Type translations#

In order to communicate between Python and JavaScript, we “translate” objects between the two languages. Depending on the type of the object we either translate the object by implicitly converting it or by proxying it. By “converting” an object we mean producing a new object in the target language which is the equivalent of the object from the source language, for example converting a Python string to the equivalent a JavaScript string. By “proxying” an object we mean producing a special object in the target language that forwards requests to the source language. When we proxy a JavaScript object into Python, the result is a JsProxy object. When we proxy a Python object into JavaScript, the result is a PyProxy object. A proxied object can be explicitly converted using the explicit conversion methods JsProxy.to_py() and PyProxy.toJs().

Python to JavaScript translations occur:

JavaScript to Python translations occur:

  • when importing from the js module

  • when passing arguments to a Python function called from JavaScript

  • when returning the result of a JavaScript function called from Python

  • when accessing an attribute of a JsProxy

Memory Leaks and Python to JavaScript translations

Any time a Python to JavaScript translation occurs, it may create a PyProxy. To avoid memory leaks, you must store the PyProxy and destroy() it when you are done with it. See Calling Python objects from JavaScript for more info.

Round trip conversions#

Translating an object from Python to JavaScript and then back to Python is guaranteed to give an object that is equal to the original object. Furthermore, if the object is proxied into JavaScript, then translation back unwraps the proxy, and the result of the round trip conversion is the original object (in the sense that they live at the same memory address).

Translating an object from JavaScript to Python and then back to JavaScript gives an object that is === to the original object. Furthermore, if the object is proxied into Python, then translation back unwraps the proxy, and the result of the round trip conversion is the original object (in the sense that they live at the same memory address). There are a few exceptions:

  1. NaN is converted to NaN after a round trip but NaN !== NaN,

  2. null is converted to undefined after a round trip, and

  3. a BigInt will be converted to a Number after a round trip unless its absolute value is greater than Number.MAX_SAFE_INTEGER (i.e., 2^53).

Implicit conversions#

We implicitly convert immutable types but not mutable types. This ensures that mutable Python objects can be modified from JavaScript and vice-versa. Python has immutable types such as tuple and bytes that have no equivalent in JavaScript. In order to ensure that round trip translations yield an object of the same type as the original object, we proxy tuple and bytes objects.

Python to JavaScript#

The following immutable types are implicitly converted from Python to JavaScript:




Number or BigInt*









* An int is converted to a Number if the absolute value is less than or equal to Number.MAX_SAFE_INTEGER otherwise it is converted to a BigInt. (If the browser does not support BigInt then a Number will be used instead. In this case, conversion of large integers from Python to JavaScript is lossy.)

JavaScript to Python#

The following immutable types are implicitly converted from JavaScript to Python:




int or float as appropriate*











* A Number is converted to an int if the absolute value is less than or equal to Number.MAX_SAFE_INTEGER and its fractional part is zero. Otherwise, it is converted to a float.


Any of the types not listed above are shared between languages using proxies that allow methods and some operations to be called on the object from the other language.

Proxying from JavaScript into Python#

When most JavaScript objects are translated into Python a JsProxy is returned. The following operations are currently supported on a JsProxy:









proxy.foo = bar

x.foo = bar

del proxy.foo

delete x.foo

hasattr(proxy, "foo")

"foo" in x






new X(...)


x.length or x.size

foo in proxy

x.has(foo) or x.includes(foo)



proxy[foo] = bar

x.set(foo, bar)

del proxy[foo]


proxy1 == proxy2

x === y


typeof x





await proxy

await x

Note that each of these operations is only supported if the proxied JavaScript object supports the corresponding operation. See the JsProxy API docs for the rest of the methods supported on JsProxy. Some other code snippets:

for v in proxy:
    # do something

is equivalent to:

for (let v of x) {
  // do something

The dir() method has been overloaded to return all keys on the prototype chain of x, so dir(x) roughly translates to:

function dir(x) {
  let result = [];
  do {
  } while ((x = Object.getPrototypeOf(x)));
  return result;

As a special case, JavaScript Array, HTMLCollection, and NodeList are container types, but instead of using array.get(7) to get the 7th element, JavaScript uses array[7]. For these cases, we translate:





proxy[idx] = val

array[idx] = val

idx in proxy

idx in array

del proxy[idx]


If you need to access the fields in a JavaScript object, you must use obj.field_name or if the name of the field is not a valid Python identifier, getattr(obj, "field name"). If you want to access the fields of the object like obj["field name"] you can use as_object_map():

from pyodide.code import run_js

obj = run_js(
      a: 7,
      b: 9,
      $c: 11
obj_map = obj.as_object_map()
assert obj_map["$c"] == 11

Another special case comes from the fact that Python reserved words cannot be used as attributes. For instance, Array.from() and Promise.finally() cannot be directly accessed because they are Python SyntaxErrors. Instead we access these attributes with Array.from_ and Promise.finally_. Similarly, to access from Python, o.from_ you have to use o.from__ with two underscores (since a single underscore is used for o.from). This is reflected in the dir of a JsProxy:

from pyodide.code import run_js
o = run_js("({finally: 1, return: 2, from: 3, from_: 4})")
assert set(dir(o)) == {"finally_", "return_", "from_", "from__"}

Proxying from Python into JavaScript#

When most Python objects are translated to JavaScript a PyProxy is produced.

