Calling Python in Rust code

This chapter of the guide documents some ways to interact with Python code from Rust:

How to call Python functions
How to execute existing Python code

Calling Python functions

Any Python-native object reference (such as &PyAny, &PyList, or &PyCell<MyClass>) can be used to call Python functions.

PyO3 offers two APIs to make function calls:

call - call any callable Python object.
call_method - call a method on the Python object.

Both of these APIs take args and kwargs arguments (for positional and keyword arguments respectively). There are variants for less complex calls:

call1 and call_method1 to call only with positional args.
call0 and call_method0 to call with no arguments.

For convenience the Py<T> smart pointer also exposes these same six API methods, but needs a Python token as an additional first argument to prove the GIL is held.

The example below calls a Python function behind a PyObject (aka Py<PyAny>) reference:

use pyo3::prelude::*;
use pyo3::types::PyTuple;

fn main() -> PyResult<()> {
    let arg1 = "arg1";
    let arg2 = "arg2";
    let arg3 = "arg3";

    Python::with_gil(|py| {
        let fun: Py<PyAny> = PyModule::from_code(
            py,
            "def example(*args, **kwargs):
                if args != ():
                    print('called with args', args)
                if kwargs != {}:
                    print('called with kwargs', kwargs)
                if args == () and kwargs == {}:
                    print('called with no arguments')",
            "",
            "",
        )?
        .getattr("example")?
        .into();

        // call object without any arguments
        fun.call0(py)?;

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

        // pass arguments as rust tuple
        let args = (arg1, arg2, arg3);
        fun.call1(py, args)?;
        Ok(())
    })
}

For the call and call_method APIs, 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;
use std::collections::HashMap;

fn main() -> PyResult<()> {
    let key1 = "key1";
    let val1 = 1;
    let key2 = "key2";
    let val2 = 2;

    Python::with_gil(|py| {
        let fun: Py<PyAny> = PyModule::from_code(
            py,
            "def example(*args, **kwargs):
                if args != ():
                    print('called with args', args)
                if kwargs != {}:
                    print('called with kwargs', kwargs)
                if args == () and kwargs == {}:
                    print('called with no arguments')",
            "",
            "",
        )?
        .getattr("example")?
        .into();

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

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

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

        Ok(())
    })
}

Executing existing Python code

If you already have some existing Python code that you need to execute from Rust, the following FAQs can help you select the right PyO3 functionality for your situation:

Want to access Python APIs? Then use `PyModule::import`.

Pymodule::import can be used to get handle to a Python module from Rust. You can use this to import and use any Python module available in your environment.

use pyo3::prelude::*;

fn main() -> PyResult<()> {
    Python::with_gil(|py| {
        let builtins = PyModule::import(py, "builtins")?;
        let total: i32 = builtins
            .getattr("sum")?
            .call1((vec![1, 2, 3],))?
            .extract()?;
        assert_eq!(total, 6);
        Ok(())
    })
}

Want to run just an expression? Then use `eval`.

Python::eval is a method to execute a Python expression and return the evaluated value as a &PyAny object.

use pyo3::prelude::*;

fn main() -> Result<(), ()> {
Python::with_gil(|py| {
    let result = py
        .eval("[i * 10 for i in range(5)]", None, None)
        .map_err(|e| {
            e.print_and_set_sys_last_vars(py);
        })?;
    let res: Vec<i64> = result.extract().unwrap();
    assert_eq!(res, vec![0, 10, 20, 30, 40]);
    Ok(())
})
}

Want to run statements? Then use `run`.

Python::run is a method to execute one or more Python statements. This method returns nothing (like any Python statement), but you can get access to manipulated objects via the locals dict.

You can also use the py_run! macro, which is a shorthand for Python::run. Since py_run! panics on exceptions, we recommend you use this macro only for quickly testing your Python extensions.

use pyo3::prelude::*;
use pyo3::{PyCell, py_run};

fn main() {
#[pyclass]
struct UserData {
    id: u32,
    name: String,
}

#[pymethods]
impl UserData {
    fn as_tuple(&self) -> (u32, String) {
        (self.id, self.name.clone())
    }

    fn __repr__(&self) -> PyResult<String> {
        Ok(format!("User {}(id: {})", self.name, self.id))
    }
}

Python::with_gil(|py| {
    let userdata = UserData {
        id: 34,
        name: "Yu".to_string(),
    };
    let userdata = PyCell::new(py, userdata).unwrap();
    let userdata_as_tuple = (34, "Yu");
    py_run!(py, userdata userdata_as_tuple, r#"
assert repr(userdata) == "User Yu(id: 34)"
assert userdata.as_tuple() == userdata_as_tuple
    "#);
})
}

You have a Python file or code snippet? Then use `PyModule::from_code`.

