Reshaping and Tidying Data
When analyzing and transforming data, it’s not always the case that the data is in the best shape possible for your purpose. Your data may not be as clean or organized as you hoped. It may not even have the necessary column attributes for your analysis. This is where the concept of reshaping and tidying data comes into play. Reshaping means transforming data so that it suits a particular analysis you’re trying to conduct. Tidying data refers to the process of organizing and structuring data in a clean and consistent way so that it is easy to work with and analyze. The process of reshaping and tidying data involves operations such as pivoting, unpivoting, stacking, and joining.
In this chapter, we’re going to cover the following main topics:
- Turning columns into rows
- Turning rows into columns
- Joining DataFrames
- Concatenating DataFrames
- Other techniques for reshaping data