Fewer operations can be overloaded in JavaScript than in Python, so some operations are more cumbersome on a PyProxy than on a JsProxy. The following operations are supported:





foo in proxy

hasattr(x, 'foo')



proxy.foo = bar

x.foo = bar

delete proxy.foo

del x.foo










foo in x



proxy.set(foo, bar)

x[foo] = bar


del x[foo]







await proxy

await x

Memory Leaks and PyProxy

Make sure to destroy PyProxies when you are done with them to avoid memory leaks.

let foo = pyodide.globals.get('foo');
foo(); // throws Error: Object has already been destroyed

Explicit Conversion of Proxies#

Python to JavaScript#

Explicit conversion of a PyProxy into a native JavaScript object is done with the toJs() method. You can also perform such a conversion in Python using to_js() which behaves in much the same way. By default, the toJs() method does a recursive “deep” conversion, to do a shallow conversion use proxy.toJs({depth : 1}). In addition to the normal type conversion, the toJs() method performs the following explicit conversions:



list, tuple






a buffer*


* Examples of buffers include bytes objects and numpy Array objects.

If you need to convert dict instead to Object, you can pass Object.fromEntries() as the dict_converter argument: proxy.toJs({dict_converter : Object.fromEntries}).

In JavaScript, Map and Set keys are compared using object identity unless the key is an immutable type (meaning a String, a Number, a BigInt, a Boolean, undefined, or null). On the other hand, in Python, dict and set keys are compared using deep equality. If a key is encountered in a dict or set that would have different semantics in JavaScript than in Python, then a ConversionError will be thrown.

See Using Python Buffer objects from JavaScript for the behavior of toJs() on buffers.

Memory Leaks and toJs

The toJs() method can create many proxies at arbitrary depth. It is your responsibility to manually destroy() these proxies if you wish to avoid memory leaks. The pyproxies argument to toJs() is designed to help with this:

let pyproxies = [];
// Do stuff
// pyproxies contains the list of proxies created by `toJs`. We can destroy them
// when we are done with them
for(let px of pyproxies){

As an alternative, if you wish to assert that the object should be fully converted and no proxies should be created, you can use proxy.toJs({create_proxies : false}). If a proxy would be created, a ConversionError is raised instead.

JavaScript to Python#

Explicit conversion of a JsProxy into a native Python object is done with the JsProxy.to_py() method. By default, the to_py() method does a recursive “deep” conversion, to do a shallow conversion use proxy.to_py(depth=1). The to_py() method performs the following explicit conversions:











* to_py() will only convert an Object into a dictionary if its constructor is Object, otherwise the object will be left alone. Example:

class Test {};
window.x = { "a" : 7, "b" : 2};
window.y = { "a" : 7, "b" : 2};
Object.setPrototypeOf(y, Test.prototype);
    from js import x, y
    # x is converted to a dictionary
    assert x.to_py() == { "a" : 7, "b" : 2}
    # y is not a "Plain Old JavaScript Object", it's an instance of type Test so it's not converted
    assert y.to_py() == y

In JavaScript, Map and Set keys are compared using object identity unless the key is an immutable type (meaning a String, a Number, a BigInt, a Boolean, undefined, or null). On the other hand, in Python, dict and set keys are compared using deep equality. If a key is encountered in a Map or Set that would have different semantics in Python than in JavaScript, then a ConversionError will be thrown. Also, in JavaScript, true !== 1 and false !== 0, but in Python, True == 1 and False == 0. This has the result that a JavaScript map can use true and 1 as distinct keys but a Python dict cannot. If the JavaScript map contains both true and 1 a ConversionError will be thrown.


Calling Python objects from JavaScript#

If a Python object is callable, the proxy will be callable too. The arguments will be translated from JavaScript to Python as appropriate, and the return value will be translated from JavaScript back to Python. If the return value is a PyProxy, you must explicitly destroy it or else it will be leaked.

