fast-python-code

Top Five Tips to Increase the Performance of Python Code

While Python is among the most well-known programming languages, it’s not without flaws.

The most important? It’s probably the speed of CPython is a program that converts Python code into bytecode which is then run in an interpreter.

Does it seem easy to comprehend? Yeah, I think!!! Speed up? Nope.

Here are five tips to speed up the speed of your Python code by five times faster than expected.

Table of Contents

Be Pythonic: Go with Python

fast-python-code

When you’re using a programming language that’s not your own, it’s simple to write code against the norm. If you are using for loops or arrays, your code will run slow. The Pythonic method of working utilizes functions like lists comprehensions, maps, and generators. Don’t forget to use features like range and sum.

Make use of memoization

It seems to be more complicated than it is. When the price of performing a call is very high and the function is expensive, adding “memorization” means that the results are stored in the cache for subsequent calls. Calls with identical parameters are taken from the cache, instead of computing again. This could result in substantial speeds increases.

The functions package comes with an LRU cache function that can be used to embellish the function you wish to recall. The following illustration fib is an essential non-memorized Fibonacci function. Fib (35) does a variety of changes; mfib, on the other hand, is the memorized version.

Go with C

fast-python-code

It isn’t always simple to achieve. It is necessary to be proficient in C and its interactions with Python. In addition, there might be a handful of instances where programming in C is sufficient. In addition, the fact CPython has been written in C can help.

The ctypes library includes a set of C types as well as the Python mappings. The library also permits users to connect to the operating system libraries; however, you need to be proficient working at a basic level and be familiar with C such as arrays, structs, and pointers before attempting.

Python machine compilation

The code you create when you build code will always perform faster than interpreted bytecode. Python compilers are readily available in Numpa Nuitka, PyPI, and Cython. Make sure you optimize your Python code before you attempt the compilation. Nampa’s compiler supports JIT (Just-In-Time) as well as it supports GPU acceleration.

If possible, implement “From”

It’s way too easy to depend on the import package all the time. It makes more sense to choose from when you can import the function you need (s). Why should you import 20 functions when you need only one?

Keep in mind that you can put imports within functions, so they only get called when necessary.

Conclusion

If you’re only using one method, you can use memoization to get the maximum performance from Python. However, you’ll likely become an improved Python developer if you were to master the Python approach. It’s interesting to look at plans to accelerate CPython by 5x over the following four versions.

Another approach to increase the speed of Python is to remove this GIL in multithreaded programs (Global Interpreter Lock). It is a mutex that permits only one thread to manage Python’s Python interpreter. Python objects and variables cannot be accessed by writing multithreaded software that does not use the Gil.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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