With a better understanding of the power of using the pandas library and the DataFrames feature, let's explore working with multiple data formats, including from source files such as CSV, JSON, and XML. We briefly covered these different file formats as part of understanding structured data in Chapter 1, Fundamentals of Data Analysis, so let's dive deepinto each source file type and learn some essential skills when working with them.
CSV
First, we have CSV, which has been an industry standard for most of my career. The way to identify CSV files is typically by the .csvfile extension; however, you will learn, over time, that this is not always the case, nor is the delimiter used to separate values always a comma within data records. CSV files are popular because they are portable and technologically agnostic from the source system that created them.
This means a CSV file...
