Data Preparation is one of those critical tasks that most digital analysts take for granted as many of the analytics platforms we use take care of this task for us or at least we like to believe they do so. With that said, Data Preparation should be a task that every good analyst completes as part of any data investigation.
Wes McKinney, author of Python for Data Analysis, defines Data Preparation as “cleaning, munging, combining, normalizing, reshaping, slicing, dicing, and transforming data for analysis.”
In this post, I am going to walk you through a real world example, focusing on Data Preparation, of how Python can be a very powerful tool for business focused data analysis.