Getting and reading data

The first step is to retrieve a dataset and open it with a program capable of manipulating the data. The simplest way of retrieving a dataset is to find a data file. Python and R can be used to open, read, modify, and save data stored in static files. In Chapter 3, Reading, Exploring, and Modifying Data - Part I, I will introduce the JSON data format and show how to use Python to read, write and modify JSON data. In Chapter 4, Reading, Exploring, and Modifying Data - Part II, I will walk through how to use Python to work with data files in the CSV and XML data formats. In Chapter 6, Cleaning Numerical Data - An Introduction to R and Rstudio, I will introduce R and Rstudio, and show how to use R to read and manipulate data. 

Larger data sources are often made available through web interfaces called application programming interfaces (APIs). APIs allow you to retrieve specific bits of data from a larger collection of data. Web APIs can be great resources for data that is otherwise hard to get. In Chapter 8, Getting Data from the Web, I discuss APIs in detail and walk through the use of Python to extract data from APIs.

Another possible source of data is a database. I won't go into detail on the use of databases in this book, though in Chapter 9, Working with Large Datasets, I will show how to interact with a particular database using Python.