Why you should use NumPy vs FOR loops in Python

Key Takeaways

you've probably seen numpy floating around a bunch of data science circles but have you ever wondered why it's so useful let me show you let's say for example we wanted to calculate the sum of the combinations of two different lists we could use nested for loops and add these together now if we time it we can see that this has taken 463 micro seconds to run what happens if we introduce numpy we can achieve the same mathematical function by using the np sum and np mesh grid function and in this case boom 63.6 microseconds a significant improvement and just a fraction of what it took when using for loops numpy is so ridiculously fast because it uses densely packed arrays of the same data types significantly improving and speeding up computation it also benefits from having some of the operations implemented in c which avoids some of the pitfalls of python including the cost of for loops pointer in direction and dynamic type checking definitely add numpy to your study list