated with the Python interpreter.

Must be send

Because Python objects are freely shared between threads by the Python interpreter, there is no guarantee which thread will eventually drop the object. Therefore all types annotated with #[pyclass] must implement Send (unless annotated with #[pyclass(unsendable)]).

Constructor

By default it is not possible to create an instance of a custom class from Python code. To declare a constructor, you need to define a method and annotate it with the #[new] attribute. Only Python's __new__ method can be specified, __init__ is not available.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct Number(i32);

#[pymethods]
impl Number {
    #[new]
    fn new(value: i32) -> Self {
        Number(value)
    }
}
}

Alternatively, if your new method may fail you can return PyResult<Self>.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::exceptions::PyValueError;
#[pyclass]
struct Nonzero(i32);

#[pymethods]
impl Nonzero {
    #[new]
    fn py_new(value: i32) -> PyResult<Self> {
        if value == 0 {
            Err(PyValueError::new_err("cannot be zero"))
        } else {
            Ok(Nonzero(value))
        }
    }
}
}

As you can see, the Rust method name is not important here; this way you can still use new() for a Rust-level constructor.

If no method marked with #[new] is declared, object instances can only be created from Rust, but not from Python.

For arguments, see the Method arguments section below.

Adding the class to a module

The next step is to create the module initializer and add our class to it

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct Number(i32);

#[pymodule]
fn my_module(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
    m.add_class::<Number>()?;
    Ok(())
}
}

PyCell and interior mutability

You sometimes need to convert your pyclass into a Python object and access it from Rust code (e.g., for testing it). PyCell is the primary interface for that.

PyCell<T: PyClass> is always allocated in the Python heap, so Rust doesn't have ownership of it. In other words, Rust code can only extract a &PyCell<T>, not a PyCell<T>.

Thus, to mutate data behind &PyCell safely, PyO3 employs the Interior Mutability Pattern like RefCell.

Users who are familiar with RefCell can use PyCell just like RefCell.

For users who are not very familiar with RefCell, here is a reminder of Rust's rules of borrowing:

• At any given time, you can have either (but not both of) one mutable reference or any number of immutable references.
• References must always be valid.

PyCell, like RefCell, ensures these borrowing rules by tracking references at runtime.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    #[pyo3(get)]
    num: i32,
}
Python::with_gil(|py| {
    let obj = PyCell::new(py, MyClass { num: 3}).unwrap();
    {
        let obj_ref = obj.borrow(); // Get PyRef
        assert_eq!(obj_ref.num, 3);
        // You cannot get PyRefMut unless all PyRefs are dropped
        assert!(obj.try_borrow_mut().is_err());
    }
    {
        let mut obj_mut = obj.borrow_mut(); // Get PyRefMut
        obj_mut.num = 5;
        // You cannot get any other refs until the PyRefMut is dropped
        assert!(obj.try_borrow().is_err());
        assert!(obj.try_borrow_mut().is_err());
    }

    // You can convert `&PyCell` to a Python object
    pyo3::py_run!(py, obj, "assert obj.num == 5");
});
}

&PyCell<T> is bounded by the same lifetime as a GILGuard. To make the object longer lived (for example, to store it in a struct on the Rust side), you can use Py<T>, which stores an object longer than the GIL lifetime, and therefore needs a Python<'_> token to access.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}

fn return_myclass() -> Py<MyClass> {
    Python::with_gil(|py| Py::new(py, MyClass { num: 1 }).unwrap())
}

let obj = return_myclass();

Python::with_gil(|py|{
    let cell = obj.as_ref(py); // Py<MyClass>::as_ref returns &PyCell<MyClass>
    let obj_ref = cell.borrow(); // Get PyRef<T>
    assert_eq!(obj_ref.num, 1);
});
}

Customizing the class

#[pyclass] can be used with the following parameters:

All of these parameters can either be passed directly on the #[pyclass(...)] annotation, or as one or more accompanying #[pyo3(...)] annotations, e.g.:

// Argument supplied directly to the `#[pyclass]` annotation.
#[pyclass(name = "SomeName", subclass)]
struct MyClass { }

// Argument supplied as a separate annotation.
#[pyclass]
#[pyo3(name = "SomeName", subclass)]
struct MyClass { }

These parameters are covered in various sections of this guide.

Return type

Generally, #[new] method have to return T: Into<PyClassInitializer<Self>> or PyResult<T> where T: Into<PyClassInitializer<Self>>.

