Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas

Scientific distributions of Python (Anaconda, WinPython, Canopy, and so on) provide analysts with an impressive range of data manipulation, exploration, and visualization tools. One important tool is pandas. Developed by Wes McKinney in 2008, but really gaining in popularity after 2012, pandas is now an essential library for data analysis in Python. We work with pandas extensively in this book, along with popular packages such as numpy, matplotlib, and scipy.

A key pandas object is the data frame, which represents data as a tabular structure, with rows and columns. In this way, it is similar to the other data stores we discuss in this chapter. However, a pandas data frame also has indexing functionality that makes selecting, combining, and transforming data relatively straightforward, as the recipes in this book will demonstrate.

Before we can make use of this great functionality, we have to...