An example:

let test = pyodide.runPython(`
    def test(x):
        return [n*n for n in x]
let result_py = test([1,2,3,4]);
// result_py is a PyProxy of a list.
let result_js = result_py.toJs();
// result_js is the array [1, 4, 9, 16]

If a function is intended to be used from JavaScript, you can use to_js() on the return value. This prevents the return value from leaking without requiring the JavaScript code to explicitly destroy it. This is particularly important for callbacks.

let test = pyodide.runPython(`
    from pyodide.ffi import to_js
    def test(x):
        return to_js([n*n for n in x])
let result = test([1,2,3,4]);
// result is the array [1, 4, 9, 16], nothing needs to be destroyed.

If you need to use a key word argument, use callKwargs(). The last argument should be a JavaScript object with the key value arguments.

let test = pyodide.runPython(`
    from pyodide.ffi import to_js
    def test(x, *, offset):
        return to_js([n*n + offset for n in x])
let result = test.callKwargs([1,2,3,4], { offset : 7});
// result is the array [8, 12, 16, 23]

Calling JavaScript functions from Python#

What happens when calling a JavaScript function from Python is a bit more complicated than calling a Python function from JavaScript. If there are any keyword arguments, they are combined into a JavaScript object and used as the final argument. Thus, if you call:

f(a=2, b=3)

then the JavaScript function receives one argument which is a JavaScript object {a : 2, b : 3}.

When a JavaScript function is called, if the return value not a Promise, a Generator, or an AsyncGenerator, any arguments that are PyProxies that were created in the process of argument conversion are also destroyed. If the result is a PyProxy it is also destroyed.

As a result of this, if a PyProxy is persisted to be used later, then it must either be copied using copy() in JavaScript, or it must be created with create_proxy() or create_once_callable(). If it’s only going to be called once use create_once_callable():

from pyodide.ffi import create_once_callable
from js import setTimeout
def my_callback():
setTimeout(create_once_callable(my_callback), 1000)

If it’s going to be called many times use create_proxy():

from pyodide.ffi import create_proxy
from js import document
def my_callback():
proxy = create_proxy(my_callback)
document.body.addEventListener("click", proxy)
# ...
# make sure to hold on to proxy
document.body.removeEventListener("click", proxy)

When a JavaScript function returns a Promise (for example, if the function is an async function), it is assumed that the Promise is going to do some work that uses the arguments of the function, so it is not safe to destroy them until the Promise resolves. In this case, the proxied function returns a Python Future instead of the original Promise. When the Promise resolves, the result is converted to Python and the converted value is used to resolve the Future. Then if the result is a PyProxy it is destroyed. Any PyProxies created in converting the arguments are also destroyed at this point.

Similarly, if the return value is a Generator or AsyncGenerator, then the arguments (and all values sent to the generator) are kept alive until it is exhausted, or until close() is called.


Using JavaScript Typed Arrays from Python#

JavaScript ArrayBuffer and TypedArray objects are proxied into Python. Python can’t directly access arrays if they are outside the WASM heap, so it’s impossible to directly use these proxied buffers as Python buffers. You can convert such a proxy to a Python memoryview using the to_py() api. This makes it easy to correctly convert the array to a Numpy array using numpy.asarray():

self.jsarray = new Float32Array([1,2,3, 4, 5, 6]);
    from js import jsarray
    array = jsarray.to_py()
    import numpy as np
    numpy_array = np.asarray(array).reshape((2,3))

After manipulating numpy_array you can assign the value back to jsarray using assign():

    numpy_array[1,1] = 77
console.log(jsarray); // [1, 2, 3, 4, 77, 6]

The assign() and assign_to() methods can be used to assign a JavaScript buffer from / to a Python buffer which is appropriately sized and contiguous. The assignment methods will only work if the data types match, the total length of the buffers match, and the Python buffer is contiguous.

Using Python Buffer objects from JavaScript#

Python objects supporting the Python Buffer protocol are proxied into JavaScript. The data inside the buffer can be accessed via the toJs() method or the getBuffer() method. The toJs() API copies the buffer into JavaScript, whereas the getBuffer() method allows low level access to the WASM memory backing the buffer. The getBuffer() API is more powerful but requires care to use correctly. For simple use cases the toJs() API should be preferred.

If the buffer is zero or one-dimensional, then toJs() will in most cases convert it to a single TypedArray. However, in the case that the format of the buffer is 's', we will convert the buffer to a string and if the format is '?' we will convert it to an Array of booleans.

If the dimension is greater than one, we will convert it to a nested JavaScript array, with the innermost dimension handled in the same way we would handle a 1d array.

