Data Wrangling with Python

Chapter 3. Data Meant to Be Read by Machines

Data can be stored in many different formats and file types. Some formats store data in a way easily handled by machines, while others store data in a way meant to be easily readable by a human. Microsoft Word documents are an example of the latter, while CSV, JSON, and XML are examples of the former. In this chapter, we will cover how to read files easily handled by machines, and in Chapters 4 and Chapter 5 we will cover files made for human consumption.

Note

File formats that store data in a way easily understood by machines are commonly referred to as machine readable. Common machine-readable formats include the following:

• Comma-Separated Values (CSV)

• JavaScript Object Notation (JSON)

• Extensible Markup Language (XML)

In spoken and written language, these data formats are typically referred to by their shorter names (e.g., CSV). We will be using these acronyms.

When looking for data or requesting data from an organization or agency, the formats described in this chapter are your best available resource. They are more easily used and ingested by your Python scripts than human-readable formats, and are usually easy to find on data websites.

Creating a Home for Your Code

To follow along with the examples and code in this chapter, you will need to save the files locally on your machine. If you haven’t already, you should create a folder where you are going to keep your Python code and data files. Call it something intuitive, ...