Data Wrangling with Python

Chapter 4. Working with Excel Files

Unlike the previous chapter’s data, not all the data in this and the following chapter will easily import into Python without a little work. This is because some data formats were made to be machine readable, while others, such as the ones we’ll look at next, were meant to be interacted with through desktop tools. In this chapter and the next, we will look at two example file types—Excel files and PDFs—and provide some generic instructions to follow if you encounter a different file type.

So far in this book, the solutions you’ve learned about for importing data have been pretty standard. In this chapter, we will begin to learn about processes which will vary greatly each time you perform them. Although the processes are more difficult, the end goal is the same: to extract the useful information and put it into a usable format in Python.

The examples we use in this chapter and the next contain data from UNICEF’s 2014 report on The State of the World’s Children. The data is available in PDF and Excel format.

When you have to extract data from files in these more difficult formats, you might think there is someone out there who hates you, because it can be painful. We assure you in most cases, the person who generated the file with the data inside simply did not identify the importance of also releasing it in a machine-readable format.

Installing Python Packages

Before we can continue, we need to learn how to install external Python packages ...