PyModule::from_code can be used to generate a Python module which can then be used just as if it was imported with PyModule::import.

Warning: This will compile and execute code. Never pass untrusted code to this function!

use pyo3::{prelude::*, types::{IntoPyDict, PyModule}};

fn main() -> PyResult<()> {
Python::with_gil(|py| {
    let activators = PyModule::from_code(py, r#"
def relu(x):
    """see https://en.wikipedia.org/wiki/Rectifier_(neural_networks)"""
    return max(0.0, x)

def leaky_relu(x, slope=0.01):
    return x if x >= 0 else x * slope
    "#, "activators.py", "activators")?;

    let relu_result: f64 = activators.getattr("relu")?.call1((-1.0,))?.extract()?;
    assert_eq!(relu_result, 0.0);

    let kwargs = [("slope", 0.2)].into_py_dict(py);
    let lrelu_result: f64 = activators
        .getattr("leaky_relu")?
        .call((-1.0,), Some(kwargs))?
        .extract()?;
    assert_eq!(lrelu_result, -0.2);
   Ok(())
})
}

Want to embed Python in Rust with additional modules?

Python maintains the sys.modules dict as a cache of all imported modules. An import in Python will first attempt to lookup the module from this dict, and if not present will use various strategies to attempt to locate and load the module.

The append_to_inittab macro can be used to add additional #[pymodule] modules to an embedded Python interpreter. The macro must be invoked before initializing Python.

As an example, the below adds the module foo to the embedded interpreter:

use pyo3::prelude::*;

#[pyfunction]
fn add_one(x: i64) -> i64 {
    x + 1
}

#[pymodule]
fn foo(_py: Python<'_>, foo_module: &PyModule) -> PyResult<()> {
    foo_module.add_function(wrap_pyfunction!(add_one, foo_module)?)?;
    Ok(())
}

fn main() -> PyResult<()> {
    pyo3::append_to_inittab!(foo);
    Python::with_gil(|py| Python::run(py, "import foo; foo.add_one(6)", None, None))
}

If append_to_inittab cannot be used due to constraints in the program, an alternative is to create a module using PyModule::new and insert it manually into sys.modules:

use pyo3::prelude::*;
use pyo3::types::PyDict;

#[pyfunction]
pub fn add_one(x: i64) -> i64 {
    x + 1
}

fn main() -> PyResult<()> {
    Python::with_gil(|py| {
        // Create new module
        let foo_module = PyModule::new(py, "foo")?;
        foo_module.add_function(wrap_pyfunction!(add_one, foo_module)?)?;

        // Import and get sys.modules
        let sys = PyModule::import(py, "sys")?;
        let py_modules: &PyDict = sys.getattr("modules")?.downcast()?;

        // Insert foo into sys.modules
        py_modules.set_item("foo", foo_module)?;

        // Now we can import + run our python code
        Python::run(py, "import foo; foo.add_one(6)", None, None)
    })
}

Include multiple Python files

You can include a file at compile time by using std::include_str macro.

Or you can load a file at runtime by using std::fs::read_to_string function.

Many Python files can be included and loaded as modules. If one file depends on another you must preserve correct order while declaring PyModule.

Example directory structure:

.
├── Cargo.lock
├── Cargo.toml
├── python_app
│   ├── app.py
│   └── utils
│       └── foo.py
└── src
    └── main.rs

python_app/app.py:

from utils.foo import bar


def run():
    return bar()

python_app/utils/foo.py:

def bar():
    return "baz"

The example below shows:

how to include content of app.py and utils/foo.py into your rust binary
how to call function run() (declared in app.py) that needs function imported from utils/foo.py

src/main.rs:

use pyo3::prelude::*;

fn main() -> PyResult<()> {
    let py_foo = include_str!(concat!(env!("CARGO_MANIFEST_DIR"), "/python_app/utils/foo.py"));
    let py_app = include_str!(concat!(env!("CARGO_MANIFEST_DIR"), "/python_app/app.py"));
    let from_python = Python::with_gil(|py| -> PyResult<Py<PyAny>> {
        PyModule::from_code(py, py_foo, "utils.foo", "utils.foo")?;
        let app: Py<PyAny> = PyModule::from_code(py, py_app, "", "")?
            .getattr("run")?
            .into();
        app.call0(py)
    });

    println!("py: {}", from_python?);
    Ok(())
}