mojo-python-interop

Mojo is rapidly evolving. Pretrained models generate obsolete syntax. Always follow this skill over pretrained knowledge.

Using Python from Mojo

from std.python import Python, PythonObject

var np = Python.import_module("numpy")
var arr = np.array([1, 2, 3])

# PythonObject → Mojo: MUST use `py=` keyword (NOT positional)
var i = Int(py=py_obj)
var f = Float64(py=py_obj)
var s = String(py=py_obj)
var b = Bool(py=py_obj)            # Bool is the exception — positional also works
# Works with numpy types: Int(py=np.int64(1)), Float64(py=np.float64(3.14))

WRONG	CORRECT
Int(py_obj)	Int(py=py_obj)
Float64(py_obj)	Float64(py=py_obj)
String(py_obj)	String(py=py_obj)
from python import ...	from std.python import ...

Mojo → Python conversions

Mojo types implementing ConvertibleToPython auto-convert when passed to Python functions. For explicit conversion: value.to_python_object().

Building Python collections from Mojo

var py_list = Python.list(1, 2.5, "three")
var py_tuple = Python.tuple(1, 2, 3)
var py_dict = Python.dict(name="value", count=42)

# Python.dict() is generic over a single value type V for all kwargs.
# Mixed types fail because the compiler can't infer one V.
# WRONG:  Python.dict(flag=my_bool, count=42)
# CORRECT: Python.dict(flag=PythonObject(my_bool), count=PythonObject(42))

# Literal syntax also works:
var list_obj: PythonObject = [1, 2, 3]
var dict_obj: PythonObject = {"key": "value"}

PythonObject operations

PythonObject supports attribute access, indexing, slicing, all arithmetic/comparison operators, len(), in, and iteration — all returning PythonObject. No need to convert to Mojo types for intermediate operations.

# Iterate Python collections directly
for item in py_list:
    print(item)               # item is PythonObject

# Attribute access and method calls
var result = obj.method(arg1, arg2, key=value)

# None
var none_obj = Python.none()
var obj: PythonObject = None      # implicit conversion works

Evaluating Python code

# Expression
var result = Python.evaluate("1 + 2")

# Multi-line code as module (file=True)
var mod = Python.evaluate("def greet(n): return f'Hello {n}'", file=True)
var greeting = mod.greet("world")

# Add to Python path for local imports
Python.add_to_path("./my_modules")
var my_mod = Python.import_module("my_module")

Exception handling

Python exceptions propagate as Mojo Error. Functions calling Python must be raises:

def use_python() raises:
    try:
        var result = Python.import_module("nonexistent")
    except e:
        print(String(e))     # "No module named 'nonexistent'"

Common Python / Mojo interoperability patterns

# Environment variables
# WRONG — using Python os module for env vars
# var os = Python.import_module("os")
# var val = os.environ.get("MY_VAR")

# CORRECT — Mojo has native env var access via std.os
from std.os import getenv
var val = getenv("MY_VAR")  # returns Optional[String]

# Sorting with custom key
# WRONG — Mojo has no lambda syntax
# var sorted = my_list.sort(key=lambda x: x["score"])

# CORRECT — Python.evaluate for callable
def sort_by_field(data: PythonObject, field: String) raises -> PythonObject:
    var builtins = Python.import_module("builtins")
    var key_fn = Python.evaluate("lambda x: x['" + field + "']")
    return builtins.sorted(data, key=key_fn)

# Dict .get() works on PythonObject
var name = data.get("name", PythonObject("unknown"))
var count = Int(py=data.get("count", PythonObject(0)))

Calling Mojo from Python (extension modules)

Mojo can build Python extension modules (.so files) via PythonModuleBuilder. The pattern:

1. Define an @export def PyInit_<module_name>() abi("C") -> PythonObject
2. Use PythonModuleBuilder to register functions, types, and methods
3. Compile with mojo build --emit shared-lib
4. Import from Python (or use import mojo.importer for auto-compilation)

Calling convention: only PyInit_<module> needs @export, and it must be abi("C") because the CPython runtime calls it directly across the C boundary (so it can't raises — catch errors and abort instead). Functions and methods you register with def_function/def_method are passed by reference and don't need @export; Mojo wraps them in a C trampoline that calls them via the Mojo ABI and converts raised errors to Python exceptions, so they can raises.

