While it's always an interesting topic, since I used Python for the first time 10 years ago, it has been tried and tried again to only realize the same thing again and again :
1) the Python language was never made for speed of execution, it was made for speed of programming. It is an awesome language for prototyping, teaching and scripting
2) If you want speed, it likely means you are doing math operations, for example ML. In that case, you'd better learn to use state-of-the-art math libraries, made of decades of hardware and mathematical expert knowledge you will never beat.
It would serve you better to :
- learn about existing state-of-the-art compiled librairies and tools in your problem space
- learn to profile your Python code
Python is a great orchestrator to glue libraries and external systems together. If you are reinventing everything in Python, you are not solving actual problems. It's matter of using the right tool for the right task.
Python is a mean to achieve something greater, it's not an end by itself.
1) the Python language was never made for speed of execution, it was made for speed of programming. It is an awesome language for prototyping, teaching and scripting
2) If you want speed, it likely means you are doing math operations, for example ML. In that case, you'd better learn to use state-of-the-art math libraries, made of decades of hardware and mathematical expert knowledge you will never beat.
It would serve you better to :
- learn about existing state-of-the-art compiled librairies and tools in your problem space
- learn to profile your Python code
Python is a great orchestrator to glue libraries and external systems together. If you are reinventing everything in Python, you are not solving actual problems. It's matter of using the right tool for the right task.
Python is a mean to achieve something greater, it's not an end by itself.