Type Conversions

In this portion of the guide we'll talk about the mapping of Python types to Rust types offered by PyO3, as well as the traits available to perform conversions between them.

Mapping of Rust types to Python types

When writing functions callable from Python (such as a #[pyfunction] or in a #[pymethods] block), the trait FromPyObject is required for function arguments, and IntoPy<PyObject> is required for function return values.

Consult the tables in the following section to find the Rust types provided by PyO3 which implement these traits.

Argument Types

When accepting a function argument, it is possible to either use Rust library types or PyO3's Python-native types. (See the next section for discussion on when to use each.)

The table below contains the Python type and the corresponding function argument types that will accept them:

PythonRustRust (Python-native)
strString, Cow<str>, &str&PyUnicode
bytesVec<u8>, &[u8]&PyBytes
intAny integer type (i32, u32, usize, etc)&PyLong
floatf32, f64&PyFloat
dict[K, V]HashMap<K, V>, BTreeMap<K, V>&PyDict
tuple[T, U](T, U), Vec<T>&PyTuple
set[T]HashSet<T>, BTreeSet<T>&PySet
frozenset[T]HashSet<T>, BTreeSet<T>&PyFrozenSet

There are also a few special types related to the GIL and Rust-defined #[pyclass]es which may come in useful:

PythonA GIL token, used to pass to PyO3 constructors to prove ownership of the GIL
PyObjectA Python object isolated from the GIL lifetime. This can be sent to other threads. To call Python APIs using this object, it must be used with AsPyRef::as_ref to get a &PyAny reference.
Py<T>Same as above, for a specific Python type or #[pyclass] T.
&PyCell<T>A #[pyclass] value owned by Python.
PyRef<T>A #[pyclass] borrowed immutably.
PyRefMut<T>A #[pyclass] borrowed mutably.

For more detail on accepting #[pyclass] values as function arguments, see the section of this guide on Python Classes.

Using Rust library types vs Python-native types

Using Rust library types as function arguments will incur a conversion cost compared to using the Python-native types. Using the Python-native types is almost zero-cost (they just require a type check similar to the Python builtin function isinstance()).

However, once that conversion cost has been paid, the Rust standard library types offer a number of benefits:

  • You can write functionality in native-speed Rust code (free of Python's runtime costs).
  • You get better interoperability with the rest of the Rust ecosystem.
  • You can use Python::allow_threads to release the Python GIL and let other Python threads make progress while your Rust code is executing.
  • You also benefit from stricter type checking. For example you can specify Vec<i32>, which will only accept a Python list containing integers. The Python-native equivalent, &PyList, would accept a Python list containing Python objects of any type.

For most PyO3 usage the conversion cost is worth paying to get these benefits. As always, if you're not sure it's worth it in your case, benchmark it!

Returning Rust values to Python

When returning values from functions callable from Python, Python-native types (&PyAny, &PyDict etc.) can be used with zero cost.

Because these types are references, in some situations the Rust compiler may ask for lifetime annotations. If this is the case, you should use Py<PyAny>, Py<PyDict> etc. instead - which are also zero-cost. For all of these Python-native types T, Py<T> can be created from T with an .into() conversion.

If your function is fallible, it should return PyResult<T>, which will raise a Python exception if the Err variant is returned.

Finally, the following Rust types are also able to convert to Python as return values:

Rust typeResulting Python Type
Any integer type (i32, u32, usize, etc)int
f32, f64float
(T, U)Tuple[T, U]
HashMap<K, V>Dict[K, V]
BTreeMap<K, V>Dict[K, V]
&PyCell<T: PyClass>T
PyRef<T: PyClass>T
PyRefMut<T: PyClass>T


PyO3 provides some handy traits to convert between Python types and Rust types.

.extract() and the FromPyObject trait

The easiest way to convert a Python object to a Rust value is using .extract(). It returns a PyResult with a type error if the conversion fails, so usually you will use something like

let v: Vec<i32> = obj.extract()?;

This method is available for many Python object types, and can produce a wide variety of Rust types, which you can check out in the implementor list of FromPyObject.

FromPyObject is also implemented for your own Rust types wrapped as Python objects (see the chapter about classes). There, in order to both be able to operate on mutable references and satisfy Rust's rules of non-aliasing mutable references, you have to extract the PyO3 reference wrappers PyRef and PyRefMut. They work like the reference wrappers of std::cell::RefCell and ensure (at runtime) that Rust borrows are allowed.

The ToPyObject trait

ToPyObject is a conversion trait that allows various objects to be converted into PyObject. IntoPy<PyObject> serves the same purpose, except that it consumes self.

*args and **kwargs for Python object calls

There are several ways how to pass positional and keyword arguments to a Python object call. PyAny provides two methods:

  • call - call any callable Python object.
  • call_method - call a specific method on the object, shorthand for get_attr then call.

Both methods need args and kwargs arguments, but there are variants for less complex calls, such as call1 for only args and call0 for no arguments at all.

use pyo3::prelude::*;
use pyo3::types::{PyDict, PyTuple};

struct SomeObject;
impl SomeObject {
    fn new(py: Python) -> PyObject {

fn main() {
    let arg1 = "arg1";
    let arg2 = "arg2";
    let arg3 = "arg3";

    let gil = Python::acquire_gil();
    let py = gil.python();

    let obj = SomeObject::new(py);

    // call object without empty arguments

    // call object with PyTuple
    let args = PyTuple::new(py, &[arg1, arg2, arg3]);
    obj.call1(py, args);

    // pass arguments as rust tuple
    let args = (arg1, arg2, arg3);
    obj.call1(py, args);

kwargs can be None or Some(&PyDict). You can use the IntoPyDict trait to convert other dict-like containers, e.g. HashMap or BTreeMap, as well as tuples with up to 10 elements and Vecs where each element is a two-element tuple.

use pyo3::prelude::*;
use pyo3::types::{IntoPyDict, PyDict};
use std::collections::HashMap;

struct SomeObject;

impl SomeObject {
    fn new(py: Python) -> PyObject {

fn main() {
    let key1 = "key1";
    let val1 = 1;
    let key2 = "key2";
    let val2 = 2;

    let gil = Python::acquire_gil();
    let py = gil.python();

    let obj = SomeObject::new(py);

    // call object with PyDict
    let kwargs = [(key1, val1)].into_py_dict(py);
    obj.call(py, (), Some(kwargs));

    // pass arguments as Vec
    let kwargs = vec![(key1, val1), (key2, val2)];
    obj.call(py, (), Some(kwargs.into_py_dict(py)));

    // pass arguments as HashMap
    let mut kwargs = HashMap::<&str, i32>::new();
    kwargs.insert(key1, 1);
    obj.call(py, (), Some(kwargs.into_py_dict(py)));

FromPy<T> and IntoPy<T>

Many conversions in PyO3 can't use std::convert::From because they need a GIL token. The FromPy trait offers an from_py method that works just like from, except for taking a Python<'_> argument. I.e. FromPy<T> could be converting a Rust object into a Python object even though it is called FromPy - it doesn't say anything about which side of the conversion is a Python object.

Just like From<T>, if you implement FromPy<T> you gain a blanket implementation of IntoPy for free.

Eventually, traits such as ToPyObject will be replaced by this trait and a FromPy trait will be added that will implement IntoPy, just like with From and Into.


Requires the num-complex optional feature.