Speed up your native Python code by over 50x! And it takes just 4 simple steps. Python’s default interpreter (CPython) is slow primarily because of its dynamicity. For instance, after defining a variable of a specific type, it can be changed to some other type. But these dynamic manipulations come at the cost of run-time and memory overheads. The Cython module converts your Python code into C. Steps to use the Cython module (refer to the image as you read): 1) Load the Cython module: %load_ext Cython 2) Add the Cython magic command 3) When using functions, specify the parameter data type 4) Define every variable using the “cdef” keyword and specify its data type. This code will run at native machine speed. P.S. The code shown below is just shown for explaining the usage of Cython. You can easily implement the same using NumPy.

Starting Python 3.14, another way to speed up Python code is by disabling GIL. Earlier, despite writing multi-threaded code, Python could only run one thread at a time. But now, Python can run it in a multi-threaded fashion. 👉 What are some other ways to speed up Python code?