Pros of CPython

• Mature and stable implementation with extensive ecosystem support
• Highly optimized C code for performance-critical parts
• Comprehensive standard library and built-in modules

Cons of CPython

• Global Interpreter Lock (GIL) limits true multi-threading
• Memory management can be less efficient compared to Rust
• Slower execution speed for certain tasks compared to compiled languages

Code Comparison

CPython (C implementation):

static PyObject *
string_count(PyStringObject *self, PyObject *args)
{
    Py_ssize_t count;
    Py_ssize_t start = 0;
    Py_ssize_t end = PY_SSIZE_T_MAX;
    PyObject *sub_obj;
    const char *str, *sub;
    Py_ssize_t len, sub_len;

    if (!PyArg_ParseTuple(args, "O|nn:count", &sub_obj, &start, &end))
        return NULL;

RustPython (Rust implementation):

fn count(
    zelf: PyObjectRef,
    args: FuncArgs,
    vm: &VirtualMachine,
) -> PyResult<PyObjectRef> {
    let (sub, start, end) = match args.args.len() {
        1 => (
            args.bind(vm, ("sub",))?,
            0,
            zelf.str(vm)?.len(),
        ),
        2 => {
            let (sub, start): (PyObjectRef, isize) = args.bind(vm, ("sub", "start"))?;
            (sub, start, zelf.str(vm)?.len())
        },

Pros of MicroPython

• Mature project with extensive hardware support
• Optimized for microcontrollers and embedded systems
• Smaller memory footprint and faster startup time

Cons of MicroPython

• Limited subset of Python standard library
• Performance can be slower for complex operations
• Less suitable for general-purpose Python development

Code Comparison

MicroPython:

import machine
import time

led = machine.Pin(2, machine.Pin.OUT)
while True:
    led.toggle()
    time.sleep_ms(500)

RustPython:

import time

while True:
    print("Hello, RustPython!")
    time.sleep(1)

MicroPython's code example demonstrates its focus on hardware interaction, while RustPython's example shows a more general-purpose Python implementation. MicroPython provides low-level hardware access, whereas RustPython aims for broader Python compatibility.

RustPython offers the advantage of Rust's performance and safety features, potentially leading to better execution speed for certain operations. It also aims to be more compatible with CPython, making it suitable for a wider range of Python applications.

However, RustPython is a newer project with less maturity and may have fewer libraries specifically optimized for embedded systems compared to MicroPython.

Pros of IronPython3

• Seamless integration with .NET ecosystem and libraries
• Better performance for Windows-specific applications
• More mature and stable implementation

Cons of IronPython3

• Limited cross-platform support compared to RustPython
• Slower development pace and less active community
• Dependency on .NET framework

Code Comparison

IronPython3:

import clr
clr.AddReference("System.Windows.Forms")
from System.Windows.Forms import MessageBox
MessageBox.Show("Hello from IronPython!")

RustPython:

import sys
print(f"Hello from RustPython! Running on {sys.platform}")

The IronPython3 example demonstrates its tight integration with .NET, allowing direct use of Windows Forms. RustPython, being more focused on cross-platform compatibility, showcases a simple print statement that works across different operating systems.

Both projects aim to implement Python in different languages (C# for IronPython3 and Rust for RustPython) with distinct goals. IronPython3 excels in .NET integration, while RustPython offers better cross-platform support and leverages Rust's performance and safety features.

Pros of Jython

• Mature project with longer history and established ecosystem
• Seamless integration with Java libraries and frameworks
• Better performance for certain Java-heavy workloads

Cons of Jython

• Limited support for newer Python features and libraries
• Slower development pace compared to RustPython
• Larger runtime size due to Java dependency

Code Comparison

Jython:

from java.util import ArrayList

list = ArrayList()
list.add("Hello")
list.add("World")
print(list)

RustPython:

my_list = ["Hello", "World"]
print(my_list)

The Jython example demonstrates its ability to directly use Java classes, while RustPython uses standard Python syntax. This highlights Jython's strength in Java interoperability, but also shows that RustPython provides a more familiar Python experience.

RustPython aims to be more compatible with CPython and focuses on performance and memory safety through Rust. It's actively developed and supports more recent Python features. However, it lacks Jython's mature Java integration capabilities.

Both projects have their merits, with Jython excelling in Java environments and RustPython offering a promising future for Python implementation in Rust.

Pros of PyPy

• Mature project with extensive compatibility and optimization
• Just-in-time (JIT) compiler for significant performance improvements
• Large community and widespread adoption in production environments

Cons of PyPy

• Limited support for some C extensions and libraries
• Slower startup time compared to CPython
• Memory usage can be higher, especially for short-running scripts

Code Comparison

PyPy:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

RustPython:

fn factorial(n: i32) -> i32 {
    if n == 0 {
        1
    } else {
        n * factorial(n - 1)
    }
}

While the Python code remains the same for both implementations, RustPython allows for optional type annotations in Rust-style syntax, potentially offering better performance and type safety.

PyPy focuses on optimizing Python code execution through its JIT compiler, while RustPython aims to implement Python in Rust, potentially offering better memory safety and easier integration with Rust ecosystems. PyPy is more mature and widely used, while RustPython is a newer project with growing potential for specific use cases and environments.

Pros of Batavia

• Written in JavaScript, allowing for easier web integration and broader developer accessibility
• Part of the BeeWare suite, offering a more comprehensive ecosystem for cross-platform development
• Focuses on running Python bytecode in the browser, potentially enabling more seamless web applications

Cons of Batavia

• Limited to running Python in web browsers, whereas RustPython aims for broader system-level integration
• May have performance limitations compared to RustPython's Rust implementation
• Less active development and smaller community compared to RustPython

Code Comparison

Batavia (JavaScript):

var vm = new batavia.VirtualMachine();
vm.run('print("Hello, Batavia!")');

RustPython (Rust):

use rustpython_vm as vm;

vm::Interpreter::without_stdlib(Default::default()).enter(|vm| {
    vm.run_code_obj(vm.compile("print('Hello, RustPython!')", "<embedded>", vm::compiler::Mode::Exec).unwrap(), vm.new_scope_with_builtins()).unwrap();
});

Both projects aim to implement Python in different languages, but their approaches and use cases differ. Batavia focuses on running Python in web browsers, while RustPython provides a more comprehensive Python implementation in Rust, potentially offering better performance and system-level integration.