To define a custom Python class, a Rust struct needs to be annotated with the #[pyclass] attribute.

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

#[pyclass]
struct MyClass {
   num: i32,
   debug: bool,
}
#}

The above example generates implementations for PyTypeInfo and PyTypeObject for MyClass.

You can use pyclasses like normal rust structs.

However, if instantiated normally, you can't treat pyclasses as Python objects.

To get a Python object which includes pyclass, we have to use some special methods.

PyRef is a special reference, which ensures that the referred struct is a part of a Python object, and you are also holding the GIL.

You can get an instance of PyRef by PyRef::new, which does 3 things:

1. Allocates a Python object in the Python heap
2. Copies the Rust struct into the Python object
3. Returns a reference to it

You can use PyRef just like &T, because it implements Deref<Target=T>.

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
# use pyo3::types::PyDict;
#[pyclass]
struct MyClass {
   num: i32,
   debug: bool,
}
let gil = Python::acquire_gil();
let py = gil.python();
let obj = PyRef::new(py, MyClass { num: 3, debug: true }).unwrap();
assert_eq!(obj.num, 3);
let dict = PyDict::new(py);
// You can treat a `PyRef` as a Python object
dict.set_item("obj", obj).unwrap();
#}

PyRefMut is a mutable version of PyRef.

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
#[pyclass]
struct MyClass {
   num: i32,
   debug: bool,
}
let gil = Python::acquire_gil();
let py = gil.python();
let mut obj = PyRefMut::new(py, MyClass { num: 3, debug: true }).unwrap();
obj.num = 5;
#}

Py is an object wrapper which stores an object longer than the GIL lifetime.

You can use it to avoid lifetime problems.

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
#[pyclass]
struct MyClass {
   num: i32,
}
fn return_myclass() -> Py<MyClass> {
    let gil = Python::acquire_gil();
    let py = gil.python();
    Py::new(py, MyClass { num: 1 }).unwrap()
}
let gil = Python::acquire_gil();
let obj = return_myclass();
assert_eq!(obj.as_ref(gil.python()).num, 1);
#}

The #[pyclass] macro accepts the following parameters:

• name=XXX - Set the class name shown in Python code. By default, the struct name is used as the class name.
• freelist=XXX - The freelist parameter adds support of free allocation list to custom class. The performance improvement applies to types that are often created and deleted in a row, so that they can benefit from a freelist. XXX is a number of items for the free list.
• gc - Classes with the gc parameter participate in Python garbage collection. If a custom class contains references to other Python objects that can be collected, the PyGCProtocol trait has to be implemented.
• weakref - Adds support for Python weak references.
• extends=BaseType - Use a custom base class. The base BaseType must implement PyTypeInfo.
• subclass - Allows Python classes to inherit from this class.
• dict - Adds __dict__ support, so that the instances of this type have a dictionary containing arbitrary instance variables.

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

#[pymethods]
impl MyClass {

     #[new]
     fn new(obj: &PyRawObject, num: i32) {
         obj.init({
             MyClass {
                 num,
             }
         });
     }
}
#}

Rules for the new method:

• If no method marked with #[new] is declared, object instances can only be created from Rust, but not from Python.
• The first parameter is the raw object and the custom new method must initialize the object with an instance of the struct using the init method. The type of the object may be the type object of a derived class declared in Python.
• The first parameter must have type &PyRawObject.
• For details on the parameter list, see the Method arguments section below.
• The return value must be T or PyResult<T> where T is ignored, so it can be just () as in the example above.

By default, PyObject is used as the base class. To override this default, use the extends parameter for pyclass with the full path to the base class. The new method of subclasses must call their parent's new method.

# use pyo3::prelude::*;
# use pyo3::PyRawObject;
#[pyclass]
struct BaseClass {
   val1: usize,
}

#[pymethods]
impl BaseClass {
   #[new]
   fn new(obj: &PyRawObject) {
       obj.init(BaseClass { val1: 10 });
   }

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

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

#[pymethods]
impl SubClass {
   #[new]
   fn new(obj: &PyRawObject) {
       obj.init(SubClass { val2: 10 });
       BaseClass::new(obj);
   }

   fn method2(&self) -> PyResult<()> {
      self.get_base().method()
   }
}

The ObjectProtocol trait provides a get_base() method, which returns a reference to the instance of the base struct.

