user: Help me to optimize this code:
doe = DistanceOnEarth()
def get_mdr_distance(row):
    route_geom = shapely.wkt.loads(row.provider_route)
    route_points = route_geom.coords[:]
    mdr_points = []
    mdr_distance = 0
    for points in split_list(route_points):
        point_1, point_2 = points
        line = shapely.geometry.LineString(points)
        is_mdr = (
            requests.post(
                MDR_URL,
                data=line.wkt,
                headers={"accept": "*/*", "Content-Type": "text/plain"},
            ).content
            == b"true"
        )
        if is_mdr:
            mdr_points.extend(points)
        else:
            if len(mdr_points) > 0:
                mdr_points.extend([point_1])
                mdr_distance += doe.get_4326_geometry_length_in_m(shapely.LineString(mdr_points))
                mdr_points = []
    if len(mdr_points) > 0:
        mdr_distance += doe.get_4326_geometry_length_in_m(shapely.LineString(mdr_points))
    return mdr_distance

assistant: Here are some tips to optimize your Python code:
- Use built-in functions and libraries like map() which are implemented in C code. So the interpreter doesn’t have to execute them in Python.
- Use keys for sorts. In Python, we should use the key argument to the built-in sort instead, which is a faster way to sort.
- Optimize loops. Loops can be optimized by using list comprehensions or generators instead of for loops.
- Apply the Peephole Optimization Technique. This technique optimizes code behind the scenes by pre-calculating constant expressions or by using membership tests.
- Profile your code. Profiling helps you identify which parts of your code are taking up the most time and resources.
- Intern strings for efficiency. String objects in Python are sequences of Unicode characters, so they are called sequences of characters. Interning strings can help reduce memory usage and improve performance.

In addition to these tips, you can also use Cython or Numba to speed up your code if you have performance-critical sections.

user: Thanks. Why Cython is much faster then Python?

assistant: Cython is often faster than Python because it compiles the Python-like code to native C or C++ code, which can then be compiled to a platform-specific binary. This process enables several optimizations that lead to better performance compared to interpreted Python code. Here are some reasons why Cython is typically faster:

1. **Static typing**: Cython allows you to declare variables with static types, unlike Python, which is dynamically typed. With static typing, the compiler can optimize memory usage and access patterns, resulting in faster execution.
2. **Native code generation**: Cython compiles Python-like code to C or C++ code, which is then compiled to a binary executable. This allows the code to run at the speed of native C or C++ code, bypassing the overhead of the Python interpreter.
3. **Optimized function calls**: Function call overhead is reduced in Cython, as it can call C functions directly instead of going through the Python function call mechanism.
4. **Loop optimizations**: Cython can optimize loops more efficiently, particularly when static typing is used, which reduces the overhead associated with looping in interpreted Python code.
5. **GIL bypass**: In some cases, Cython allows you to release the Global Interpreter Lock (GIL) when performing computations in parallel, enabling better performance for multi-threaded applications. This is particularly helpful when working with CPU-bound tasks.

Remember to use Cython when you need performance improvements in your Python code, particularly in computationally intensive tasks, such as numerical computations or data processing.