5 Recommendations For Speeding Up Python Code

Python is one of the world’s most widely used programming languages, yet it has certain drawbacks. Which is most important? It’s a lack of speed. Python code is transformed into bytecode by CPython and then carried out by an interpreter.

The bytecode is cached in an a.pyc file until your source code is modified; the initial run takes a little longer since CPython translates it to bytecode. For measuring speed, the second and subsequent runs are preferable.

Here are five techniques for five-times-quicker Python code:

  1. Be Pythonesque

Writing code that deviates from the convention is relatively straightforward when coming from another programming language. Your code will run more slowly if you employ loops or arrays. Python uses tools like maps, list comprehensions, and generators to accomplish tasks. Remember useful functions like sum and range, too.

  1. Utilize memorization

it seems more complicated than it is. Adding “memorization” to a function when the cost of calling it is high means the results are cached; subsequent calls with the same argument values draw from the cache rather than recalculating the results. It may cause noticeable speed gains.

You can decorate the function you want to remember using the lru cache function in the func tools package. Fib(35) performs numerous additions in the example below, where fib is a straightforward non-memorized Fibonacci function, and a file is the memoized version.

  1. Compose in C

It can be complicated to do this. You must be familiar with how Python works with C. Additionally, there can be just a few instances when C programming is sufficient. It helps that CPython was created in C.

A collection of C types and their Python mappings can be found in the ctypes library. You can also call into the operating system libraries using this library. Still, before using it, you should feel comfortable working at a low level and be familiar with C, including arrays, structs, and pointers.

  1. compilation on the Python machine

Always run faster than interpreted bytecode is the code created when you compile code. Several Python compilers are available, including Cython, Nuitka, Pypi, and Numpad. Before attempting to compile your Python code, optimize it. The JIT (Just-In-Time) compiler used by Numpa enables GPU acceleration.

  1. Deploy ‘From’ whenever possible

Relying just on import packages is too simple. However, using from when you can import the necessary function makes more sense (s). When only one function is needed, why would you import twenty?

Remember that imports can be contained within functions, so they are only called when required.


To get the maximum speed out of Python, employ memoization if you only use one technique. However, learning the pythonic approach will help you become a better Python programmer. Plans to accelerate CPython by up to 5 times over the following four releases are intriguing.

Removing the GIL from multithreaded programs is another suggested strategy for accelerating Python (Global Interpreter Lock). Only one thread can control the Python interpreter, thanks to this mutex. Python variables and objects cannot be accessed if you write multithreaded code that does not use the Gil.

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