Property descriptor methods can be defined in a #[pymethods] impl block only and have to be annotated with #[getter] and #[setter] attributes. For example:

# #![allow(unused_variables)]
#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_variables)]
#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_variables)]
#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.

For simple cases where a member variable is just read and written with no side effects, you can also declare getters and setters in your Rust struct field definition, for example:

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

Then it is available from Python code as self.num.

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.

# #![allow(unused_variables)]
#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 IntoPyObject; 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_variables)]
#fn main() {
# use pyo3::prelude::*;
# #[pyclass]
# struct MyClass {
#    num: i32,
#    debug: bool,
# }

#[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.

To create a class method for a custom class, the method needs to be annotated with the #[classmethod] attribute.

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
# use pyo3::types::PyType;
# #[pyclass]
# struct MyClass {
#    num: i32,
#    debug: bool,
# }

#[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 IntoPyObject.

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 IntoPyObject.

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

#[pymethods]
impl MyClass {
     #[staticmethod]
     fn static_method(param1: i32, param2: &str) -> PyResult<i32> {
        Ok(10)
     }
}
#}

To specify a custom __call__ method for a custom class, the method needs to be annotated with the #[call] attribute. Arguments of the method are specified as for instance methods.

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
use pyo3::types::PyTuple;
# #[pyclass]
# struct MyClass {
#    num: i32,
#    debug: bool,
# }

#[pymethods]
impl MyClass {
     #[call]
     #[args(args="*")]
     fn __call__(&self, args: &PyTuple) -> PyResult<i32> {
        println!("MyClass has been called");
        Ok(self.num)
     }
}
#}

By default, PyO3 uses function signatures to determine which arguments are required. Then it scans the incoming args and kwargs parameters. If it can not find all required parameters, it raises a TypeError exception. It is possible to override the default behavior with the #[args(...)] attribute. This attribute accepts a comma separated list of parameters in the form of attr_name="default value". Each parameter has to match the method parameter by name.

Each parameter can be one of the following types:

• "*": var arguments separator, each parameter defined after "*" is a keyword-only parameter. Corresponds to python's def meth(*, arg1.., arg2=..).
• args="*": "args" is var args, corresponds to Python's def meth(*args). Type of the args parameter has to be &PyTuple.
• kwargs="**": "kwargs" receives keyword arguments, corresponds to Python's def meth(**kwargs). The type of the kwargs parameter has to be Option<&PyDict>.
• arg="Value": arguments with default value. Corresponds to Python's def meth(arg=Value). If the arg argument is defined after var arguments, it is treated as a keyword-only argument. Note that Value has to be valid rust code, PyO3 just inserts it into the generated code unmodified.

Example:

# #![allow(unused_variables)]
#fn main() {
# use pyo3::prelude::*;
use pyo3::types::{PyDict, PyTuple};
#
# #[pyclass]
# struct MyClass {
#    num: i32,
#    debug: bool,
# }
#
#[pymethods]
impl MyClass {
    #[args(arg1=true, args="*", arg2=10, kwargs="**")]
    fn method(&self, arg1: bool, args: &PyTuple, arg2: i32, kwargs: Option<&PyDict>) -> PyResult<i32> {
        Ok(1)
    }
}
#}

Python's object model defines several protocols for different object behavior, like sequence, mapping or number protocols. PyO3 defines separate traits for each of them. To provide specific Python object behavior, you need to implement the specific trait for your struct. Important note, each protocol implementation block has to be annotated with the #[pyproto] attribute.

The PyObjectProtocol trait provides several basic customizations.

To customize object attribute access, define the following methods:

• fn __getattr__(&self, name: FromPyObject) -> PyResult<impl IntoPyObject>
• fn __setattr__(&mut self, name: FromPyObject, value: FromPyObject) -> PyResult<()>
• fn __delattr__(&mut self, name: FromPyObject) -> PyResult<()>

Each method corresponds to Python's self.attr, self.attr = value and del self.attr code.

• fn __repr__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>

• fn __str__(&self) -> PyResult<impl ToPyObject<ObjectType=PyString>>

  Possible return types for __str__ and __repr__ are PyResult<String> or PyResult<PyString>.