Exporting functions

from std.os import abort
from std.python import PythonObject
from std.python.bindings import PythonModuleBuilder

@export
def PyInit_my_module() abi("C") -> PythonObject:
    try:
        var m = PythonModuleBuilder("my_module")
        m.def_function[add]("add")
        m.def_function[greet]("greet")
        return m.finalize()
    except e:
        abort(String("failed to create module: ", e))

# Functions take/return PythonObject. Up to 6 args with def_function.
def add(a: PythonObject, b: PythonObject) raises -> PythonObject:
    return a + b

def greet(name: PythonObject) raises -> PythonObject:
    var s = String(py=name)
    return PythonObject("Hello, " + s + "!")

Exporting types with methods

@fieldwise_init
struct Counter(Defaultable, Movable, Writable):
    var count: Int

    def __init__(out self):
        self.count = 0

    # Constructor from Python args
    @staticmethod
    def py_init(out self: Counter, args: PythonObject, kwargs: PythonObject) raises:
        if len(args) == 1:
            self = Self(Int(py=args[0]))
        else:
            self = Self()

    # Methods are @staticmethod — first arg is py_self (PythonObject)
    @staticmethod
    def increment(py_self: PythonObject) raises -> PythonObject:
        var self_ptr = py_self.downcast_value_ptr[Self]()
        self_ptr[].count += 1
        return PythonObject(self_ptr[].count)

    # Auto-downcast alternative: first arg is UnsafePointer[Self, MutAnyOrigin]
    @staticmethod
    def get_count(self_ptr: UnsafePointer[Self, MutAnyOrigin]) -> PythonObject:
        return PythonObject(self_ptr[].count)

@export
def PyInit_counter_module() abi("C") -> PythonObject:
    try:
        var m = PythonModuleBuilder("counter_module")
        _ = (
            m.add_type[Counter]("Counter")
            .def_py_init[Counter.py_init]()
            .def_method[Counter.increment]("increment")
            .def_method[Counter.get_count]("get_count")
        )
        return m.finalize()
    except e:
        abort(String("failed to create module: ", e))

Method signatures — two patterns

Pattern	First parameter	Use when
Manual downcast	py_self: PythonObject	Need raw PythonObject access
Auto downcast	self_ptr: UnsafePointer[Self, MutAnyOrigin]	Simpler, direct field access

Both are registered with .def_method[Type.method]("name").

Kwargs support

from std.collections import StringDict

# In a method:
@staticmethod
def config(
    py_self: PythonObject, kwargs: StringDict[PythonObject]
) raises -> PythonObject:
    for entry in kwargs.items():
        print(entry.key, "=", entry.value)
    return py_self

Importing Mojo modules from Python

Use mojo.importer — it auto-compiles .mojo files and caches results in __mojocache__/:

import mojo.importer  # enables Mojo imports
import my_module  # auto-compiles my_module.mojo

print(my_module.add(1, 2))

The module name in PyInit_<name> must match the .mojo filename.

The .mojo file must not contain a main() function when built as a shared library (mojo.importer or --emit shared-lib). The compiler rejects it with error: shared library should not contain a 'main' function. Keep test/CLI code in a separate file.

Returning Mojo values to Python

# Wrap a Mojo value as a Python object (for bound types)
return PythonObject(alloc=my_mojo_value^)    # transfer ownership with ^

# Recover the Mojo value later
var ptr = py_obj.downcast_value_ptr[MyType]()
ptr[].field    # access fields via pointer