Machine Learning Workflows in Python from Scratch Part 1: Data Preparation

It seems that, anymore, the perception of machine learning is often reduced to passing a series of arguments to a growing number of libraries and APIs, hoping for magic, and awaiting the results. Maybe you have a very good idea of what's going on under the hood in these libraries -- from data preparation to model building to results interpretation and visualization and beyond -- but you are still relying on these various tools to get the job done. Using well-tested and proven implementations of tools for performing regular tasks makes sense for a whole host of reasons. Reinventing wheels which don't roll efficiently is not best practice... it's limiting, and it takes an unnecessarily long time. Whether you are using open source or proprietary tools to get your work done, these implementations have been honed by teams of individuals ensuring that you get your hands on the best quality instruments with which to accomplish your goals.