6 Costly Numpy Mistakes to Avoid in Python
Numpy is one of the most central libraries to Python but we all make simple mistakes, or we even make those mistakes that we know we shouldn’t make but still haven’t really figured out how to broach the subject. I’m a pretty average programmer and even now, I still fumble around with many problems that I face in Python . So with that in mind, I decided to write down what common problems we face and how to go about them. It’s embarrassing to have lived through these: but here we go! 1: Lists or Numpy Arrays? When I st a rted programming , I couldn’t figure out the difference and only when I began to use matrix methods did I really appreciate the difference here. Simply, a List is an ordered set of elements whereas, an array is a grid of values and it contains information about the raw data, how to locate an element , and how to interpret an element . Lists are declared between by a pair of square brackets [‘this is a list’] whereas a Numpy Array is defin