Chapter 7. Data Analysis Application Examples

In this chapter, we want to get you acquainted with typical data preparation tasks and analysis techniques, because being fluent in preparing, grouping, and reshaping data is an important building block for successful data analysis.

While preparing data seems like a mundane task – and often it is – it is a step we cannot skip, although we can strive to simplify it by using tools such as Pandas.

Why is preparation necessary at all? Because most useful data will come from the real world and will have deficiencies, contain errors or will be fragmentary.

There are more reasons why data preparation is useful: it gets you in close contact with the raw material. Knowing your input helps you to spot potential errors early and build confidence in your results.

Here are a few data preparation scenarios:

• A client hands you three files, each containing time series data about a single geological phenomenon, but the observed data is recorded on different intervals and uses different separators
• A machine learning algorithm can only work with numeric data, but your input only contains text labels
• You are handed the raw logs of a web server of an up and coming service and your task is to make suggestions on a growth strategy, based on existing visitor behavior