For constructors that may fail, you should wrap the return type in a PyResult as well. Consult the table below to determine which type your constructor should return:

Inheritance

By default, PyAny is used as the base class. To override this default, use the extends parameter for pyclass with the full path to the base class.

For convenience, (T, U) implements Into<PyClassInitializer<T>> where U is the baseclass of T. But for more deeply nested inheritance, you have to return PyClassInitializer<T> explicitly.

To get a parent class from a child, use PyRef instead of &self for methods, or PyRefMut instead of &mut self. Then you can access a parent class by self_.as_ref() as &Self::BaseClass, or by self_.into_super() as PyRef<Self::BaseClass>.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;

#[pyclass(subclass)]
struct BaseClass {
    val1: usize,
}

#[pymethods]
impl BaseClass {
    #[new]
    fn new() -> Self {
        BaseClass { val1: 10 }
    }

    pub fn method(&self) -> PyResult<usize> {
        Ok(self.val1)
    }
}

#[pyclass(extends=BaseClass, subclass)]
struct SubClass {
    val2: usize,
}

#[pymethods]
impl SubClass {
    #[new]
    fn new() -> (Self, BaseClass) {
        (SubClass { val2: 15 }, BaseClass::new())
    }

    fn method2(self_: PyRef<'_, Self>) -> PyResult<usize> {
        let super_ = self_.as_ref();  // Get &BaseClass
        super_.method().map(|x| x * self_.val2)
    }
}

#[pyclass(extends=SubClass)]
struct SubSubClass {
    val3: usize,
}

#[pymethods]
impl SubSubClass {
    #[new]
    fn new() -> PyClassInitializer<Self> {
        PyClassInitializer::from(SubClass::new())
            .add_subclass(SubSubClass{val3: 20})
    }

    fn method3(self_: PyRef<'_, Self>) -> PyResult<usize> {
        let v = self_.val3;
        let super_ = self_.into_super();  // Get PyRef<'_, SubClass>
        SubClass::method2(super_).map(|x| x * v)
    }
}
Python::with_gil(|py| {
    let subsub = pyo3::PyCell::new(py, SubSubClass::new()).unwrap();
    pyo3::py_run!(py, subsub, "assert subsub.method3() == 3000")
});
}

You can also inherit native types such as PyDict, if they implement PySizedLayout. However, this is not supported when building for the Python limited API (aka the abi3 feature of PyO3).

However, because of some technical problems, we don't currently provide safe upcasting methods for types that inherit native types. Even in such cases, you can unsafely get a base class by raw pointer conversion.

#![allow(unused)]
fn main() {
#[cfg(not(Py_LIMITED_API))] {
use pyo3::prelude::*;
use pyo3::types::PyDict;
use pyo3::AsPyPointer;
use std::collections::HashMap;

#[pyclass(extends=PyDict)]
#[derive(Default)]
struct DictWithCounter {
    counter: HashMap<String, usize>,
}

#[pymethods]
impl DictWithCounter {
    #[new]
    fn new() -> Self {
        Self::default()
    }
    fn set(mut self_: PyRefMut<'_, Self>, key: String, value: &PyAny) -> PyResult<()> {
        self_.counter.entry(key.clone()).or_insert(0);
        let py = self_.py();
        let dict: &PyDict = unsafe { py.from_borrowed_ptr_or_err(self_.as_ptr())? };
        dict.set_item(key, value)
    }
}
Python::with_gil(|py| {
    let cnt = pyo3::PyCell::new(py, DictWithCounter::new()).unwrap();
    pyo3::py_run!(py, cnt, "cnt.set('abc', 10); assert cnt['abc'] == 10")
});
}
}

If SubClass does not provide a baseclass initialization, the compilation fails.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;

#[pyclass]
struct BaseClass {
    val1: usize,
}

#[pyclass(extends=BaseClass)]
struct SubClass {
    val2: usize,
}

#[pymethods]
impl SubClass {
    #[new]
    fn new() -> Self {
        SubClass { val2: 15 }
    }
}
}

Object properties

PyO3 supports two ways to add properties to your #[pyclass]:

• For simple struct fields with no side effects, a #[pyo3(get, set)] attribute can be added directly to the field definition in the #[pyclass].
• For properties which require computation you can define #[getter] and #[setter] functions in the #[pymethods] block.

We'll cover each of these in the following sections.

