Efficient Python Math

Python is notorious for being a slow programming language. When looping through problems in most fashions in Python, it can be noticeably slower than languages like C.

There are ways to reduce the amount of time it takes to do larger calculations, no matter how simple.

NumPy vs Python

A great tool to use is NumPy. Being familiar with NumPy and it’s benefits can significantly speed up your code. NumPy is written primarily in C which results in it’s execution time beating Python’s significantly. Below you can see an example of using NumPy Arrays vs Native Python Lists.

This yields the following results

As we can see, NumPy arrays are significantly faster! NumPy Arrays beat Python’s built-in lists by 2.6 seconds! While 2.6 seconds may not seem like a huge difference, in programming it is a HUGE gap for an operation this small.

Let’s look at another example of how we can make Python math more efficient.

Python Caching

If you are a new Python developer, or developer in general, you may not have heard of caches or caching before. Your computers have what is reffered to as a cache, which is a storage for temporary files. Typically this cache is stored somewhere in your computers memory.

Python makes it very easy to cache answers to a function. This can drastically reduce the amount of time functions take to complete. In this example we will use a fibonacci sequence.

Let’s view the code without any caching.

This unsuprisingy yields the following result:

Using the module functools to enable function caching we can drastically reduce this time.

All we have to do is import lru_cache from functools, then add the decorator above the fib function, with a maxsize of None. Remember to always read the documentation when using a new module, maxsize of None may not be the best idea for your setup. Let’s see what this code looks like:

This yield a MUCH quicker result of:

In Python, there are always a lot of roads to your destination. As we all further our Python skills it is important to learn efficient ways to design our programs. Your future developer self will thank you!