An example of a case where you would not want to use the toJs() method is when the buffer is bitmapped image data. If for instance you have a 3d buffer shaped 1920 x 1080 x 4, then toJs() will be extremely slow. In this case you could use getBuffer(). On the other hand, if you have a 3d buffer shaped 1920 x 4 x 1080, the performance of toJs() will most likely be satisfactory. Typically, the innermost dimension won’t matter for performance.

The getBuffer() method can be used to retrieve a reference to a JavaScript typed array that points to the data backing the Python object, combined with other metadata about the buffer format. The metadata is suitable for use with a JavaScript ndarray library if one is present. For instance, if you load the JavaScript ndarray package, you can do:

let proxy = pyodide.globals.get("some_numpy_ndarray");
let buffer = proxy.getBuffer();
try {
  if (buffer.readonly) {
    // We can't stop you from changing a readonly buffer, but it can cause undefined behavior.
    throw new Error("Uh-oh, we were planning to change the buffer");
  let array = new ndarray(
  // manipulate array here
  // changes will be reflected in the Python ndarray!
} finally {
  buffer.release(); // Release the memory when we're done


All entrypoints and exit points from Python code are wrapped in JavaScript try blocks. At the boundary between Python and JavaScript, errors are caught, converted between languages, and rethrown.

JavaScript errors are wrapped in a JsException. Python exceptions are converted to a PythonError. At present if an exception crosses between Python and JavaScript several times, the resulting error message won’t be as useful as one might hope.

In order to reduce memory leaks, the PythonError has a formatted traceback, but no reference to the original Python exception. The original exception has references to the stack frame and leaking it will leak all the local variables from that stack frame. The actual Python exception will be stored in sys.last_value so if you need access to it (for instance to produce a traceback with certain functions filtered out), use that.

Be careful Proxying Stack Frames

If you make a PyProxy of sys.last_value, you should be especially careful to destroy() it when you are done with it, or you may leak a large amount of memory if you don’t.

The easiest way is to only handle the exception in Python:

def reformat_exception():
    from traceback import format_exception
    # Format a modified exception here
    # this just prints it normally but you could for instance filter some frames
    return "".join(
        format_exception(sys.last_type, sys.last_value, sys.last_traceback)
let reformat_exception = pyodide.globals.get("reformat_exception");
try {
} catch(e){
    // replace error message
    e.message = reformat_exception();
    throw e;

Importing Objects#

It is possible to access objects in one language from the global scope in the other language. It is also possible to create custom namespaces and access objects on the custom namespaces.

Importing Python objects into JavaScript#

A Python global variable in the __main__ global scope can be imported into JavaScript using the pyodide.globals.get() method. Given the name of the Python global variable, it returns the value of the variable translated to JavaScript.

let x = pyodide.globals.get("x");

As always, if the result is a PyProxy and you care about not leaking the Python object, you must destroy it when you are done. It’s also possible to set values in the Python global scope with pyodide.globals.set() or remove them with pyodide.globals.delete():

pyodide.globals.set("x", 2);
pyodide.runPython("print(x)"); // Prints 2

If you execute code with a custom globals dictionary, you can use a similar approach:

let my_py_namespace = pyodide.globals.get("dict")();
pyodide.runPython("x=2", my_py_namespace);
let x = my_py_namespace.get("x");

To access a Python module from JavaScript, use pyimport():

let sys = pyodide.pyimport("sys");

Importing JavaScript objects into Python#

JavaScript objects in the globalThis global scope can be imported into Python using the js module.

When importing a name from the js module, the js module looks up JavaScript attributes of the globalThis scope and translates the JavaScript objects into Python.

import js
js.document.title = 'New window title'
from js.document.location import reload as reload_page

You can also assign to JavaScript global variables in this way:

pyodide.runPython("js.x = 2");
console.log(window.x); // 2

You can create your own custom JavaScript modules using pyodide.registerJsModule() and they will behave like the js module except with a custom scope:

let my_js_namespace = { x : 3 };
pyodide.registerJsModule("my_js_namespace", my_js_namespace);
    from my_js_namespace import x
    print(x) # 3
    my_js_namespace.y = 7
console.log(my_js_namespace.y); // 7

If the JavaScript object’s name is a reserved Python keyword, the setattr() function can be used to access the object by name within the js module::

lambda = (x) => {return x + 1};
//'from js import lambda' will cause a Syntax Error, since 'lambda' is a Python reserved keyword. Instead:
    import js
    js_lambda = getattr(js, 'lambda')

If a JavaScript object has a property that is a reserved Python keyword, the setattr() and getattr() function can be used to access that property by name:

people = {global: "lots and lots"};
//Trying to access 'people.global' will raise a Syntax Error, since 'global' is a Python reserved keyword. Instead:
    from js import people
    setattr(people, 'global', 'even more')
    print(getattr(people, 'global'))