Object properties using #[pyo3(get, set)]

For simple cases where a member variable is just read and written with no side effects, you can declare getters and setters in your #[pyclass] field definition using the pyo3 attribute, like in the example below:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    #[pyo3(get, set)]
    num: i32
}
}

The above would make the num field available for reading and writing as a self.num Python property. To expose the property with a different name to the field, specify this alongside the rest of the options, e.g. #[pyo3(get, set, name = "custom_name")].

Properties can be readonly or writeonly by using just #[pyo3(get)] or #[pyo3(set)] respectively.

To use these annotations, your field type must implement some conversion traits:

• For get the field type must implement both IntoPy<PyObject> and Clone.
• For set the field type must implement FromPyObject.

Object properties using #[getter] and #[setter]

For cases which don't satisfy the #[pyo3(get, set)] trait requirements, or need side effects, descriptor methods can be defined in a #[pymethods] impl block.

This is done using the #[getter] and #[setter] attributes, like in the example below:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}

#[pymethods]
impl MyClass {
    #[getter]
    fn num(&self) -> PyResult<i32> {
        Ok(self.num)
    }
}
}

A getter or setter's function name is used as the property name by default. There are several ways how to override the name.

If a function name starts with get_ or set_ for getter or setter respectively, the descriptor name becomes the function name with this prefix removed. This is also useful in case of Rust keywords like type (raw identifiers can be used since Rust 2018).

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}
#[pymethods]
impl MyClass {
    #[getter]
    fn get_num(&self) -> PyResult<i32> {
        Ok(self.num)
    }

    #[setter]
    fn set_num(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

In this case, a property num is defined and available from Python code as self.num.

Both the #[getter] and #[setter] attributes accept one parameter. If this parameter is specified, it is used as the property name, i.e.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
   num: i32,
}
#[pymethods]
impl MyClass {
    #[getter(number)]
    fn num(&self) -> PyResult<i32> {
        Ok(self.num)
    }

    #[setter(number)]
    fn set_num(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

In this case, the property number is defined and available from Python code as self.number.

Attributes defined by #[setter] or #[pyo3(set)] will always raise AttributeError on del operations. Support for defining custom del behavior is tracked in #1778.

Instance methods

To define a Python compatible method, an impl block for your struct has to be annotated with the #[pymethods] attribute. PyO3 generates Python compatible wrappers for all functions in this block with some variations, like descriptors, class method static methods, etc.

Since Rust allows any number of impl blocks, you can easily split methods between those accessible to Python (and Rust) and those accessible only to Rust. However to have multiple #[pymethods]-annotated impl blocks for the same struct you must enable the multiple-pymethods feature of PyO3.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    num: i32,
}
#[pymethods]
impl MyClass {
    fn method1(&self) -> PyResult<i32> {
        Ok(10)
    }

    fn set_method(&mut self, value: i32) -> PyResult<()> {
        self.num = value;
        Ok(())
    }
}
}

Calls to these methods are protected by the GIL, so both &self and &mut self can be used. The return type must be PyResult<T> or T for some T that implements IntoPy<PyObject>; the latter is allowed if the method cannot raise Python exceptions.

A Python parameter can be specified as part of method signature, in this case the py argument gets injected by the method wrapper, e.g.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
#[allow(dead_code)]
    num: i32,
}
#[pymethods]
impl MyClass {
    fn method2(&self, py: Python<'_>) -> PyResult<i32> {
        Ok(10)
    }
}
}

From the Python perspective, the method2 in this example does not accept any arguments.

Class methods

To create a class method for a custom class, the method needs to be annotated with the #[classmethod] attribute. This is the equivalent of the Python decorator @classmethod.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::PyType;
#[pyclass]
struct MyClass {
    #[allow(dead_code)]
    num: i32,
}
#[pymethods]
impl MyClass {
    #[classmethod]
    fn cls_method(cls: &PyType) -> PyResult<i32> {
        Ok(10)
    }
}
}

Declares a class method callable from Python.

• The first parameter is the type object of the class on which the method is called. This may be the type object of a derived class.
• The first parameter implicitly has type &PyType.
• For details on parameter-list, see the documentation of Method arguments section.
• The return type must be PyResult<T> or T for some T that implements IntoPy<PyObject>.