• fn __bytes__(&self) -> PyResult<PyBytes>

  Provides the conversion to bytes.

• fn __format__(&self, format_spec: &str) -> PyResult<impl ToPyObject<ObjectType=PyString>>

  Special method that is used by the format() builtin and the str.format() method. Possible return types are PyResult<String> or PyResult<PyString>.

• fn __richcmp__(&self, other: impl FromPyObject, op: CompareOp) -> PyResult<impl ToPyObject>

  Overloads Python comparison operations (==, !=, <, <=, >, and >=). The op argument indicates the comparison operation being performed. The return type will normally be PyResult<bool>, but any Python object can be returned. If other is not of the type specified in the signature, the generated code will automatically return NotImplemented.

• fn __hash__(&self) -> PyResult<impl PrimInt>

  Objects that compare equal must have the same hash value. The return type must be PyResult<T> where T is one of Rust's primitive integer types.

• fn __bool__(&self) -> PyResult<bool>

  Determines the "truthyness" of the object.

If your type owns references to other Python objects, you will need to integrate with Python's garbage collector so that the GC is aware of those references. To do this, implement the PyGCProtocol trait for your struct. It includes two methods __traverse__ and __clear__. These correspond to the slots tp_traverse and tp_clear in the Python C API. __traverse__ must call visit.call() for each reference to another Python object. __clear__ must clear out any mutable references to other Python objects (thus breaking reference cycles). Immutable references do not have to be cleared, as every cycle must contain at least one mutable reference. Example:

# #![allow(unused_variables)]
#fn main() {
extern crate pyo3;

use pyo3::prelude::*;
use pyo3::PyTraverseError;
use pyo3::gc::{PyGCProtocol, PyVisit};

#[pyclass]
struct ClassWithGCSupport {
    obj: Option<PyObject>,
}

#[pyproto]
impl PyGCProtocol for ClassWithGCSupport {
    fn __traverse__(&self, visit: PyVisit) -> Result<(), PyTraverseError> {
        if let Some(ref obj) = self.obj {
            visit.call(obj)?
        }
        Ok(())
    }

    fn __clear__(&mut self) {
        if let Some(obj) = self.obj.take() {
            // Release reference, this decrements ref counter.
            let gil = GILGuard::acquire();
            let py = gil.python();
            py.release(obj);
        }
    }
}
#}

Special protocol trait implementations have to be annotated with the #[pyproto] attribute.

It is also possible to enable GC for custom class using the gc parameter of the pyclass attribute. i.e. #[pyclass(gc)]. In that case instances of custom class participate in Python garbage collection, and it is possible to track them with gc module methods.

Iterators can be defined using the PyIterProtocol trait. It includes two methods __iter__ and __next__:

• fn __iter__(slf: PyRefMut<Self>) -> PyResult<impl IntoPyObject>
• fn __next__(slf: PyRefMut<Self>) -> PyResult<Option<impl IntoPyObject>>

Returning Ok(None) from __next__ indicates that that there are no further items.

Example:

# #![allow(unused_variables)]
#fn main() {
extern crate pyo3;

use pyo3::prelude::*;
use pyo3::PyIterProtocol;

#[pyclass]
struct MyIterator {
    iter: Box<Iterator<Item = PyObject> + Send>,
}

#[pyproto]
impl PyIterProtocol for MyIterator {
    fn __iter__(slf: PyRefMut<Self>) -> PyResult<Py<MyIterator>> {
        Ok(slf.into())
    }
    fn __next__(mut slf: PyRefMut<Self>) -> PyResult<Option<PyObject>> {
        Ok(slf.iter.next())
    }
}
#}

TODO: Which traits to implement (basically PyTypeCreate: PyObjectAlloc + PyTypeInfo + PyMethodsProtocol + Sized) and what they mean.

Users should be able to define a #[pyclass] with or without #[pymethods], while PyO3 needs a trait with a function that returns all methods. Since it's impossible to make the code generation in pyclass dependent on whether there is an impl block, we'd need to implement the trait on #[pyclass] and override the implementation in #[pymethods], which is to the best of my knowledge only possible with the specialization feature, which can't be used on stable.

To escape this we use inventory, which allows us to collect impls from arbitrary source code by exploiting some binary trick. See inventory: how it works and pyo3_derive_backend::py_class::impl_inventory for more details.