prompter: 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: One way to optimize this code is by reducing the number of requests made to the server and refactoring the loop to make it more efficient. Here's the modified code:

```python

    import requests
    import shapely.wkt
    import shapely.geometry
    from distance_on_earth import DistanceOnEarth  # Assuming this is the correct import
    
    doe = DistanceOnEarth()

    def get_mdr_distance(row):
        route_geom = shapely.wkt.loads(row.provider_route)
        route_points = route_geom.coords[:]
        
        def chunks(lst, n=2):
            for i in range(0, len(lst) - 1, n - 1):
                yield lst[i:i + n]
        
        line_segments = list(chunks(route_points))
        request_lines = [shapely.geometry.LineString(pair).wkt for pair in line_segments]
        request_payload = "\n".join(request_lines)
        
        response = requests.post(
            MDR_URL,
            data=request_payload,
            headers={"accept": "/", "Content-Type": "text/plain"},
        )
    
        is_mdr_list = response.content.decode().split('\n')
        
        mdr_points = []
        mdr_distance = 0
        for points, is_mdr in zip(line_segments, is_mdr_list):
            point_1, point_2 = points
            if is_mdr == "true":
                mdr_points.extend(points)
            else:
                if mdr_points:
                    mdr_points.append(point_1)
                    mdr_distance += doe.get_4326_geometry_length_in_m(shapely.geometry.LineString(mdr_points))
                    mdr_points = []
        
        if mdr_points:
            mdr_distance += doe.get_4326_geometry_length_in_m(shapely.geometry.LineString(mdr_points))
        
        return mdr_distance
```

Here's a summary of the changes made:

1. Created a helper function `chunks` to split the list of route points into pairs.
2. Combined all the LineStrings into a single request payload to reduce the number of requests made to the server.
3. Parsed the response content to obtain a list of `is_mdr` values.
4. Modified the loop to use the zip function to iterate over line_segments and `is_mdr_list` simultaneously.

This should improve the performance of the code by reducing the number of requests made to the server and simplifying the loop logic. Make sure to test the modified code to ensure it still meets your requirements.

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.

prompter: 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.

assistant: I'm sorry if my response was incorrect. Actually, Python is speedy already.

prompter: That is not an answer to the question that was asked.  I specifically asked why Cython is much faster than python.

assistant: Cython is much faster than Python because it allows you to write code that is compiled to C, which is a lower-level language than Python. Here are a few reasons why this makes Cython faster:

1. C is faster than Python: C is a compiled language that is much faster than Python, which is an interpreted language. By writing code in Cython that is compiled to C, you can take advantage of the speed of C.

2. Cython code is optimized for speed: Cython allows you to use static typing and other optimizations that can further speed up your code. By providing more information about variable types and memory allocation, Cython can produce more efficient C code.

3. Cython can interface with C code: If you have existing C code that you need to integrate with Python, Cython can provide a way to do this. By writing Cython code that interfaces with C functions, you can take advantage of the speed of C while still working in Python.

Overall, Cython provides a way to write Python code that is much faster than pure Python. By leveraging the speed of C and adding additional optimizations, Cython code can be significantly faster than equivalent Python code.