Static methods

To create a static method for a custom class, the method needs to be annotated with the #[staticmethod] attribute. The return type must be T or PyResult<T> for some T that implements IntoPy<PyObject>.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {
    #[allow(dead_code)]
    num: i32,
}
#[pymethods]
impl MyClass {
    #[staticmethod]
    fn static_method(param1: i32, param2: &str) -> PyResult<i32> {
        Ok(10)
    }
}
}

Class attributes

To create a class attribute (also called class variable), a method without any arguments can be annotated with the #[classattr] attribute.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {}
#[pymethods]
impl MyClass {
    #[classattr]
    fn my_attribute() -> String {
        "hello".to_string()
    }
}

Python::with_gil(|py| {
    let my_class = py.get_type::<MyClass>();
    pyo3::py_run!(py, my_class, "assert my_class.my_attribute == 'hello'")
});
}

Note: if the method has a Result return type and returns an Err, PyO3 will panic during class creation.

If the class attribute is defined with const code only, one can also annotate associated constants:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
struct MyClass {}
#[pymethods]
impl MyClass {
    #[classattr]
    const MY_CONST_ATTRIBUTE: &'static str = "foobar";
}
}

Method arguments

Similar to #[pyfunction], the #[args] attribute can be used to specify the way that #[pymethods] accept arguments. Consult the documentation for function signatures to see the parameters this attribute accepts.

The following example defines a class MyClass with a method method. This method has an #[args] attribute which sets default values for num and name, and indicates that py_args should collect all extra positional arguments and py_kwargs all extra keyword arguments:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
use pyo3::types::{PyDict, PyTuple};

#[pyclass]
struct MyClass {
    num: i32,
}
#[pymethods]
impl MyClass {
    #[new]
    #[args(num = "-1")]
    fn new(num: i32) -> Self {
        MyClass { num }
    }

    #[args(
        num = "10",
        py_args = "*",
        name = "\"Hello\"",
        py_kwargs = "**"
    )]
    fn method(
        &mut self,
        num: i32,
        name: &str,
        py_args: &PyTuple,
        py_kwargs: Option<&PyDict>,
    ) -> String {
        let num_before = self.num;
        self.num = num;
        format!(
            "py_args={:?}, py_kwargs={:?}, name={}, num={} num_before={}",
            py_args, py_kwargs, name, self.num, num_before,
        )
    }
}
}

In Python this might be used like:

>>> import mymodule
>>> mc = mymodule.MyClass()
>>> print(mc.method(44, False, "World", 666, x=44, y=55))
py_args=('World', 666), py_kwargs=Some({'x': 44, 'y': 55}), name=Hello, num=44, num_before=-1
>>> print(mc.method(num=-1, name="World"))
py_args=(), py_kwargs=None, name=World, num=-1, num_before=44

Making class method signatures available to Python

The text_signature = "..." option for #[pyfunction] also works for classes and methods:

#![allow(dead_code)]
use pyo3::prelude::*;
use pyo3::types::PyType;

// it works even if the item is not documented:
#[pyclass(text_signature = "(c, d, /)")]
struct MyClass {}

#[pymethods]
impl MyClass {
    // the signature for the constructor is attached
    // to the struct definition instead.
    #[new]
    fn new(c: i32, d: &str) -> Self {
        Self {}
    }
    // the self argument should be written $self
    #[pyo3(text_signature = "($self, e, f)")]
    fn my_method(&self, e: i32, f: i32) -> i32 {
        e + f
    }
    #[classmethod]
    #[pyo3(text_signature = "(cls, e, f)")]
    fn my_class_method(cls: &PyType, e: i32, f: i32) -> i32 {
        e + f
    }
    #[staticmethod]
    #[pyo3(text_signature = "(e, f)")]
    fn my_static_method(e: i32, f: i32) -> i32 {
        e + f
    }
}

fn main() -> PyResult<()> {
    Python::with_gil(|py| {
        let inspect = PyModule::import(py, "inspect")?.getattr("signature")?;
        let module = PyModule::new(py, "my_module")?;
        module.add_class::<MyClass>()?;
        let class = module.getattr("MyClass")?;

        if cfg!(not(Py_LIMITED_API)) || py.version_info() >= (3, 10)  {
            let doc: String = class.getattr("__doc__")?.extract()?;
            assert_eq!(doc, "");

            let sig: String = inspect
                .call1((class,))?
                .call_method0("__str__")?
                .extract()?;
            assert_eq!(sig, "(c, d, /)");
        } else {
            let doc: String = class.getattr("__doc__")?.extract()?;
            assert_eq!(doc, "");

            inspect.call1((class,)).expect_err("`text_signature` on classes is not compatible with compilation in `abi3` mode until Python 3.10 or greater");
         }

