Can you explain to me, why python is generally regarded as slower than C++?
Python is generally regarded as slower than C++ for several reasons:

Interpretation vs. Compilation: Python is an interpreted language, which means that Python code is executed line-by-line during runtime. On the other hand, C++ is a compiled language, where the entire code is translated into machine code before execution. Compiled languages tend to have faster execution times as the translation step allows for various performance optimizations.

Dynamic Typing: Python is a dynamically-typed language, which means that variable types are determined at runtime. This flexibility comes at the cost of performance, as the interpreter has to perform additional checks and lookups to determine the type of an object during runtime. C++ is a statically-typed language, where variable types are defined during compilation, allowing the compiler to optimize memory usage and access.

Memory Management: Python uses a garbage collector to manage memory, which can lead to performance overhead as it periodically checks for objects that are no longer in use and frees their memory. C++ allows for manual memory management, which gives programmers more control over memory allocation and deallocation, leading to more efficient memory usage and potentially faster execution.

Abstraction: Python is designed to be easy-to-read and user-friendly, providing a high level of abstraction that simplifies programming tasks. This abstraction often comes at the cost of performance, as certain low-level optimizations are not available in Python. C++ offers lower-level access to memory and hardware, which enables programmers to implement performance-critical optimizations.

Global Interpreter Lock (GIL): In CPython, the reference implementation of Python, the Global Interpreter Lock (GIL) prevents multiple native threads from executing Python bytecodes simultaneously. This can limit the performance benefits of multi-threading in Python, while C++ can take full advantage of multi-threading and parallel processing for better performance.

It is important to note that while Python may be slower than C++ in certain cases, it is often preferred for its simplicity, readability, and extensive libraries. In many applications, the ease of development and maintainability offered by Python outweighs the performance gains provided by C++.
Python is high level language, made for people who don't know about hardware and don't have any coding skills that can program and make applications, in the other hand C++ is language closer to the CPU, and it is made for computer experts and those who understand hardware as well.  Most important is that Python is actually written in C++, and that is the main reason that it can never be faster than C++.
While it is true that Python is a high-level language designed for ease-of-use and readability, and that C++ is a lower-level language designed for more direct interaction with hardware, these factors alone do not determine the performance differences between the two languages.

It is also true that Python is implemented in C and C++, and that this implementation can impact performance. However, the performance of Python code is not solely determined by the underlying implementation. Rather, it is a result of various factors, including the efficiency of the language constructs and libraries used, the optimization techniques employed by the interpreter or compiler, and the hardware and operating system being used.

In practice, the performance of Python can be significantly improved through the use of various optimization techniques, such as just-in-time (JIT) compilation, caching, and parallel processing. Additionally, Python's extensive libraries and frameworks can provide pre-optimized solutions for many common tasks, making it a popular choice for data analysis, machine learning, and scientific computing.

While it is true that C++ can provide higher performance than Python in certain cases, such as low-level systems programming or real-time applications, Python's ease-of-use and extensive libraries make it a valuable tool for many other types of applications. Ultimately, the choice between Python and C++ should be based on the specific requirements of the project and the skillset of the development team.