# Summary

This workshop has introduced you to the basics of using either numba or cython to accelerate your Python script.

Hopefully, you have seen that it is possible to significantly accelerate your scripts, sometimes by thousands of times, by using numba or cython. This saves you time, as well as saving energy!

These tools work best when your scripts consist of primarily loops over numerical data held in arrays.

To optimise, a good strategy is to;

1. Profile your code to find the “slow” parts. You should try to accelerate the slowest parts of your code first.

2. Move as much of your data as possible into arrays, e.g. numpy arrays.

3. Use optimised functions (e.g. those from numpy or scipy) if they are available to do what you need, as it is preferable and more time / energy efficient to use those than trying to write your own code.

4. If you need to write your own code, then write it as loops, which are then either JIT-compiled using numba, or pre-compiled using cython.

5. If iterations of each loop are independent, then experiment with parallelising them using prange. We have a bonus chapter that shows how you can parallelise more complex loops.

6. Profile your code throughout, using different data sizes, so that you can verify that you really are speeding up your code.

# numba or cython?

numba is significantly easier to use and, in my experience, produces code that is slightly faster (and is more reliably going to be faster).

However, numba is limited to the types of Python that it can accelerate, and is best suited only to smaller functions that clearly involve looping over numerical data held in arrays.

cython is very good for larger or more complex code. You can easily mix C and Python together into single .pyx files, and you have a lot more control over how you move things in memory, and how and when you take and release the GIL. This power comes with a lot of complexity though, which is why, as you’ve experienced, using cython is a lot harder and more complex than using numba.

A good rule of thumb is to start optimising using numba, and then only switch to cython if you reach the limits of what numba supports.

# What’s next?

We strongly encourage you to read the complete tutorials and documentation of numba and cython. They are well-written and very detailed.

# Credits

All text is published under a Creative Commons Attribution 4.0 International License with all code snippets licensed as MIT.

The source for the material can be found on GitHub where fixes are welcome.