        {
            let method = class.getattr("my_method")?;

            assert!(method.getattr("__doc__")?.is_none());

            let sig: String = inspect
                .call1((method,))?
                .call_method0("__str__")?
                .extract()?;
            assert_eq!(sig, "(self, /, e, f)");
        }

        {
            let method = class.getattr("my_class_method")?;

            assert!(method.getattr("__doc__")?.is_none());

            let sig: String = inspect
                .call1((method,))?
                .call_method0("__str__")?
                .extract()?;
            assert_eq!(sig, "(cls, e, f)");
        }

        {
            let method = class.getattr("my_static_method")?;

            assert!(method.getattr("__doc__")?.is_none());

            let sig: String = inspect
                .call1((method,))?
                .call_method0("__str__")?
                .extract()?;
            assert_eq!(sig, "(e, f)");
        }

        Ok(())
    })
}

Note that text_signature on classes is not compatible with compilation in abi3 mode until Python 3.10 or greater.

#[pyclass] enums

Currently PyO3 only supports fieldless enums. PyO3 adds a class attribute for each variant, so you can access them in Python without defining #[new]. PyO3 also provides default implementations of __richcmp__ and __int__, so they can be compared using ==:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
enum MyEnum {
    Variant,
    OtherVariant,
}

Python::with_gil(|py| {
    let x = Py::new(py, MyEnum::Variant).unwrap();
    let y = Py::new(py, MyEnum::OtherVariant).unwrap();
    let cls = py.get_type::<MyEnum>();
    pyo3::py_run!(py, x y cls, r#"
        assert x == cls.Variant
        assert y == cls.OtherVariant
        assert x != y
    "#)
})
}

You can also convert your enums into int:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
enum MyEnum {
    Variant,
    OtherVariant = 10,
}

Python::with_gil(|py| {
    let cls = py.get_type::<MyEnum>();
    let x = MyEnum::Variant as i32; // The exact value is assigned by the compiler.
    pyo3::py_run!(py, cls x, r#"
        assert int(cls.Variant) == x
        assert int(cls.OtherVariant) == 10
        assert cls.OtherVariant == 10  # You can also compare against int.
        assert 10 == cls.OtherVariant
    "#)
})
}

PyO3 also provides __repr__ for enums:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
enum MyEnum{
    Variant,
    OtherVariant,
}

Python::with_gil(|py| {
    let cls = py.get_type::<MyEnum>();
    let x = Py::new(py, MyEnum::Variant).unwrap();
    pyo3::py_run!(py, cls x, r#"
        assert repr(x) == 'MyEnum.Variant'
        assert repr(cls.OtherVariant) == 'MyEnum.OtherVariant'
    "#)
})
}

All methods defined by PyO3 can be overridden. For example here's how you override __repr__:

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass]
enum MyEnum {
    Answer = 42,
}

#[pymethods]
impl MyEnum {
    fn __repr__(&self) -> &'static str {
        "42"
    }
}

Python::with_gil(|py| {
    let cls = py.get_type::<MyEnum>();
    pyo3::py_run!(py, cls, "assert repr(cls.Answer) == '42'")
})
}

Enums and their variants can also be renamed using #[pyo3(name)].

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass(name = "RenamedEnum")]
enum MyEnum {
    #[pyo3(name = "UPPERCASE")]
    Variant,
}

Python::with_gil(|py| {
    let x = Py::new(py, MyEnum::Variant).unwrap();
    let cls = py.get_type::<MyEnum>();
    pyo3::py_run!(py, x cls, r#"
        assert repr(x) == 'RenamedEnum.UPPERCASE'
        assert x == cls.UPPERCASE
    "#)
})
}

You may not use enums as a base class or let enums inherit from other classes.

#![allow(unused)]
fn main() {
use pyo3::prelude::*;
#[pyclass(subclass)]
enum BadBase{
    Var1,
}
}

#![allow(unused)]
fn main() {
use pyo3::prelude::*;

#[pyclass(subclass)]
struct Base;

#[pyclass(extends=Base)]
enum BadSubclass{
    Var1,
}
}

#[pyclass] enums are currently not interoperable with IntEnum in Python.

Implementation details

The #[pyclass] macros rely on a lot of conditional code generation: each #[pyclass] can optionally have a #[pymethods] block.

To support this flexibility the #[pyclass] macro expands to a blob of boilerplate code which sets up the structure for "dtolnay specialization". This implementation pattern enables the Rust compiler to use #[pymethods] implementations when they are present, and fall back to default (empty) definitions when they are not.

This simple technique works for the case when there is zero or one implementations. To support multiple #[pymethods] for a #[pyclass] (in the multiple-pymethods feature), a registry mechanism provided by the inventory crate is used instead. This collects impls at library load time, but isn't supported on all platforms. See inventory: how it works for more details.

The #[pyclass] macro expands to roughly the code seen below. The PyClassImplCollector is the type used internally by PyO3 for dtolnay specialization:

#![allow(unused)]
fn main() {
#[cfg(not(any(feature = "multiple-pymethods", feature = "pyproto")))] {
use pyo3::prelude::*;
// Note: the implementation differs slightly with the `pyproto` or `multiple-pymethods` features enabled.
struct MyClass {
    #[allow(dead_code)]
    num: i32,
}
unsafe impl pyo3::type_object::PyTypeInfo for MyClass {
    type AsRefTarget = pyo3::PyCell<Self>;
    const NAME: &'static str = "MyClass";
    const MODULE: ::std::option::Option<&'static str> = ::std::option::Option::None;
    #[inline]
    fn type_object_raw(py: pyo3::Python<'_>) -> *mut pyo3::ffi::PyTypeObject {
        use pyo3::type_object::LazyStaticType;
        static TYPE_OBJECT: LazyStaticType = LazyStaticType::new();
        TYPE_OBJECT.get_or_init::<Self>(py)
    }
}

impl pyo3::PyClass for MyClass {
    type Frozen = pyo3::pyclass::boolean_struct::False;
}

impl<'a, 'py> pyo3::impl_::extract_argument::PyFunctionArgument<'a, 'py> for &'a MyClass
{
    type Holder = ::std::option::Option<pyo3::PyRef<'py, MyClass>>;

    #[inline]
    fn extract(obj: &'py pyo3::PyAny, holder: &'a mut Self::Holder) -> pyo3::PyResult<Self> {
        pyo3::impl_::extract_argument::extract_pyclass_ref(obj, holder)
    }
}

impl<'a, 'py> pyo3::impl_::extract_argument::PyFunctionArgument<'a, 'py> for &'a mut MyClass
{
    type Holder = ::std::option::Option<pyo3::PyRefMut<'py, MyClass>>;

    #[inline]
    fn extract(obj: &'py pyo3::PyAny, holder: &'a mut Self::Holder) -> pyo3::PyResult<Self> {
        pyo3::impl_::extract_argument::extract_pyclass_ref_mut(obj, holder)
    }
}

impl pyo3::IntoPy<PyObject> for MyClass {
    fn into_py(self, py: pyo3::Python<'_>) -> pyo3::PyObject {
        pyo3::IntoPy::into_py(pyo3::Py::new(py, self).unwrap(), py)
    }
}

impl pyo3::impl_::pyclass::PyClassImpl for MyClass {
    const DOC: &'static str = "Class for demonstration\u{0}";
    const IS_BASETYPE: bool = false;
    const IS_SUBCLASS: bool = false;
    type Layout = PyCell<MyClass>;
    type BaseType = PyAny;
    type ThreadChecker = pyo3::impl_::pyclass::ThreadCheckerStub<MyClass>;
    type PyClassMutability = <<pyo3::PyAny as pyo3::impl_::pyclass::PyClassBaseType>::PyClassMutability as pyo3::impl_::pycell::PyClassMutability>::MutableChild;
    type Dict = pyo3::impl_::pyclass::PyClassDummySlot;
    type WeakRef = pyo3::impl_::pyclass::PyClassDummySlot;
    type BaseNativeType = pyo3::PyAny;

    fn items_iter() -> pyo3::impl_::pyclass::PyClassItemsIter {
        use pyo3::impl_::pyclass::*;
        let collector = PyClassImplCollector::<MyClass>::new();
        static INTRINSIC_ITEMS: PyClassItems = PyClassItems { slots: &[], methods: &[] };
        PyClassItemsIter::new(&INTRINSIC_ITEMS, collector.py_methods())
    }
}

Python::with_gil(|py| {
    let cls = py.get_type::<MyClass>();
    pyo3::py_run!(py, cls, "assert cls.__name__ == 'MyClass'")
});
}
}